A Survey on Ambient Intelligence in Healthcare
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paper_title: Ambient intelligence: A survey

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In this article we survey ambient intelligence (AmI), including its applications, some of the technologies it uses, and its social and ethical implications. The applications include AmI at home, care of the elderly, healthcare, commerce, and business, recommender systems, museums and tourist scenarios, and group decision making. Among technologies, we focus on ambient data management and artificial intelligence; for example planning, learning, event-condition-action rules, temporal reasoning, and agent-oriented technologies. The survey is not intended to be exhaustive, but to convey a broad range of applications, technologies, and technical, social, and ethical challenges.
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paper_title: Ambient Intelligence, Wireless Networking, And Ubiquitous Computing

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Networking in Miniature Computers and Embedded Systems. Ubiquitous Computing. Context Transparency and Awareness. Computational Intelligence. Computer Architectures for Ambient Intelligence. 3G and Beyond Services and Architectures for Mobile User Support. Semantic Web, Knowledge Management, and Discovery for Network Services. Middleware and Distributed Computing Issues for Ubiquitous Computing Using Wireless and Mobile Technologies. Polymorphic, Programmable/Active Networks for Mobile and Wireless Environments. Dynamic Data- and Model-Driven Network Architectures and Services. Nomadic User Support and Management. Service Provisioning for Mobile/Wireless Users. Security and Privacy in Mobile and Wireless World. Peer-to-Peer and Grid-Based Services for Mobile Users.
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paper_title: Ambient Intelligence: Technologies, Applications, and Opportunities

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Ambient intelligence is an emerging discipline that brings intelligence to our everyday environments and makes those environments sensitive to us. Ambient intelligence (AmI) research builds upon advances in sensors and sensor networks, pervasive computing, and artificial intelligence. Because these contributing fields have experienced tremendous growth in the last few years, AmI research has strengthened and expanded. Because AmI research is maturing, the resulting technologies promise to revolutionarize daily human life by making people's surroundings flexible and adaptive. In this paper, we provide a survey of the technologies that comprise ambient intelligence and of the applications that are dramatically affected by it. In particular, we specifically focus on the research that makes AmI technologies ''intelligent''. We also highlight challenges and opportunities that AmI researchers will face in the coming years.
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paper_title: Applications of Smartphones for Ubiquitous Health Monitoring and Wellbeing Management

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Advances in smartphone technology and data communications facilitate the use of ubiquitous health monitoring and mobile health application as a solution of choice for the overwhelming problems of the healthcare system. In addition to easier management and seamless access to historical records, ubiquitous technology has the potential to motivate users to take an active role and manage their own conditions. In this paper we present capabilities of the current generation of smartphones and possible applications for ubiquitous health monitoring and wellness management. We describe the architecture and organization of ubiquitous health monitoring systems, Body Sensor Networks, and integration of wearable and environmental sensors. We also describe mainstream mobile health related applications in today's mobile marketplaces such as Apple App Store and Google Android Marketplace. Finally, we present the development of UAHealth - our integrated mobile health monitoring system for wellness management, designed to monitor physical activity, weight, and heart activity.
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paper_title: Pervasive Computing in Health Care: Smart Spaces and Enterprise Information Systems

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Middleware for pervasive computing is an active area of research that is now starting to mature. Prototypical systems have been developed and used for demonstration ap- plications in a number of domains. However, realistic pervasive environments of the future will need to integrate with enterprise systems, including software, hardware, databases, standards, and life cycle. Designing and implementing "enterprise-strength" pervasive applications today is not an easy task, as we illustrate with a simple case study of a healthcare scenario. We use the case study to draw conclusions about requirements placed on future pervasive systems. We present a scenario where context middleware, within a few years, might assist in clinical-care business practices. Patients are instrumented with vital-sign monitors and with a means of determining their location. Physicians and nurses have wireless PDAs, also instrumented with a means of determining their location. Context-aware applications help optimize physician rounds, support nurse triage, simplify the user interface to pervasive devices, provide additional data for billing reconciliation, and provide clinical communications. A physician, Dr. Able, arrives on the second floor of the hospital. A graphic appears on his PDA showing the rooms assigned to his patients. Rooms are highlighted depending on whether the patient is currently in the room, and ambulating patients are notified that the doctor is making rounds. Relatives waiting elsewhere in the hospital are also notified so that they
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paper_title: Development and Validation of a Smartphone Heart Rate Acquisition Application for Health Promotion and Wellness Telehealth Applications

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Objective. Current generation smartphones' video camera technologies enable photoplethysmographic (PPG) acquisition and heart rate (HR) measurement. The study objective was to develop an Android application and compare HRs derived from a Motorola Droid to electrocardiograph (ECG) and Nonin 9560BT pulse oximeter readings during various movement-free tasks. Materials and Methods. HRs were collected simultaneously from 14 subjects, ages 20 to 58, healthy or with clinical conditions, using the 3 devices during 5-minute periods while at rest, reading aloud under observation, and playing a video game. Correlation between the 3 devices was determined, and Bland-Altman plots for all possible pairs of devices across all conditions assessed agreement. Results. Across conditions, all device pairs showed high correlations. Bland-Altman plots further revealed the Droid as a valid measure for HR acquisition. Across all conditions, the Droid compared to ECG, 95% of the data points (differences between devices) fell within the limits of agreement. Conclusion. The Android application provides valid HRs at varying levels of movement free mental/perceptual motor exertion. Lack of electrode patches or wireless sensor telemetric straps make it advantageous for use in mobile-cell-phone-delivered health promotion and wellness programs. Further validation is needed to determine its applicability while engaging in physical movement-related activities.
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paper_title: A Survey on Wireless Body Area Networks

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The increasing use of wireless networks and the constant miniaturization of electrical devices has empowered the development of Wireless Body Area Networks (WBANs). In these networks various sensors are attached on clothing or on the body or even implanted under the skin. The wireless nature of the network and the wide variety of sensors offer numerous new, practical and innovative applications to improve health care and the Quality of Life. The sensors of a WBAN measure for example the heartbeat, the body temperature or record a prolonged electrocardiogram. Using a WBAN, the patient experiences a greater physical mobility and is no longer compelled to stay in the hospital. This paper offers a survey of the concept of Wireless Body Area Networks. First, we focus on some applications with special interest in patient monitoring. Then the communication in a WBAN and its positioning between the different technologies is discussed. An overview of the current research on the physical layer, existing MAC and network protocols is given. Further, cross layer and quality of service is discussed. As WBANs are placed on the human body and often transport private data, security is also considered. An overview of current and past projects is given. Finally, the open research issues and challenges are pointed out.
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paper_title: Body Area Networks: A Survey

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Advances in wireless communication technologies, such as wearable and implantable biosensors, along with recent developments in the embedded computing area are enabling the design, development, and implementation of body area networks. This class of networks is paving the way for the deployment of innovative healthcare monitoring applications. In the past few years, much of the research in the area of body area networks has focused on issues related to wireless sensor designs, sensor miniaturization, low-power sensor circuitry, signal processing, and communications protocols. In this paper, we present an overview of body area networks, and a discussion of BAN communications types and their related issues. We provide a detailed investigation of sensor devices, physical layer, data link layer, and radio technology aspects of BAN research. We also present a taxonomy of BAN projects that have been introduced/proposed to date. Finally, we highlight some of the design challenges and open issues that still need to be addressed to make BANs truly ubiquitous for a wide range of applications.
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paper_title: HomeMesh: a low-cost indoor wireless mesh for home networking

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Wi-Fi access technology has become popular in recent years. Many users nowadays use Wi-Fi to gain wireless access to the Internet from offices, public libraries, shopping malls, homes, and other places. However, current Wi-Fi deployment is limited to areas where wired LAN is available. Due to its relatively short transmission range in indoor environments (typically several tens of meters), Wi-Fi coverage needs to be extended significantly to full coverage of a certain area. The wireless mesh network (WMN) is a practical and effective solution. In this article we present HomeMesh, an off-the-shelf, simple, and cost-effective WMN for the indoor home environment. HomeMesh is based on simple protocols, implementable in normal notebooks or PCs, and is compatible with existing Wi-Fi APs and clients (i.e., no AP and client modifications). To achieve better end-to-end delay and throughput, HomeMesh dynamically selects its access path based on the ETX metric. We have implemented HomeMesh and conducted proofof- concept experiments in an indoor environment. Our mesh solution is shown to be effective in improving Wi-Fi services.
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paper_title: Sensor Networks for Ambient Intelligence

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Due to rapid advances in networking and sensing technology we are witnessing a growing interest in sensor networks, in which a variety of sensors are connected to each other and to computational devices capable of multimodal signal processing and data analysis. Such networks are seen to play an increasingly important role as key enablers in emerging pervasive computing technologies. In the first part of this paper we give an overview of recent developments in the area of multimodal sensor networks, paying special attention to ambient intelligence applications. In the second part, we discuss how the time series generated by data streams emanating from the sensors can be mined for temporal patterns, indicating cross-sensor signal correlations.
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paper_title: ReTrust: Attack-Resistant and Lightweight Trust Management for Medical Sensor Networks

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Wireless medical sensor networks (MSNs) enable ubiquitous health monitoring of users during their everyday lives, at health sites, without restricting their freedom. Establishing trust among distributed network entities has been recognized as a powerful tool to improve the security and performance of distributed networks such as mobile ad hoc networks and sensor networks. However, most existing trust systems are not well suited for MSNs due to the unique operational and security requirements of MSNs. Moreover, similar to most security schemes, trust management methods themselves can be vulnerable to attacks. Unfortunately, this issue is often ignored in existing trust systems. In this paper, we identify the security and performance challenges facing a sensor network for wireless medical monitoring and suggest it should follow a two-tier architecture. Based on such an architecture, we develop an attack-resistant and lightweight trust management scheme named ReTrust. This paper also reports the experimental results of the Collection Tree Protocol using our proposed system in a network of TelosB motes, which show that ReTrust not only can efficiently detect malicious/faulty behaviors, but can also significantly improve the network performance in practice.
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paper_title: Wireless mesh networks: a survey

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Wireless mesh networks (WMNs) consist of mesh routers and mesh clients, where mesh routers have minimal mobility and form the backbone of WMNs. They provide network access for both mesh and conventional clients. The integration of WMNs with other networks such as the Internet, cellular, IEEE 802.11, IEEE 802.15, IEEE 802.16, sensor networks, etc., can be accomplished through the gateway and bridging functions in the mesh routers. Mesh clients can be either stationary or mobile, and can form a client mesh network among themselves and with mesh routers. WMNs are anticipated to resolve the limitations and to significantly improve the performance of ad hoc networks, wireless local area networks (WLANs), wireless personal area networks (WPANs), and wireless metropolitan area networks (WMANs). They are undergoing rapid progress and inspiring numerous deployments. WMNs will deliver wireless services for a large variety of applications in personal, local, campus, and metropolitan areas. Despite recent advances in wireless mesh networking, many research challenges remain in all protocol layers. This paper presents a detailed study on recent advances and open research issues in WMNs. System architectures and applications of WMNs are described, followed by discussing the critical factors influencing protocol design. Theoretical network capacity and the state-of-the-art protocols for WMNs are explored with an objective to point out a number of open research issues. Finally, testbeds, industrial practice, and current standard activities related to WMNs are highlighted.
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paper_title: Sensor-Based Activity Recognition

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Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.
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paper_title: Recognizing independent and joint activities among multiple residents in smart environments

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The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper, we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.
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paper_title: Discovering Activities to Recognize and Track in a Smart Environment

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The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.
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paper_title: Experiences in the development of a Smart Lab

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There is now a growing demand to provide improved delivery of health and social care due to changes in the age profile of our population. One area where these services may be improved is through the development of smart living environments. Within this paper we provide an overview of the drivers behind the development of such environments along with details of the different ways in which they may exist. Finally, we provide details of our initial experiences in the establishment of a Smart Living Environment for the development of assistive technologies to support independent living.
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paper_title: Activity Recognition using Actigraph Sensor

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accelerometers are being extensively used in the recognition of simple ambulatory activities. Using wearable sensors for activity recognition is the latest topic of interest in smart home research. We use an Actigraph watch with an embedded accelerometer sensor to recognize real-life activities done in a home. Real-life activities include the set of Activities of Daily Living (ADL). ADLs are the crucial activities we perform everyday in our homes. Actigraph watches have been profusely used in sleep studies to determine the sleep/wake cycles and also the quality of sleep. In this paper, we investigate the possibility of using Actigraph watches to recognize activities. The data collected from an Actigraph watch was analyzed to predict ADLs (Activities of Daily Living). We apply machine learning algorithms to the Actigraph data to predict the ADLs. Also, a comparative study of activity prediction accuracy obtained from four machine learning algorithms is discussed.
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paper_title: Conditional random fields for activity recognition

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Activity recognition is a key component for creating intelligent, multi-agent systems. Intrinsically, activity recognition is a temporal classification problem. In this paper, we compare two models for temporal classification: hidden Markov models (HMMs), which have long been applied to the activity recognition problem, and conditional random fields (CRFs). CRFs are discriminative models for labeling sequences. They condition on the entire observation sequence, which avoids the need for independence assumptions between observations. Conditioning on the observations vastly expands the set of features that can be incorporated into the model without violating its assumptions. Using data from a simulated robot tag domain, chosen because it is multi-agent and produces complex interactions between observations, we explore the differences in performance between the discriminatively trained CRF and the generative HMM. Additionally, we examine the effect of incorporating features which violate independence assumptions between observations; such features are typically necessary for high classification accuracy. We find that the discriminatively trained CRF performs as well as or better than an HMM even when the model features do not violate the independence assumptions of the HMM. In cases where features depend on observations from many time steps, we confirm that CRFs are robust against any degradation in performance.
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paper_title: Human motion detection using Markov random fields

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In this paper, we propose Markov random fields (MRFs) to automatically detect a moving human body through minimizing the joint energy of the MRF for the velocity and relative position of body parts. The relaxation labeling algorithm is employed to find the best body part labeling configuration between MRFs and observed data. We detect a walking motion viewed monocularly based on point features, where some points are from the unoccluded body parts and some belong to the background. The results show that MRFs can detect human motions robustly and accurately.
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paper_title: Object relevance weight pattern mining for activity recognition and segmentation

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Monitoring daily activities of a person has many potential benefits in pervasive computing. These include providing proactive support for the elderly and monitoring anomalous behaviors. A typical approach in existing research on activity detection is to construct sequence-based models of low-level activity features based on the order of object usage. However, these models have poor accuracy, require many parameters to estimate, and demand excessive computational effort. Many other supervised learning approaches have been proposed but they all suffer from poor scalability due to the manual labeling involved in the training process. In this paper, we simplify the activity modeling process by relying on the relevance weights of objects as the basis of activity discrimination rather than on sequence information. For each activity, we mine the web to extract the most relevant objects according to their normalized usage frequency. We develop a KeyExtract algorithm for activity recognition and two algorithms, MaxGap and MaxGain, for activity segmentation with linear time complexities. Simulation results indicate that our proposed algorithms achieve high accuracy in the presence of different noise levels indicating their good potential in real-world deployment.
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paper_title: SMASH: a distributed sensing and processing garment for the classification of upper body postures

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This paper introduces a smart textile for posture classification. A distributed sensing and processing architecture is implemented into a loose fitting long sleeve shirt. Standardized interfaces to remote periphery support the variable placement of different sensor modalities at any location of the textile. ::: ::: The shirt is equipped with acceleration sensors in order to determine the postural resolution and the systems feasibility for applications in movement rehabilitation. For the garment characterization an arm posture measurement method is proposed and applied in a study with 5 users. ::: ::: The classification performance is analyzed on data from overall 8 users, conducting 12 posture types, relevant for shoulder and elbow joint rehabilitation. We present results for different user-modes, with classification rates of 89% for a user-independent evaluation. Moreover, the relation of body dimensions on the posture classification performance are analyzed.
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paper_title: Mining models of human activities from the web

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The ability to determine what day-to-day activity (such as cooking pasta, taking a pill, or watching a video) a person is performing is of interest in many application domains. A system that can do this requires models of the activities of interest, but model construction does not scale well: humans must specify low-level details, such as segmentation and feature selection of sensor data, and high-level structure, such as spatio-temporal relations between states of the model, for each and every activity. As a result, previous practical activity recognition systems have been content to model a tiny fraction of the thousands of human activities that are potentially useful to detect. In this paper, we present an approach to sensing and modeling activities that scales to a much larger class of activities than before. We show how a new class of sensors, based on Radio Frequency Identification (RFID) tags, can directly yield semantic terms that describe the state of the physical world. These sensors allow us to formulate activity models by translating labeled activities, such as 'cooking pasta', into probabilistic collections of object terms, such as 'pot'. Given this view of activity models as text translations, we show how to mine definitions of activities in an unsupervised manner from the web. We have used our technique to mine definitions for over 20,000 activities. We experimentally validate our approach using data gathered from actual human activity as well as simulated data.
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paper_title: Strategies for inference mechanism of conditional random fields for multiple-resident activity recognition in a smart home

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Multiple-resident activity recognition is a major challenge for building a smart-home system. In this paper, conditional random fields (CRFs) are chosen as our activity recognition models for overcoming this challenge. We evaluate our proposed approach with several strategies, including conditional random field with iterative inference and the one with decomposition inference, to enhance the commonly used CRFs so that they can be applied to a multipleresident environment. We use the multi-resident CASAS data collected at WSU (Washington State University) to validate these strategies. The results show that data association of non-obstructive sensor data is of vital importance to improve the performance of activity recognition in a multiple-resident environment. Furthermore, the study also suggests that human interaction be taken into consideration for further accuracy improvement.
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paper_title: Recognizing human motion with multiple acceleration sensors

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In this paper experiments with acceleration sensors are described for human activity recognition of a wearable device user. The use of principal component analysis and independent component analysis with a wavelet transform is tested for feature generation. Recognition of human activity is examined with a multilayer perceptron classifier. Best classification results for recognition of different human motion were 83-90%, and they were achieved by utilizing independent component analysis and principal component analysis. The difference between these methods turned out to be negligible.
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paper_title: Recognizing daily activities with RFID-based sensors

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We explore a dense sensing approach that uses RFID sensor network technology to recognize human activities. In our setting, everyday objects are instrumented with UHF RFID tags called WISPs that are equipped with accelerometers. RFID readers detect when the objects are used by examining this sensor data, and daily activities are then inferred from the traces of object use via a Hidden Markov Model. In a study of 10 participants performing 14 activities in a model apartment, our approach yielded recognition rates with precision and recall both in the 90% range. This compares well to recognition with a more intrusive short-range RFID bracelet that detects objects in the proximity of the user; this approach saw roughly 95% precision and 60% recall in the same study. We conclude that RFID sensor networks are a promising approach for indoor activity monitoring.
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paper_title: Voice activity detection driven acoustic event classification for monitoring in smart homes

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This contribution focuses on acoustic event detection and classification for monitoring of elderly people in ambient assistive living environments such as smart homes or nursing homes. We describe an autonomous system for robust detection of acoustic events in various practically relevant acoustic situations that benefits from a voice activity detection inspired preprocessing mechanism. Therefore, various already established voice activity detection schemes have been evaluated beforehand. As a specific use case, we address coughing as an acoustic event of interest which can be interpreted as an indicator for a potentially upcoming illness. After the detection of such events using a psychoacoustically motivated spectro-temporal representation (the so-called cochleogram), we forward its output to a statistical event modeling stage for automatic instantaneous emergency classification and long-term monitoring. The parameters derived by this procedure can then be used to inform medical or care-service personal.
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paper_title: Activity and location recognition using wearable sensors

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Using measured acceleration and angular velocity data gathered through inexpensive, wearable sensors, this dead-reckoning method can determine a user's location, detect transitions between preselected locations, and recognize and classify sitting, standing, and walking behaviors. Experiments demonstrate the proposed method's effectiveness.
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paper_title: Inferring High-Level Behavior from Low-Level Sensors

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We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using Expectation-Maximization. The training data is drawn from a GPS sensor stream that was collected by the authors over a period of three months. We demonstrate that by adding more external knowledge about bus routes and bus stops, accuracy is improved.
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paper_title: Simultaneous tracking & activity recognition (star) using many anonymous, binary sensors

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In this paper we introduce the simultaneous tracking and activity recognition (STAR) problem, which exploits the synergy between location and activity to provide the information necessary for automatic health monitoring. Automatic health monitoring can potentially help the elderly population live safely and independently in their own homes by providing key information to caregivers. Our goal is to perform accurate tracking and activity recognition for multiple people in a home environment. We use a “bottom-up” approach that primarily uses information gathered by many minimally invasive sensors commonly found in home security systems. We describe a Rao-Blackwellised particle filter for room-level tracking, rudimentary activity recognition (i.e., whether or not an occupant is moving), and data association. We evaluate our approach with experiments in a simulated environment and in a real instrumented home.
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paper_title: Bayesian Activity Recognition in Residence for Elders

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The growing population of elders in our society calls for a new approach in caregiving. By inferring what activities elderly are performing in their houses it is possible to determine their physical and cognitive capabilities. In this paper we describe probabilistic models for performing activity recognition from sensor patterns. We introduce a new observation model which takes the history of sensor readings into account. Results show that the new observation model improves accuracy, but a description using less parameters is likely to give even better results.
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paper_title: Activity Recognition in the Home Using Simple and Ubiquitous Sensors

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In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be “tape on and forget” devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used.
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paper_title: An activity monitoring system for elderly care using generative and discriminative models

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An activity monitoring system allows many applications to assist in care giving for elderly in their homes. In this paper we present a wireless sensor network for unintrusive observations in the home and show the potential of generative and discriminative models for recognizing activities from such observations. Through a large number of experiments using four real world datasets we show the effectiveness of the generative hidden Markov model and the discriminative conditional random fields in activity recognition.
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paper_title: Learning Situation Models in a Smart Home

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This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.
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paper_title: A survey of vision-based methods for action representation, segmentation and recognition

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Action recognition has become a very important topic in computer vision, with many fundamental applications, in robotics, video surveillance, human-computer interaction, and multimedia retrieval among others and a large variety of approaches have been described. The purpose of this survey is to give an overview and categorization of the approaches used. We concentrate on approaches that aim on classification of full-body motions, such as kicking, punching, and waving, and we categorize them according to how they represent the spatial and temporal structure of actions; how they segment actions from an input stream of visual data; and how they learn a view-invariant representation of actions.
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paper_title: Fabric-based strain sensors for measuring movement in wearable telemonitoring applications

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This paper summarises preliminary work comparing conductive yarns, knitting structures and yarn compositions in order to integrate smart sensor strips into a surrounding garment as a kinematic measurement tool. The conductive areas of the garment were to be used as a strain-sensitive material; ultimately measuring knee joint movement. In total, thirty sample fabrics were developed using conductive yarns; six of which were chosen to be tested for responsiveness during repeated strain. Preliminary tests showed good levels of responsiveness to strain and acceptable levels of recovery.
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paper_title: Activity recognition and monitoring using multiple sensors on different body positions

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The design of an activity recognition and monitoring system based on the eWatch, multi-sensor platform worn on different body positions, is presented in this paper. The system identifies the user's activity in realtime using multiple sensors and records the classification results during a day. We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity. The classification accuracy on different body positions used for wearing electronic devices was evaluated.
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paper_title: Learning Setting-Generalized Activity Models for Smart Spaces

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Smart home activity recognition systems can learn generalized models for common activities that span multiple environment settings and resident types.
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paper_title: Transfer learning for activity recognition: a survey

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Many intelligent systems that focus on the needs of a human require information about the activities being performed by the human. At the core of this capability is activity recognition, which is a challenging and well-researched problem. Activity recognition algorithms require substantial amounts of labeled training data yet need to perform well under very diverse circumstances. As a result, researchers have been designing methods to identify and utilize subtle connections between activity recognition datasets, or to perform transfer-based activity recognition. In this paper, we survey the literature to highlight recent advances in transfer learning for activity recognition. We characterize existing approaches to transfer-based activity recognition by sensor modality, by differences between source and target environments, by data availability, and by type of information that is transferred. Finally, we present some grand challenges for the community to consider as this field is further developed.
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paper_title: Health-status monitoring through analysis of behavioral patterns

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With the rapid growth of the elderly population, there is a need to support the ability of elders to maintain an independent and healthy lifestyle in their homes rather than through more expensive and isolated care facilities. One approach to accomplish these objectives employs the concepts of ambient intelligence to remotely monitor an elder's activities and condition. The SmartHouse project uses a system of basic sensors to monitor a person's in-home activity; a prototype of the system is being tested within a subject's home. We examined whether the system could be used to detect behavioral patterns and report the results in this paper. Mixture models were used to develop a probabilistic model of behavioral patterns. The results of the mixture-model analysis were then evaluated by using a log of events kept by the occupant.
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paper_title: Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning

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Activity recognition has attracted increasing attention in recent years due to its potential to enable a number of compelling context-aware applications. As most approaches rely on supervised learning methods, obtaining substantial amounts of labeled data is often an important bottle-neck for these approaches. In this paper, we present and explore a novel method for activity recognition from sparsely labeled data. The method is based on multi-instance learning allowing to significantly reduce the required level of supervision. In particular we propose several novel extensions of multi-instance learning to support different annotation strategies. The validity of the approach is demonstrated on two public datasets for three different labeling scenarios.
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paper_title: Activity recognition from user-annotated acceleration data

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In this work, algorithms are developed and evaluated to de- tect physical activities from data acquired using five small biaxial ac- celerometers worn simultaneously on different parts of the body. Ac- celeration data was collected from 20 subjects without researcher su- pervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. De- cision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers - thigh and wrist - the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves.
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paper_title: Mining sequential patterns

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We are given a large database of customer transactions, where each transaction consists of customer-id, transaction time, and the items bought in the transaction. We introduce the problem of mining sequential patterns over such databases. We present three algorithms to solve this problem, and empirically evaluate their performance using synthetic data. Two of the proposed algorithms, AprioriSome and AprioriAll, have comparable performance, albeit AprioriSome performs a little better when the minimum number of customers that must support a sequential pattern is low. Scale-up experiments show that both AprioriSome and AprioriAll scale linearly with the number of customer transactions. They also have excellent scale-up properties with respect to the number of transactions per customer and the number of items in a transaction. >
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paper_title: Distributed Recognition of Human Actions Using Wearable Motion Sensor Networks

paper_content:

We propose a distributed recognition framework to classify continuous human actions using a low-bandwidth wearable motion sensor network, called distributed sparsity classifier (DSC). The algorithm classifies human actions using a set of training motion sequences as prior examples. It is also capable of rejecting outlying actions that are not in the training categories. The classification is operated in a distributed fashion on individual sensor nodes and a base station computer. We model the distribution of multiple action classes as a mixture subspace model, one subspace for each action class. Given a new test sample, we seek the sparsest linear representation of the sample w.r.t. all training examples. We show that the dominant coefficients in the representation only correspond to the action class of the test sample, and hence its membership is encoded in the sparse representation. Fast linear solvers are provided to compute such representation via e 1-minimization. To validate the accuracy of the framework, a public wearable action recognition database is constructed, called wearable action recognition database (WARD). The database is comprised of 20 human subjects in 13 action categories. Using up to five motion sensors in the WARD database, DSC achieves state-of-the-art performance. We further show that the recognition precision only decreases gracefully using smaller subsets of active sensors. It validates the robustness of the distributed recognition framework on an unreliable wireless network. It also demonstrates the ability of DSC to conserve sensor energy for communication while preserve accurate global classification. ::: ::: (This work was partially supported by ARO MURI W911NF-06-1-0076, NSF TRUST Center, and the startup funding from the University of Texas and Texas Instruments.)
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paper_title: Inferring activities from interactions with objects

paper_content:

A key aspect of pervasive computing is using computers and sensor networks to effectively and unobtrusively infer users' behavior in their environment. This includes inferring which activity users are performing, how they're performing it, and its current stage. Recognizing and recording activities of daily living is a significant problem in elder care. A new paradigm for ADL inferencing leverages radio-frequency-identification technology, data mining, and a probabilistic inference engine to recognize ADLs, based on the objects people use. We propose an approach that addresses these challenges and shows promise in automating some types of ADL monitoring. Our key observation is that the sequence of objects a person uses while performing an ADL robustly characterizes both the ADL's identity and the quality of its execution. So, we have developed Proactive Activity Toolkit (PROACT).
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paper_title: An activity recognition system for mobile phones

paper_content:

We present a novel system that recognizes and records the motional activities of a person using a mobile phone. Wireless sensors measuring the intensity of motions are attached to body parts of the user. Sensory data is collected by a mobile application that recognizes prelearnt activities in real-time. For efficient motion pattern recognition of gestures and postures, feed-forward backpropagation neural networks are adopted. The design and implementation of the system are presented along with the records of our experiences. Results show high recognition rates for distinguishing among six different motion patterns. The recognized activity can be used as an additional retrieval key in an extensive mobile memory recording and sharing project. Power consumption measurements of the wireless communication and the recognition algorithm are provided to characterize the resource requirements of the system.
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paper_title: Fine-grained activity recognition by aggregating abstract object usage

paper_content:

In this paper we present results related to achieving finegrained activity recognition for context-aware computing applications. We examine the advantages and challenges of reasoning with globally unique object instances detected by an RFID glove. We present a sequence of increasingly powerful probabilistic graphical models for activity recognition. We show the advantages of adding additional complexity and conclude with a model that can reason tractably about aggregated object instances and gracefully generalizes from object instances to their classes by using abstraction smoothing. We apply these models to data collected from a morning household routine.
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paper_title: A Long-Term Evaluation of Sensing Modalities for Activity Recognition

paper_content:

We study activity recognition using 104 hours of annotated data collected from a person living in an instrumented home. The home contained over 900 sensor inputs, including wired reed switches, current and water flow inputs, object and person motion detectors, and RFID tags. Our aim was to compare different sensor modalities on data that approached "real world" conditions, where the subject and annotator were unaffiliated with the authors. We found that 10 infra-red motion detectors outperformed the other sensors on many of the activities studied, especially those that were typically performed in the same location. However, several activities, in particular "eating" and "reading" were difficult to detect, and we lacked data to study many fine-grained activities. We characterize a number of issues important for designing activity detection systems that may not have been as evident in prior work when data was collected under more controlled conditions.
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paper_title: Sensor-Based Human Activity Recognition in a Multi-user Scenario

paper_content:

Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.
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paper_title: Object relevance weight pattern mining for activity recognition and segmentation

paper_content:

Monitoring daily activities of a person has many potential benefits in pervasive computing. These include providing proactive support for the elderly and monitoring anomalous behaviors. A typical approach in existing research on activity detection is to construct sequence-based models of low-level activity features based on the order of object usage. However, these models have poor accuracy, require many parameters to estimate, and demand excessive computational effort. Many other supervised learning approaches have been proposed but they all suffer from poor scalability due to the manual labeling involved in the training process. In this paper, we simplify the activity modeling process by relying on the relevance weights of objects as the basis of activity discrimination rather than on sequence information. For each activity, we mine the web to extract the most relevant objects according to their normalized usage frequency. We develop a KeyExtract algorithm for activity recognition and two algorithms, MaxGap and MaxGain, for activity segmentation with linear time complexities. Simulation results indicate that our proposed algorithms achieve high accuracy in the presence of different noise levels indicating their good potential in real-world deployment.
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paper_title: An unsupervised approach to activity recognition and segmentation based on object-use fingerprints

paper_content:

Human activity recognition is an important task which has many potential applications. In recent years, researchers from pervasive computing are interested in deploying on-body sensors to collect observations and applying machine learning techniques to model and recognize activities. Supervised machine learning techniques typically require an appropriate training process in which training data need to be labeled manually. In this paper, we propose an unsupervised approach based on object-use fingerprints to recognize activities without human labeling. We show how to build our activity models based on object-use fingerprints, which are sets of contrast patterns describing significant differences of object use between any two activity classes. We then propose a fingerprint-based algorithm to recognize activities. We also propose two heuristic algorithms based on object relevance to segment a trace and detect the boundary of any two adjacent activities. We develop a wearable RFID system and conduct a real-world trace collection done by seven volunteers in a smart home over a period of 2 weeks. We conduct comprehensive experimental evaluations and comparison study. The results show that our recognition algorithm achieves a precision of 91.4% and a recall 92.8%, and the segmentation algorithm achieves an accuracy of 93.1% on the dataset we collected.
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paper_title: Creating Evolving User Behavior Profiles Automatically

paper_content:

Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach for creating and recognizing automatically the behavior profile of a computer user is presented. In this case, a computer user behavior is represented as the sequence of the commands she/he types during her/his work. This sequence is transformed into a distribution of relevant subsequences of commands in order to find out a profile that defines its behavior. Also, because a user profile is not necessarily fixed but rather it evolves/changes, we propose an evolving method to keep up to date the created profiles using an Evolving Systems approach. In this paper, we combine the evolving classifier with a trie-based user profiling to obtain a powerful self-learning online scheme. We also develop further the recursive formula of the potential of a data point to become a cluster center using cosine distance, which is provided in the Appendix. The novel approach proposed in this paper can be applicable to any problem of dynamic/evolving user behavior modeling where it can be represented as a sequence of actions or events. It has been evaluated on several real data streams.
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paper_title: Transferring knowledge of activity recognition across sensor networks

paper_content:

A problem in performing activity recognition on a large scale (i.e. in many homes) is that a labelled data set needs to be recorded for each house activity recognition is performed in. This is because most models for activity recognition require labelled data to learn their parameters. In this paper we introduce a transfer learning method for activity recognition which allows the use of existing labelled data sets of various homes to learn the parameters of a model applied in a new home. We evaluate our method using three large real world data sets and show our approach achieves good classification performance in a home for which little or no labelled data is available.
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paper_title: Improving the recognition of interleaved activities

paper_content:

We introduce Interleaved Hidden Markov Models for recognizing multitasked activities. The model captures both inter-activity and intra-activity dynamics. Although the state space is intractably large, we describe an approximation that is both effective and efficient. This method significantly reduces the error rate when compared with previously proposed methods. The algorithm is suitable for mobile platforms where computational resources may be limited.
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paper_title: Coping with multiple residents in a smart environment

paper_content:

Smart environment research has resulted in many useful tools for modeling, monitoring, and adapting to a single resident. However, many of these tools are not equipped for coping with multiple residents in the same environment simultaneously. In this paper we investigate a first step in coping with multiple residents, that of attributing sensor events to individuals in a multi-resident environment. We discuss approaches that can be used to achieve this goal and we evaluate our implementations in the context of two physical smart environment testbeds. We also explore how learning resident identifiers can aid in performing other analyses on smart environment sensor data such as activity recognition.
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paper_title: Mining Sensor Streams for Discovering Human Activity Patterns over Time

paper_content:

In recent years, new emerging application domains have introduced new constraints and methods in data mining field. One of such application domains is activity discovery from sensor data. Activity discovery and recognition plays an important role in a wide range of applications from assisted living to security and surveillance. Most of the current approaches for activity discovery assume a static model of the activities and ignore the problem of mining and discovering activities from a data stream over time. Inspired by the unique requirements of activity discovery application domain, in this paper we propose a new stream mining method for finding sequential patterns over time from streaming non-transaction data using multiple time granularities. Our algorithm is able to find sequential patterns, even if the patterns exhibit discontinuities (interruptions) or variations in the sequence order. Our algorithm also addresses the problem of dealing with rare events across space and over time. We validate the results of our algorithms using data collected from two different smart apartments.
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paper_title: Learning Setting-Generalized Activity Models for Smart Spaces

paper_content:

Smart home activity recognition systems can learn generalized models for common activities that span multiple environment settings and resident types.
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paper_title: Recognizing independent and joint activities among multiple residents in smart environments

paper_content:

The pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. A primary challenge that needs to be tackled to meet this need is the ability to recognize and track functional activities that people perform in their own homes and everyday settings. In this paper, we look at approaches to perform real-time recognition of Activities of Daily Living. We enhance other related research efforts to develop approaches that are effective when activities are interrupted and interleaved. To evaluate the accuracy of our recognition algorithms we assess them using real data collected from participants performing activities in our on-campus smart apartment testbed.
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paper_title: Activity knowledge transfer in smart environments

paper_content:

Current activity recognition approaches usually ignore knowledge learned in previous smart environments when training the recognition algorithms for a new smart environment. In this paper, we propose a method of transferring the knowledge of learned activities in multiple physical spaces, e.g. homes A and B, to a new target space, e.g. home C. Transferring the knowledge of learned activities to a target space results in reducing the data collection and annotation period, achieving an accelerated learning pace and exploiting the insights from previous settings. We validate our algorithms using data collected from several smart apartments.
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paper_title: The Advanced Health and Disaster Aid Network: A Light-Weight Wireless Medical System for Triage

paper_content:

Advances in semiconductor technology have resulted in the creation of miniature medical embedded systems that can wirelessly monitor the vital signs of patients. These lightweight medical systems can aid providers in large disasters who become overwhelmed with the large number of patients, limited resources, and insufficient information. In a mass casualty incident, small embedded medical systems facilitate patient care, resource allocation, and real-time communication in the advanced health and disaster aid network (AID-N). We present the design of electronic triage tags on lightweight, embedded systems with limited memory and computational power. These electronic triage tags use noninvasive, biomedical sensors (pulse oximeter, electrocardiogram, and blood pressure cuff) to continuously monitor the vital signs of a patient and deliver pertinent information to first responders. This electronic triage system facilitates the seamless collection and dissemination of data from the incident site to key members of the distributed emergency response community. The real-time collection of data through a mesh network in a mass casualty drill was shown to approximately triple the number of times patients that were triaged compared with the traditional paper triage system.
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paper_title: GAIS: A Method for Detecting Interleaved Sequential Patterns from Imperfect Data

paper_content:

This paper introduces a novel method, GAIS, for detecting interleaved sequential patterns from databases. A case, where data is of low quality and has errors is considered. Pattern detection from erroneous data, which contains multiple interleaved patterns is an important problem in a field of sensor network applications. We approach the problem by grouping data rows with the help of a model database and comparing groups with the models. In evaluation GAIS clearly outperforms the greedy algorithm. Using GAIS desired sequential patterns can be detected from low quality data.
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paper_title: Mining Sensor Streams for Discovering Human Activity Patterns over Time

paper_content:

In recent years, new emerging application domains have introduced new constraints and methods in data mining field. One of such application domains is activity discovery from sensor data. Activity discovery and recognition plays an important role in a wide range of applications from assisted living to security and surveillance. Most of the current approaches for activity discovery assume a static model of the activities and ignore the problem of mining and discovering activities from a data stream over time. Inspired by the unique requirements of activity discovery application domain, in this paper we propose a new stream mining method for finding sequential patterns over time from streaming non-transaction data using multiple time granularities. Our algorithm is able to find sequential patterns, even if the patterns exhibit discontinuities (interruptions) or variations in the sequence order. Our algorithm also addresses the problem of dealing with rare events across space and over time. We validate the results of our algorithms using data collected from two different smart apartments.
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paper_title: Improving home automation by discovering regularly occurring device usage patterns

paper_content:

The data stream captured by recording inhabitant-device interactions in an environment can be mined to discover significant patterns, which an intelligent agent could use to automate device interactions. However, this knowledge discovery problem is complicated by several challenges, such as excessive noise in the data, data that does not naturally exist as transactions, a need to operate in real time, and a domain where frequency may not be the best discriminator. We propose a novel data mining technique that addresses these challenges and discovers regularly-occurring interactions with a smart home. We also discuss a case study that shows the data mining technique can improve the accuracy of two prediction algorithms, thus demonstrating multiple uses for a home automation system. Finally, we present an analysis of the algorithm and results obtained using inhabitant interactions.
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paper_title: Health-status monitoring through analysis of behavioral patterns

paper_content:

With the rapid growth of the elderly population, there is a need to support the ability of elders to maintain an independent and healthy lifestyle in their homes rather than through more expensive and isolated care facilities. One approach to accomplish these objectives employs the concepts of ambient intelligence to remotely monitor an elder's activities and condition. The SmartHouse project uses a system of basic sensors to monitor a person's in-home activity; a prototype of the system is being tested within a subject's home. We examined whether the system could be used to detect behavioral patterns and report the results in this paper. Mixture models were used to develop a probabilistic model of behavioral patterns. The results of the mixture-model analysis were then evaluated by using a log of events kept by the occupant.
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paper_title: Keeping the Resident in the Loop: Adapting the Smart Home to the User

paper_content:

Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.
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paper_title: Discovering Activities to Recognize and Track in a Smart Environment

paper_content:

The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.
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paper_title: Constraint-based sequential pattern mining: the pattern-growth methods

paper_content:

Constraints are essential for many sequential pattern mining applications. However, there is no systematic study on constraint-based sequential pattern mining. In this paper, we investigate this issue and point out that the framework developed for constrained frequent-pattern mining does not fit our mission well. An extended framework is developed based on a sequential pattern growth methodology. Our study shows that constraints can be effectively and efficiently pushed deep into the sequential pattern mining under this new framework. Moreover, this framework can be extended to constraint-based structured pattern mining as well.
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paper_title: An approach to cognitive assessment in smart home

paper_content:

In this paper, we describe an approach to developing an ecologically valid framework for performing automated cognitive assessment. To automate assessment, we use a machine learning approach that builds a model of cognitive health based on observations of activity performance and uses lab-based assessment to provide ground truth for training and testing the learning algorithm. To evaluate our approach, we recruited older adults to perform a set of activities in our smart home test-bed. While participants perform activities, sensors placed in the smart home unobtrusively capture the progress of the activity. During analysis, we extract features that indicate how well participants perform the activities. Our machine-learning algorithm accepts these features as input and outputs the cognitive status of the participants as belonging to one of two groups: Cognitively healthy or Dementia. We conclude that machine-learning algorithms can distinguish between cognitively healthy older adults and older adults with dementia given adequate features that represent how well they have performed the activity.
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paper_title: Anomaly Detection Using Temporal Data Mining in a Smart Home Environment

paper_content:

Objectives: To many people, home is a sanctuary. With the maturing of smart home technologies, many people with cognitive and physical disabilities can lead independent lives in their own homes for extended periods of time. In this paper, we investigate the design of machine learning algorithms that support this goal. We hypothesize that machine learning algorithms can be designed to automatically learn models of resident behavior in a smart home, and that the results can be used to perform automated health monitoring and to detect anomalies. Methods: Specifically, our algorithms draw upon the temporal nature of sensor data collected in a smart home to build a model of expected activities and to detect unexpected, and possibly health-critical, events in the home. Results: We validate our algorithms using synthetic data and real activity data collected from volunteers in an automated smart environment. Conclusions: The results from our experiments support our hypothesis that a model can be learned from observed smart home data and used to report anomalies, as they occur, in a smart home.
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paper_title: Modeling the scaling properties of human mobility

paper_content:

While the fat tailed jump size and the waiting time distributions characterizing individual human trajectories strongly suggest the relevance of the continuous time random walk (CTRW) models of human mobility, no one seriously believes that human traces are truly random. Given the importance of human mobility, from epidemic modeling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model not only accounts for the empirically observed scaling laws but also allows us to analytically predict most of the pertinent scaling exponents.
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paper_title: Sensor-Based Abnormal Human-Activity Detection

paper_content:

With the availability of affordable sensors and sensor networks, sensor-based human activity recognition has attracted much attention in artificial intelligence and ubiquitous computing. In this paper, we present a novel two-phase approach for detecting abnormal activities based on wireless sensors attached to a human body. Detecting abnormal activities is a particular important task in security monitoring and healthcare applications of sensor networks, among many others. Traditional approaches to this problem suffer from a high false positive rate, particularly when the collected sensor data are biased towards normal data while the abnormal events are rare. Therefore, there is a lack of training data for many traditional data mining methods to be applied. To solve this problem, our approach first employs a one-class support vector machine (SVM) that is trained on commonly available normal activities, which filters out the activities that have a very high probability of being normal. We then derive abnormal activity models from a general normal model via a kernel nonlinear regression (KNLR) to reduce false positive rate in an unsupervised manner. We show that our approach provides a good tradeoff between abnormality detection rate and false alarm rate, and allows abnormal activity models to be automatically derived without the need to explicitly label the abnormal training data, which are scarce. We demonstrate the effectiveness of our approach using real data collected from a sensor network that is deployed in a realistic setting.
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paper_title: Planning as Heuristic Search

paper_content:

In the AIPS98 Planning Contest, the HSP planner showed that heuristic search planners can be competitive with state-of-the-art Graphplan and SAT planners. Heuristic search planners like HSP transform planning problems into problems of heuristic search by automatically extracting heuristics from Strips encodings. They differ from specialized problem solvers such as those developed for the 24-Puzzle and Rubik’s Cube in that they use a general declarative language for stating problems and a general mechanism for extracting heuristics from these representations. In this paper, we study a family of heuristic search planners that are based on a simple and general heuristic that assumes that action preconditions are independent. The heuristic is then used in the context of best-first and hill-climbing search algorithms, and is tested over a large collection of domains. We then consider variations and extensions such as reversing the direction of the search for speeding node evaluation, and extracting information about propositional invariants for avoiding dead-ends. We analyze the resulting planners, evaluate their performance, and explain when they do best. We also compare the performance of these planners with two state-of-the-art planners, and show that the simplest planner based on a pure best-first search yields the most solid performance over a large set of problems. We also discuss the strengths and limitations of this approach, establish a correspondence between heuristic search planning and Graphplan, and briefly survey recent ideas that can reduce the current gap in performance between general heuristic search planners and specialized solvers.  2001 Elsevier Science B.V. All rights reserved.
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paper_title: What Planner for Ambient Intelligence Applications?

paper_content:

The development of ambient intelligence (AmI) applications that effectively adapt to the needs of the users and environments requires, among other things, the presence of planning mechanisms for goal-oriented behavior. Planning is intended as the ability of an AmI system to build a course of actions that, when carried out by the devices in the environment, achieve a given goal. The problem of planning in AmI has not yet been adequately explored in literature. We propose a planning system for AmI applications, based on the hierarchical task network (HTN) approach and called distributed hierarchical task network (D- HTN), able to find courses of actions to address given goals. The plans produced by D-HTN are flexibly tailored to exploit the capabilities of the devices currently available in the environment in the best way. We discuss both the architecture and the implementation of D-HTN. Moreover, we present some of the experimental results that validated the proposed planner in a realistic application scenario in which an AmI system monitors and answers the needs of a diabetic patient.
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paper_title: Fast Planning Through Planning Graph Analysis

paper_content:

Abstract We introduce a new approach to planning in STRIPS-like domains based on constructing and analyzing a compact structure we call a planning graph. We describe a new planner, Graphplan, that uses this paradigm. Graphplan always returns a shortest possible partial-order plan, or states that no valid plan exists. We provide empirical evidence in favor of this approach, showing that Graphplan outperforms the total-order planner, Prodigy and the partial-order planner, UCPOP, on a variety of interesting natural and artificial planning problems. We also give empirical evidence that the plans produced by Graphplan are quite sensible. Since searches made by this approach are fundamentally different from the searches of other common planning methods, they provide a new perspective on the planning problem.
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paper_title: O-Plan: a Web-based AI Planning Agent

paper_content:

In these demonstrations we show O-Plan, an AI planning agent working over the WWW. There are a number of demonstrations ranging from a simple “single shot” generation of Unix systems administration scripts through to comprehensive use of AI technologies across the whole planning lifecycle in military and civilian crisis situations The applications are derived from actual user requirements and domain knowledge. The AI planning technologies demonstrated include: • Domain knowledge elicitation • Rich plan representation and use • Hierarchical Task Network Planning • Detailed constraint management • Goal structure-based plan monitoring • Dynamic issue handling • Plan repair in low and high tempo situations • Interfaces for users with different roles • Management of planning and execution workflow The featured demonstrations, and others, are available at http://www.aiai.ed.ac.uk/~oplan/isd/
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paper_title: The use of computer vision in an intelligent environment to support aging-in-place, safety, and independence in the home

paper_content:

This paper discusses the use of computer vision in pervasive healthcare systems, specifically in the design of a sensing agent for an intelligent environment that assists older adults with dementia during an activity of daily living. An overview of the techniques applied in this particular example is provided, along with results from preliminary trials completed using the new sensing agent. A discussion of the results obtained to date is presented, including technical and social issues that remain for the advancement and acceptance of this type of technology within pervasive healthcare.
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paper_title: Plans and planning in smart homes

paper_content:

In this chapter, we review the use (and uses) of plans and planning in Smart Homes. Plans have several applications within Smart Homes, including: sharing task execution with the home's inhabitants, providing task guidance to inhabitants, and to identifying emergencies. These plans are not necessarily generated automatically, nor are they always represented in a human-readable form. The chapter ends with a discussion of the research issues surrounding the integration of plans and planning into Smart Homes.
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paper_title: An investigation into reactive planning in complex domains

paper_content:

A model of purely reactive planning is proposed based on the concept of reactive action packages. A reactive action package, or RAP, can be thought of as an independent entity pursuing some goal in competition with many others at execution time. The RAP processing algorithm addresses the problems of execution monitoring and replanning in uncertain domains with a single, uniform representation and control structure. Use of the RAP model as a basis for adaptive strategic planning is also discussed.
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paper_title: Implementing decision support systems: a survey

paper_content:

The successful implementation of decision support systems (DSS) is a function of many factors. This paper provides an overview of the factors that determine the success or failure of DSS. The paper discusses the following topics and concepts: assessing successful implementation; research on implementation; implementation frameworks; the implementation process; and research guides.
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paper_title: Context-aware knowledge modelling for decision support in e-health

paper_content:

In the context of e-health, professionals and healthcare service providers in various organisational and geographical locations are to work together, using information and communication systems, for the purpose of providing better patient-centred and technology-supported healthcare services at anytime and from anywhere. However, various organisations and geographies have varying contexts of work, which are dependent on their local work culture, available expertise, available technologies, people's perspectives and attitudes and organisational and regional agendas. As a result, there is the need to ensure that a suggestion - information and knowledge - provided by a professional to support decision making in a different, and often distant, organisation and geography takes into cognizance the context of the local work setting in which the suggestion is to be used. To meet this challenge, we propose a framework for context-aware knowledge modelling in e-health, which we refer to as ContextMorph. ContextMorph combines the commonKADS knowledge modelling methodology with the concept of activity landscape and context-aware modelling techniques in order to morph, i.e. enrich and optimise, a knowledge resource to support decision making across various contexts of work. The goal is to integrate explicit information and tacit expert experiences across various work domains into a knowledge resource adequate for supporting the operational context of the work setting in which it is to be used.
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paper_title: Persuasion in ambient intelligence

paper_content:

Although the field of persuasive technologies has lately attracted a lot of attention, only recently the notion of ambient persuasive technologies was introduced. Ambient persuasive technologies can be integrated into every aspect of life, and as such have greater persuasive power than the traditional box like machines. This article discusses ambient persuasion and poses a model that structures the knowledge from social sciences on persuasion, attitude change, and behavior change. Using this model the challenges of ambient persuasive technologies to fulfill its persuasive promises are identified. From the ambient persuasion model it is clear that ambient persuasive technologies can go beyond traditional persuasive technologies by being context and situational aware, by interpreting individual differences between users, and by being a social actor in their own right.
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paper_title: Physical, Social, and Experiential Knowledge in Pervasive Computing Environments

paper_content:

Pervasive computing designers and researchers often create services and applications to help people record their experiences. At the same time, cheap, small, and easy-to-deploy recording technologies are quickly emerging throughout public spaces. In many ways, these technologies are pervasive computing realized. Understanding how people deal with audio and video recording is therefore a good way to explore how people might adopt, adapt, and react to pervasive computing technologies in general. A long-term deployment of a system for recording experiences in informal spaces demonstrates that people use physical, social, and experiential knowledge to determine new technologies' relative utility and safety. In this paper, we aim to add significantly to the research surrounding security and privacy concerns by focusing on them rather than just noting them as a side effect of testing an application's utility and usability.
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paper_title: A Survey on Privacy Preserving Data Mining

paper_content:

Privacy preserving becomes an important issue in the development progress of data mining techniques. Privacy preserving data mining has become increasingly popular because it allows sharing of privacy-sensitive data for analysis purposes. So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In turn, such problems in data collection can affect the success of data mining, which relies on sufficient amounts of accurate data in order to produce meaningful results. In recent years, the wide availability of personal data has made the problem of privacy preserving data mining an important one. A number of methods have recently been proposed for privacy preserving data mining of multidimensional data records. This paper intends to reiterate several privacy preserving data mining technologies clearly and then proceeds to analyze the merits and shortcomings of these technologies.
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paper_title: Minimum spanning tree partitioning algorithm for microaggregation

paper_content:

This paper presents a clustering algorithm for partitioning a minimum spanning tree with a constraint on minimum group size. The problem is motivated by microaggregation, a disclosure limitation technique in which similar records are aggregated into groups containing a minimum of k records. Heuristic clustering methods are needed since the minimum information loss microaggregation problem is NP-hard. Our MST partitioning algorithm for microaggregation is sufficiently efficient to be practical for large data sets and yields results that are comparable to the best available heuristic methods for microaggregation. For data that contain pronounced clustering effects, our method results in significantly lower information loss. Our algorithm is general enough to accommodate different measures of information loss and can be used for other clustering applications that have a constraint on minimum group size.
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paper_title: Findings from a participatory evaluation of a smart home application for older adults.

paper_content:

The aim of this paper is to present a participatory evaluation of an actual "smart home" project implemented in an independent retirement facility. Using the participatory evaluation process, residents guided the research team through development and implementation of the initial phase of a smart home project designed to assist residents to remain functionally independent and age in place. We recruited nine residents who provided permission to install the technology in their apartments. We conducted a total of 75 interviews and three observational sessions. Residents expressed overall positive perceptions of the sensor technologies and did not feel that these interfered with their daily activities. The process of adoption and acceptance of the sensors included three phases, familiarization, adjustment and curiosity, and full integration. Residents did not express privacy concerns. They provided detailed feedback and suggestions that were integrated into the redesign of the system. They also reported a sense of control resulting from their active involvement in the evaluation process. Observational sessions confirmed that the sensors were not noticeable and residents did not change their routines. The participatory evaluation approach not only empowers end-users but it also allows for the implementation of smart home systems that address residents' needs.
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paper_title: MagIC System: a New Textile-Based Wearable Device for Biological Signal Monitoring. Applicability in Daily Life and Clinical Setting

paper_content:

The paper presents a new textile-based wearable system for the unobtrusive recording of cardiorespiratory and motion signals during spontaneous behavior along with the first results concerning the application of this device in daily life and in a clinical environment. The system, called MagIC (Maglietta Interattiva Computerizzata), is composed of a vest, including textile sensors for detecting ECG and respiratory activity, and a portable electronic board for motion detection, signal preprocessing and wireless data transmission to a remote monitoring station. The MagIC system has been tested in freely moving subjects at work, at home, while driving and cycling and in microgravity condition during a parabolic flight. Applicability of the system in cardiac in-patients is now under evaluation. Preliminary data derived from recordings performed on patients in bed and during physical exercise showed 1) good signal quality over most of the monitoring periods, 2) a correct identification of arrhythmic events, and 3) a correct estimation of the average beat-by-beat heart rate. These positive results supports further developments of the MagIC system, aimed at tuning this approach for a routine use in clinical practice and in daily life
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paper_title: The IMMED project: wearable video monitoring of people with age dementia

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In this paper, we describe a new application for multimedia indexing, using a system that monitors the instrumental activities of daily living to assess the cognitive decline caused by dementia. The system is composed of a wearable camera device designed to capture audio and video data of the instrumental activities of a patient, which is leveraged with multimedia indexing techniques in order to allow medical specialists to analyze several hour long observation shots efficiently.
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paper_title: New approach for the early detection of dementia by recording in-house activities.

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People with dementia often have low physical activity and some sleep problems. This study focused on daily life activities and sleeping conditions, and examined the use of these parameters for detecting dementia. Five passive infrared (IR) sensors were installed in each of 14 subject's houses. Each patient lived alone. The subjects' in-house movements were recorded by the passive IR sensor for approximately 3 months (average, 78 days). Based on these records, the following parameters of life activities were assessed: (1) the number of outings, (2) total sleep time, (3) number of sleep interruptions, and (4) sleep rhythm. Subjects with impaired cognition (Mini Mental State Examination [MMSE] < 24) had a significantly lesser number of outings (p = 0.001) and a tendency toward a shorter sleep time (p = 0.054) in comparison with control subjects (MMSE >or= 24). These results suggest that the monitoring of life activities by using passive infrared sensors could be an efficient method for detecting dementia.
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paper_title: Acoustic fall detection using Gaussian mixture models and GMM supervectors

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We present a system that detects human falls in the home environment, distinguishing them from competing noise, by using only the audio signal from a single far-field microphone. The proposed system models each fall or noise segment by means of a Gaussian mixture model (GMM) supervector, whose Euclidean distance measures the pairwise difference between audio segments. A support vector machine built on a kernel between GMM supervectors is employed to classify audio segments into falls and various types of noise. Experiments on a dataset of human falls, collected as part of the Netcarity project, show that the method improves fall classification F-score to 67% from 59% of a baseline GMM classifier. The approach also effectively addresses the more difficult fall detection problem, where audio segment boundaries are unknown. Specifically, we employ it to reclassify confusable segments produced by a dynamic programming scheme based on traditional GMMs. Such post-processing improves a fall detection accuracy metric by 5% relative.
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paper_title: Towards automatic analysis of social interaction patterns in a nursing home environment from video

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In this paper, we propose an ontology-based approach for analyzing social interaction patterns in a nursing home from video. Social interaction patterns are broken into individual activities and behavior events using a multi-level context hierarchy ontology framework. To take advantage of an ontology in representing how social interactions evolve, we design and refine the ontology based on knowledge gained from 80 hours of video recorded in the public spaces of a nursing home. The ontology is implemented using a dynamic Bayesian network to statistically model the multi-level concepts defined in the ontology. We have developed a prototype system to illustrate the proposed concept. Experiment results have demonstrated feasibility of the proposed approach. The objective of this research is to automatically create concise and comprehensive reports of activities and behaviors of patients to support physicians and caregivers in a nursing facility
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paper_title: Home health monitoring system in the sleep

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This paper describes a development of health monitoring system that uses a non-invasive type microphone based pressure sensor. The system can estimate the sleep stages from the heartbeats and body motion measured by the sensor. The algorithm for sleep stages estimations as implemented to the single chip microcomputer. The validity of the proposed system is confirmed by comparing the conventional sleep stage estimation results.
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paper_title: A wearable health care system based on knitted integrated sensors

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A comfortable health monitoring system named WEALTHY is presented. The system is based on a textile wearable interface implemented by integrating sensors, electrodes, and connections in fabric form, advanced signal processing techniques, and modern telecommunication systems. Sensors, electrodes and connections are realized with conductive and piezoresistive yarns. The sensorized knitted fabric is produced in a one step process. The purpose of this paper is to show the feasibility of a system based on fabric sensing elements. The capability of this system to acquire simultaneously several biomedical signals (i.e. electrocardiogram, respiration, activity) has been investigated and compared with a standard monitoring system. Furthermore, the paper presents two different methodologies for the acquisition of the respiratory signal with textile sensors. Results show that the information contained in the signals obtained by the integrated systems is comparable with that obtained by standard sensors. The proposed system is designed to monitor individuals affected by cardiovascular diseases, in particular during the rehabilitation phase. The system can also help professional workers who are subject to considerable physical and psychological stress and/or environmental and professional health risks.
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paper_title: Ambient intelligence and pervasive systems for the monitoring of citizens at cardiac risk: New solutions from the EPI-MEDICS project

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In western countries, heart disease is the main cause of premature death. Most of cardiac deaths occur out of hospital. Symptoms are often interpreted incorrectly. Victims do not survive long enough to benefit from in-hospital treatments. To reduce the time before treatment, the only useful diagnostic tool to assess the presence of a cardiac event is the electrocardiogram (ECG). Event and transtelephonic ECG recorders are used to improve decision-making but require setting up new infrastructures. The Pervasive solution proposed by the European EPI-MEDICS project is an intelligent Personal ECG Monitor for the early detection of cardiac events. It includes decision-making techniques, generates different alarm levels and forwards alarm messages to the relevant care providers by means of new generation wireless communication. It is cost saving, involving care provider only if necessary without specific infrastructure. Healthcare becomes personalized, wearable, ubiquitous.
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paper_title: Healthy Aims: Developing New Medical Implants and Diagnostic Equipment

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Healthy Aims is a 23- million, four-year project, funded under the EU's information society technology sixth framework program to develop intelligent medical implants and diagnostic systems (www.healthyaims.org). The project has 25 partners from 10 countries, including commercial, clinical, and research groups. This consortium represents a combination of disciplines to design and fabricate new medical devices and components as well as to test them in laboratories and subsequent clinical trials. The project focuses on medical implants for nerve stimulation and diagnostic equipment based on strain-gauge technology.
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paper_title: Sensorized environment for self-communication based on observation of daily human behavior

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This paper describes an intelligent environmental system, SELF (Sensorized Environment for Life), that enables a person to maintain his or her health through "self-communication." The system externalizes a "self" by storing personal data such as physiological status, analyzing it, and reporting useful information to assist the person, in maintaining his or her health. To establish the methodology to create such a system, this study analyzes and re-defines the self as an object of information processing. Self-communication is nothing but understanding ourselves objectively. It is not easy to put into practice because it requires a visualized or numerized representation of oneself. As an example of an externalized self, this study focused on the physiological status of a person. The physiological status is obtained as follows: SELF observes the person's behavior with distributed sensors invisibly embedded in the daily environment, extracts physiological parameters from it, analyzes the parameters, and accumulates the results. The accumulated results are used for reporting useful information to maintain the person's health. The authors constructed a model room for SELF consisting of a bed with pressure sensor array, a ceiling lighting dome with a microphone, and a washstand with a display. Fundamental results of self-externalization using SELF are presented.
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paper_title: Liverpool Telecare Pilot: telecare as an information tool

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The role of telecare systems is normally seen as identifying, and drawing attention to, situations of concern in the homes of service users. While this may currently be the primary reason for deploying such systems, the scope of telecare should not be limited to such an alarm generation role. The role of telecare in enhancing community-based care provision may be broadened by using similar, or identical, technology for providing relevant information to the carers of service users. In this paper we present a technical overview and discussion of an information provision approach to telecare which was trialled as one aspect of a pilot service in Liverpool, UK. The service used data collected by the telecare system to produce visual daily behavioural profiles and presented these to carers. The recipients for these profiles included social workers, occupational therapists and relatives of the service users. In this paper we discuss the visual profiles together with the benefits offered by such an information provision approach, including the perspective of a occupational therapist based in Liverpool.
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paper_title: A daily behavior enabled hidden Markov model for human behavior understanding

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This paper presents a Hierarchical Context Hidden Markov Model (HC-HMM) for behavior understanding from video streams in a nursing center. The proposed HC-HMM infers elderly behaviors through three contexts which are spatial, activities, and temporal context. By considering the hierarchical architecture, HC-HMM builds three modules composing the three components, reasoning in the primary and the secondary relationship. The spatial contexts are defined from the spatial structure, so that it is placed as the primary inference contexts. The temporal duration is associated to elderly activities, so activities are placed in the following of spatial contexts and the temporal duration is placed after activities. Between the spatial context reasoning and behavior reasoning of activities, a modified duration HMM is applied to extract activities. According to this design, human behaviors different in spatial contexts would be distinguished in first module. The behaviors different in activities would be determined in second module. The third module is to recognize behaviors involving different temporal duration. By this design, an abnormal signaling process corresponding to different situations is also placed for application. The developed approach has been applied for understanding of elder behaviors in a nursing center. Results have indicated the promise of the approach which can accurately interpret 85% of the elderly behaviors. For abnormal detection, the approach was found to have 90% accuracy, with 0% false alarm.
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paper_title: Portable Preimpact Fall Detector With Inertial Sensors

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Falls and the resulting hip fractures in the elderly are a major health and economic problem. The goal of this study was to investigate the feasibility of a portable preimpact fall detector in detecting impending falls before the body impacts on the ground. It was hypothesized that a single sensor with the appropriate kinematics measurements and detection algorithms, located near the body center of gravity, would be able to distinguish an in-progress and unrecoverable fall from nonfalling activities. The apparatus was tested in an array of daily nonfall activities of young (n = 10) and elderly (n = 14) subjects, and simulated fall activities of young subjects. A threshold detection method was used with the magnitude of inertial frame vertical velocity as the main variable to separate the nonfall and fall activities. The algorithm was able to detect all fall events at least 70 ms before the impact. With the threshold adapted to each individual subject, all falls were detected successfully, and no false alarms occurred. This portable preimpact fall detection apparatus will lead to the development of a new generation inflatable hip pad for preventing fall-related hip fractures.
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paper_title: Individualization, globalization and health - about sustainable information technologies and the aim of medical informatics

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This paper discusses aspects of information technologies for health care, in particular on transinstitutional health information systems (HIS) and on health-enabling technologies, with some consequences for the aim of medical informatics. It is argued that with the extended range of health information systems and the perspective of having adequate transinstitutional HIS architectures, a substantial contribution can be made to better patient-centered care, with possibilities ranging from regional, national to even global care. It is also argued that in applying health-enabling technologies, using ubiquitous, pervasive computing environments and ambient intelligence approaches, we can expect that in addition care will become more specific and tailored for the individual, and that we can achieve better personalized care. In developing health care systems towards transinstitutional HIS and health-enabling technologies, the aim of medical informatics, to contribute to the progress of the sciences and to high-quality, efficient, and affordable health care that does justice to the individual and to society, may be extended to also contributing to self-determined and self-sufficient (autonomous) life. Reference is made and examples are given from the Yearbook of Medical Informatics of the International Medical Informatics Association (IMIA) and from the work of Professor Jochen Moehr.
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paper_title: An eigenspace-based approach for human fall detection using Integrated Time Motion Image and Neural Network

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Falls are a major health hazard for the elderly and a serious obstacle for independent living. Since falling causes dramatic physical-psychological consequences, development of intelligent video surveillance systems is so important due to providing safe environments. To this end, this paper proposes a novel approach for human fall detection based on combination of integrated time motion images and eigenspace technique. Integrated time motion image (ITMI) is a type of spatio-temporal database that includes motion and time of motion occurrence. Applying eigenspace technique to ITMIs leads in extracting eigen-motion and finally MLP Neural Network is used for precise classification of motions and determination of a fall event. Unlike existent fall detection systems only deal with limited movement patterns, we considered wide range of motions consisting normal daily life activities, abnormal behaviors and also unusual events. Reliable recognition rate of experimental results underlines satisfactory performance of our system.
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paper_title: A Smart and Passive Floor-Vibration Based Fall Detector for Elderly

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Falls are very prevalent among the elderly. They are the second leading cause of unintentional-injury death for people of all ages and the leading cause of death for elders 79 years and older. Studies have shown that the medical outcome of a fall is largely dependent upon the response and rescue time. Hence, a highly accurate automatic fall detector is an important component of the living setting for older adult to expedite and improve the medical care provided to this population. Though there are several kinds of fall detectors currently available, they suffer from various drawbacks. Some of them are intrusive while others require the user to wear and activate the devices, and hence may fail in the event of user non-compliance. This paper describes the working principle and the design of a floor vibration-based fall detector that is completely passive and unobtrusive to the resident. The detector was designed to overcome some of the common drawbacks of the earlier fall detectors. The performance of the detector is evaluated by conducting controlled laboratory tests using anthropomorphic dummies. The results showed 100% fall detection rate with minimum potential for false alarms
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paper_title: A survey on fall detection: Principles and approaches

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Fall detection is a major challenge in the public health care domain, especially for the elderly, and reliable surveillance is a necessity to mitigate the effects of falls. The technology and products related to fall detection have always been in high demand within the security and the health-care industries. An effective fall detection system is required to provide urgent support and to significantly reduce the medical care costs associated with falls. In this paper, we give a comprehensive survey of different systems for fall detection and their underlying algorithms. Fall detection approaches are divided into three main categories: wearable device based, ambience device based and vision based. These approaches are summarised and compared with each other and a conclusion is derived with some discussions on possible future work.
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paper_title: AMON: a wearable multiparameter medical monitoring and alert system

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This paper describes an advanced care and alert portable telemedical monitor (AMON), a wearable medical monitoring and alert system targeting high-risk cardiac/respiratory patients. The system includes continuous collection and evaluation of multiple vital signs, intelligent multiparameter medical emergency detection, and a cellular connection to a medical center. By integrating the whole system in an unobtrusive, wrist-worn enclosure and applying aggressive low-power design techniques, continuous long-term monitoring can be performed without interfering with the patients' everyday activities and without restricting their mobility. In the first two and a half years of this EU IST sponsored project, the AMON consortium has designed, implemented, and tested the described wrist-worn device, a communication link, and a comprehensive medical center software package. The performance of the system has been validated by a medical study with a set of 33 subjects. The paper describes the main concepts behind the AMON system and presents details of the individual subsystems and solutions as well as the results of the medical validation.
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paper_title: Keeping the Resident in the Loop: Adapting the Smart Home to the User

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Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.
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paper_title: Behavioral Telemonitoring of the Elderly at Home: Detection of Nycthemeral Rhythms Drifts from Location Data

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Supporting ageing in place and staying at home, delaying institutionalization, lightening the caregivers' burden, improving the elderly quality of life are as many expectations that TeleHealthCare aims at coming up to. This paper proposes a method for Telemonitoring to detect abnormal changes in behavior which may lead to an early entrance in dependency. This method allows to detect and quantify a possible nycthemeral shift in daily routine. Such a disorder is common with elderly but in severe cases, it may be a marker of pathological behavior. Particularly, in individuals with Alzheimer disease, it appears to be an indicator for more rapid decline. In all the cases, the detection of a disruption in the activity circadian clock needs a follow-up visit. The method introduced is fast and cost-effective in computation. It measures the dissimilarity between sequences of activity using a variant of the Hamming distance traditionally used in information theory. Then results are interpreted according to the circular Gumbel distribution. This method is illustrated through a longitudinal study of the successive locations of an elderly woman within her own flat. In this preliminary work, the records were captured by passive infrared sensors placed in each room allowing only the detection of elementary activities of daily living. The method was tested by varying the timebox width of the study (i. e. the duration of the watched activities) and in a second time by distinguishing the day of the week. In both cases, it provides interesting insights into the behavior and the daily routine of the watched person as well as deviations from this routine. Important deviations will trigger alarms to alert the care providers. Diagnosing early abnormal behaviors is crucial for the person management and treatment effectiveness and consequently his/her maintaining at home.
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paper_title: Detection of Cognitive Injured Body Region Using Multiple Triaxial Accelerometers for Elderly Falling

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This paper aimed to use several triaxial acceleration sensor devices for joint sensing of injured body parts, when an accidental fall occurs. The model transmitted the information fed by the sensors distributed over various body parts to the computer through wireless transmission devices for further analysis and judgment, and employed cognitive adjustment method to adjust the acceleration range of various body parts in different movements. The model can determine the possible occurrence of fall accidents, when the acceleration significantly exceeds the usual acceleration range. In addition, after a fall accident occurs, the impact acceleration and normal (habitual) acceleration can be compared to determine the level of injury. This study also implemented a sensing system for analysis. The area of the body parts that may sustain greater impact force are marked red in this system, so that more information can be provided for medical personnel for more accurate judgment.
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paper_title: Mobile Human Airbag System for Fall Protection Using MEMS Sensors and Embedded SVM Classifier

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This paper introduces a mobile human airbag system designed for fall protection for the elderly. A Micro Inertial Measurement Unit ( muIMU) of 56 mm times 23 mm times 15 mm in size is built. This unit consists of three dimensional MEMS accelerometers, gyroscopes, a Bluetooth module and a Micro Controller Unit (MCU). It records human motion information, and, through the analysis of falls using a high-speed camera, a lateral fall can be determined by gyro threshold. A human motion database that includes falls and other normal motions (walking, running, etc.) is set up. Using a support vector machine (SVM) training process, we can classify falls and other normal motions successfully with a SVM filter. Based on the SVM filter, an embedded digital signal processing (DSP) system is developed for real-time fall detection. In addition, a smart mechanical airbag deployment system is finalized. The response time for the mechanical trigger is 0.133 s, which allows enough time for compressed air to be released before a person falls to the ground. The integrated system is tested and the feasibility of the airbag system for real-time fall protection is demonstrated.
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paper_title: SenseCam: A Retrospective Memory Aid

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This paper presents a novel ubiquitous computing device, the SenseCam, a sensor augmented wearable stills camera. SenseCam is designed to capture a digital record of the wearer's day, by recording a series of images and capturing a log of sensor data. We believe that reviewing this information will help the wearer recollect aspects of earlier experiences that have subsequently been forgotten, and thereby form a powerful retrospective memory aid. In this paper we review existing work on memory aids and conclude that there is scope for an improved device. We then report on the design of SenseCam in some detail for the first time. We explain the details of a first in-depth user study of this device, a 12-month clinical trial with a patient suffering from amnesia. The results of this initial evaluation are extremely promising; periodic review of images of events recorded by SenseCam results in significant recall of those events by the patient, which was previously impossible. We end the paper with a discussion of future work, including the application of SenseCam to a wider audience, such as those with neurodegenerative conditions such as Alzheimer's disease.
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paper_title: Autominder: an intelligent cognitive orthotic system for people with memory impairment

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The world’s population is aging at a phenomenal rate. Certain types of cognitive decline, in particular some forms of memory impairment, occur much more frequently in the elderly. This paper describes Autominder, a cognitive orthotic system intended to help older adults adapt to cognitive decline and continue the satisfactory performance of routine activities, thereby potentially enabling them to remain in their own homes longer. Autominder achieves this goal by providing adaptive, personalized reminders of (basic, instrumental, and extended) activities of daily living. Cognitive orthotic systems on the market today mainly provide alarms for prescribed activities at fixed times that are specified in advance. In contrast, Autominder uses a range of AI techniques to model an individual’s daily plans, observe and reason about the execution of those plans, and make decisions about whether and when it is most appropriate to issue reminders. Autominder is currently deployed on a mobile robot, and is being developed as part of the Initiative on Personal Robotic Assistants for the Elderly (the Nursebot project).
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paper_title: Interaction in pervasive computing settings using Bluetooth-enabled active tags and passive RFID technology together with mobile phones

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Passive RFID technology and unobtrusive Bluetooth-enabled active tags are means to augment products and everyday objects with information technology invisible to human users. This paper analyzes general interaction patterns in such pervasive computing settings where information about the user's context is derived by a combination of active and passive tags present in the user's environment. The concept of invisible preselection of interaction partners based on the user's context is introduced It enables unobtrusive interaction with smart objects in that it combines different forms of association, e.g. implicit and user initiated association, by transferring interaction stubs to mobile devices based on the user's current situation. Invisible preselection can also be used for remote interaction. By assigning phone numbers to smart objects, we propose making this remote user interaction with everyday items as easy as making a phone call. We evaluate the suitability of the proposed concepts on the basis of three concrete examples: a product monitoring system, a smart medicine cabinet, and a remote interaction application.
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paper_title: Blind Path Identification System Design Base on RFID

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Blind path identification with the help of RFID technology not only makes the blind can gain feelings of orientation during walking, but also has the knowledge of the exactly location of the street. The main idea is that when a blind man walking on the path which has electronic tags pre-built under the tile of the blind path, those tags activated by radio wave came from the RFID reader sent their identity codes transmitted by the reader to the computer, and after the query of the database, the number of the street, the name of the shop of the current location of the blind will be known immediately, meanwhile, the corresponding voice data will be sent via earphones to the blind in order to get the precise identification. The system is composed of the hardware and software. The hardware includes the electronic tags, reader, MCU, wireless earphone and GSM circuit. The software is consisted of the database that controls the read-write of the tags, the management of the street info as well as the sending of the voice data. Experimental result shows that the system almost can achieve the proposed objective and provide the novel technology support for the blind path construction.
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paper_title: Opportunity Knocks: A System to Provide Cognitive Assistance with Transportation Services

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We present an automated transportation routing system, called “Opportunity Knocks,” whose goal is to improve the efficiency, safety and independence of individuals with mild cognitive disabilities. Our system is implemented on a combination of a Bluetooth sensor beacon that broadcasts GPS data, a GPRS-enabled cell-phone, and remote activity inference software. The system uses a novel inference engine that does not require users to explicitly provide information about the start or ending points of their journeys; instead this information is learned from users’ past behavior. Futhermore, we demonstrate how route errors can be detected and how the system helps to correct the errors with real-time transit information. In addition we present a novel solution to the problem of labeling positions with place names.
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paper_title: The next generation of mobile medication management solutions

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In this paper, we describe the development of an internet-based system and a novel mobile home based device for the management of medication. We extend these concepts through the descriptions of an enhanced service with the use of mobile phone technology and home based digital TV services.
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paper_title: ALARM-NET: Wireless sensor networks for assisted-living and residential monitoring

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We describe ALARM-NET, a wireless sensor network for assisted-living and residential monitoring. It integrates environmental and physiological sensors in a scalable, heterogeneous architecture. A query protocol allows real-time collection and processing of sensor data by user interfaces and back-end analysis programs. One such program determines circadian activity rhythms of residents, feeding activity information back into the sensor network to aid context-aware power management, dynamic privacy policies, and data association. Communication is secured end-to-end to protect sensitive medical and operational information. The ALARM-NET system has been implemented as a network of MICAz sensors, stargate gateways, iPAQ PDAs, and PCs. Customized infrared motion and dust sensors, and integrated temperature, light, pulse, and blood oxygenation sensors are present. Software components include: TinyOS query processor and security modules for motes; AlarmGate, an embedded Java application for managing power, privacy, security, queries, and client connections; Java resident monitoring and sensor data querying applications for PDAs and PCs; and a circadian activity rhythm analysis program. We show the correctness, robustness, and extensibility of the system architecture through a scenario-based evaluation of the integrated ALARM-NET system, as well as performance data for individual software components.
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paper_title: Orange alerts: Lessons from an outdoor case study

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Ambient Assisted Living (AAL) is of particular relevance to those who may suffer from Alzheimer's Disease or dementia, and, of course, their carers. The slow but progressive nature of the disease, together with its neurological nature, ultimately compromises the behavior and function of people who may be essentially healthy from a physical perspective. An illustration of this is the wandering behavior frequently found in people with dementia. In this paper, a novel AAL solution for caregivers, particularly tailored for Alzheimer's patients who are the early stage of the disease and exhibit unpredictable wandering behavior, is briefly described. Salient aspects of a user evaluation are presented, and some issues relevant to the practical design of AAL systems in dementia cases are identified.
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paper_title: The use of computer vision in an intelligent environment to support aging-in-place, safety, and independence in the home

paper_content:

This paper discusses the use of computer vision in pervasive healthcare systems, specifically in the design of a sensing agent for an intelligent environment that assists older adults with dementia during an activity of daily living. An overview of the techniques applied in this particular example is provided, along with results from preliminary trials completed using the new sensing agent. A discussion of the results obtained to date is presented, including technical and social issues that remain for the advancement and acceptance of this type of technology within pervasive healthcare.
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paper_title: Magic Medicine Cabinet: A Situated Portal for Consumer Healthcare

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In this paper, we introduce a smart appliance for consumer healthcare called "Magic Medicine Cabinet." It integrates such technologies like smart labels, face recognition, health monitoring devices, flat panel displays, and the Web to provide situated support for a broad range of health needs, including condition monitoring, medication reminders, interactions with one's own pharmacists and physicians, as well as access to personalized health information.
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paper_title: The myheart project - Fighting cardiovascular diseases by prevention and early diagnosis

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MyHeart is a so-called Integrated Project of the European Union aiming to develop intelligent systems for the prevention and monitoring of cardiovascular diseases. The project develops smart electronic and textile systems and appropriate services that empower the users to take control of their own health status. I. INTRODUCTION ARDIOVASCULAR diseases (CVD) are the leading cause of death in developed countries. Roughly 45% of all deaths in the EU are due to cardio-vascular diseases. More than 20% of all European citizens suffer from a chronic cardio-vascular disease. Europe spends annually hundred billion Euros on CVD. With the ageing population, it is a challenge for Europe to provide its citizens with healthcare at affordable costs. It is the aim of the MyHeart project to fight CVD by prevention and early diagnosis. A healthy and preventive lifestyle as well as early diagnosis of heart diseases could save millions of life years annually, reduce the morbidity significantly and, simultaneously, improve the quality of life of the European citizen. Prevention offers the opportunity to systematically fight the origin of cardio- vascular diseases as well as to improve the medical outcome after an event. Prevention is therefore believed to be the solution for improving the quality of care for cardio- vascular diseases. Classical medical institutions offer only intermittent, episodical treatment, while prevention asks for a lifelong continuous change of habits and therefore for a continuous health-care delivery process. The institutional points of care cannot provide preventive healthcare in a cost-effective manner due to their inherent cost structure. Novel methods are needed that provide continuous and ubiquitous access to medical excellence in a cost-effective way. The approach of the MyHeart project is to monitor Vital Body Signs (VBS) with wearable technology, to process the measured data and to give (therapy) recommendations
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paper_title: A tool to promote prolonged engagement in art therapy: design and development from arts therapist requirements

paper_content:

This paper describes the development of a tool that assists arts therapists working with older adults with dementia. Participation in creative activities is becoming accepted as a method for improving quality of life. This paper presents the design of a novel tool to increase the capacity of creative arts therapists to engage cognitively impaired older adults in creative activities. The tool is a creative arts touch-screen interface that presents a user with activities such as painting, drawing, or collage. It was developed with a user-centered design methodology in collaboration with a group of creative arts therapists. The tool is customizable by therapists, allowing them to design and build personalized therapeutic/goal-oriented creative activities for each client. In this paper, we evaluate the acceptability of the tool by arts therapists (our primary user group). We perform this evaluation qualitatively with a set of one-on-one interviews with arts therapists who work specifically with persons with dementia. We show how their responses during interviews support the idea of a customizable assistance tool. We evaluate the tool in simulation by showing a number of examples, and by demonstrating customizable components.
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paper_title: Towards evolutionary ambient assisted living systems

paper_content:

Ambient assisted living (AAL) is advocated as technological solutions that will enable the elderly population maintain their independence for a longer time than would otherwise be the case. Though the facts motivating the need for AAL are indisputable, the inherently heterogeneous nature and requirements of the elderly population raise significant difficulties. One particular challenge is that of designing AAL systems that can evolve to meet the requirements of individuals as their needs and circumstances change. This demands the availability of an adaptive, open, scalable software platform that incorporates a select combination of autonomic and intelligent techniques. Given that the first generation of AAL systems will be deployed in the near future, it is incumbent on designers to factor this need for evolution and adaptivity in their designs and implementations. Thus this paper explores AAL from a number of prospective and considers an agent-based middleware approach to realising an architecture for evolutionary AAL.
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paper_title: SWAN: System for Wearable Audio Navigation

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Wearable computers can certainly support audio-only presentation of information; a visual interface need not be present for effective user interaction. A system for wearable audio navigation (SWAN) is being developed to serve as a navigation and orientation aid for persons temporarily or permanently visually impaired. SWAN is a wearable computer consisting of audio-only output and tactile input via a handheld interface. SWAN aids a user in safe pedestrian navigation and includes the ability for the user to author new GIS data relevant to their needs of wayfinding, obstacle avoidance, and situational awareness support. Emphasis is placed on representing pertinent data with non-speech sounds through a process of sonification. SWAN relies on a geographic information system (GIS) infrastructure for supporting geocoding and spatialization of data. Furthermore, SWAN utilizes novel tracking technology.
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paper_title: Wireless Health Care Service System for Elderly With Dementia

paper_content:

The purpose of this paper is to integrate the technologies of radio frequency identification, global positioning system, global system for mobile communications, and geographic information system (GIS) to construct a stray prevention system for elderly persons suffering from dementia without interfering with their activities of daily livings. We also aim to improve the passive and manpowered way of searching the missing patient with the help of the information technology. Our system provides four monitoring schemes, including indoor residence monitoring, outdoor activity area monitoring, emergency rescue, and remote monitoring modes, and we have developed a service platform to implement these monitoring schemes. The platform consists of a web service server, a database server, a message controller server, and a health-GIS (H-GIS) server. Family members or volunteer workers can identify the real-time positions of missing elderly using mobile phone, PDA, Notebook PC, and various mobile devices through the service platform. System performance and reliability is analyzed. Experiments performed on four different time slots, from three locations, through three mobile telecommunication companies show that the overall transaction time is 34 s and the average deviation of the geographical location is about 8 m. A questionnaire surveyed by 11 users show that eight users are satisfied with the system stability and 10 users would like to carry the locating device themselves, or recommend it to their family members
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paper_title: A Blind Navigation System Using RFID for Indoor Environments

paper_content:

A location and tracking system becomes very important to our future world of pervasive computing, where information is all around us. Location is one of the most needed information for emerging and future applications. Since the public use of GPS satellite is allowed, several state-of-the-art devices become part of our life, e.g. a car navigator and a mobile phone with a built-in GPS receiver. However, location information for indoor environments is still very limited as mentioned in paper. Several techniques are proposed to get location information in buildings such as using a radio signal triangulation. Using Radio Frequency Identification (RFID) tags is a new way of giving location information to users. Due to its passive communication circuit, RFID tags can be embedded almost anywhere without an energy source. The tags stores location information and give it to any reader that is within a proximity range which can be up to 7-10 centimeters. In this paper RFID-based system for navigation in a building for blind people or visually impaired
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paper_title: iMAT: Intelligent medication administration tools

paper_content:

iMAT is a system of automatic medication dispensers and software tools. It is for people who take medications on long term basis at home to stay well and independent. The system helps its users to improve rigor in compliance by preventing misunderstanding of medication directions and making medication schedules more tolerant to tardiness and negligence. This paper presents an overview of the assumptions, models, architecture and implementation of the system.
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paper_title: CAALYX: a new generation of location-based services in healthcare

paper_content:

Recent advances in mobile positioning systems and telecommunications are providing the technology needed for the development of location-aware tele-care applications. This paper introduces CAALYX – Complete Ambient Assisted Living Experiment, an EU-funded project that aims at increasing older people's autonomy and self-confidence by developing a wearable light device capable of measuring specific vital signs of the elderly, detecting falls and location, and communicating automatically in real-time with his/her care provider in case of an emergency, wherever the older person happens to be, at home or outside.
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paper_title: Wireless Medication Management System: Design and performance evaluation

paper_content:

To keep healthcare costs under control, a high-level of medication adherence, or compliance with medication regimen, must be achieved. In this paper, we show how wireless technologies can be used to improve medication adherence. More specifically, we present the design, operation, and evaluation of a wireless medication system, termed Smart Medication Management System (SMMS). We also present multiple metrics for quality of service and an analytical model for evaluating the medication adherence. The performance results show that very high medication adherence is achievable by SMMS for single and multiple medications even for patients with mild cognitive deficiency. The performance of context-aware reminders as implemented by SMMS is found to be most effective as compared to other interventions. Several powerful "composite" interventions are also proposed and evaluated for medication adherence. The results also show that with increasing hospitalization cost the healthcare savings due to improved medication adherence become even more significant. The proposed work forms the basis to provide personalized interventions to patients for improving medication adherence in multiple surroundings using wireless technologies.
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paper_title: SAPHIRE: A Multi-Agent System for Remote Healthcare Monitoring through Computerized Clinical Guidelines

paper_content:

Due to increasing percentage of graying population and patients with chronic diseases, the world is facing serious problems for serving high quality healthcare services to citizens at a reasonable costs. In this paper, we are providing a Clininical Desicion Support system for remote monitoring of patients at their homes, and at the hospital to decrease the load of medical practitioners and also healthcare costs. As the expert knowledge required to build the clinical decision support system, Clinical Guidelines are exploited. Examining the reasons of failure for adoption of clinical guidelines by healthcare institutes, we have realized that necessary measures should be taken in order to establish a semantic interoperability environment to be able to communicate with various heterogenous clinical systems. In this paper these requirements are detailed and a semantic infrastructure to enable easy deployment and execution of clinical guidelines in heterogenous healthcare enviroments is presented. Due to the nature of the problem which necessitates having many autonomous entities dealing with heterogenous distributed resources, we have built the system as a Multi-Agent System. The architecture described in this paper is realized within the scope of IST-27074 SAPHIRE project.
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paper_title: Context-Aware wireless sensor networks for assisted-living and residential monitoring

paper_content:

Improving the quality of healthcare and the prospects of "aging in place" using wireless sensor technology requires solving difficult problems in scale, energy management, data access, security, and privacy. We present AlarmNet, a novel system for assisted living and residential monitoring that uses a two-way flow of data and analysis between the front- and back-ends to enable context-aware protocols that are tailored to residents' individual patterns of living. AlarmNet integrates environmental, physiological, and activity sensors in a scalable heterogeneous architecture. The SenQ query protocol provides real-time access to data and lightweight in-network processing. Circadian activity rhythm analysis learns resident activity patterns and feeds them back into the network to aid context-aware power management and dynamic privacy policies.
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paper_title: TigerPlace: an innovative educational and research environment

paper_content:

This item also falls under AAAI copyright. For more information, please visit http://www.aaai.org/ojs/index.php/aimagazine/index
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paper_title: Telemonitoring and telerehabilitation of patients with Parkinson's disease: health technology assessment of a novel wearable step counter.

paper_content:

Step counting is an important index of motion in telemonitoring. One of the most diffused wearable systems, designed for this purpose, is the pedometer. The accuracy of commercial pedometers has been reported in the literature. Several limits have been found in many commercial systems both in healthy subjects and in people with disabilities. Furthermore, commercial pedometers lack interoperability. This paper introduces a new wearable system for step counting for telemonitoring applications. This system is based on a wearable device with a force-sensing resistor. It is affixed on the gastrocnemius muscle for monitoring muscular expansion correlated with the gait. The data exchange is assured by the XTR-434H (Aurel, FC, Italy) telemetric system. The proposed gastrocnemius expansion measurement unit (GEMU) was tested on 5 subjects with Parkinsons disease at Level 3 of the Tinetti test of unbalance. Ten repetitions of 500 steps with three different speeds (fast, slow, and normal) were performed. The mean error was <0.5%. Results also showed that GEMU performed better than an accelerometer unit (considered in the literature the best solution for this disability). The study showed that GEMU had a high performance in subjects with Parkinsons disease, causing a high degree of unbalance that confounded motion style. The next phase will be the optimization of GEMU for long-term medical applications at the patients home.
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paper_title: A sensor-enhanced health information system to support automatically controlled exercise training of COPD patients

paper_content:

For an improvement of the quality of life for patients suffering from chronic obstructive pulmonary disease (COPD) we developed a concept and prototype of a sensor-enhanced health information system. This system includes a component that is monitoring the rehabilitation training and automatically controls the target load for the exercise on the basis of his or her vital data. The system also detects potentially critical health states and communicates alarms to external users. The component interacts with a personal electronic health record (PHR) that provides additional health related information for the decision making process, as feedback to the user and as an opportunity for physicians to optimize the users exercise plan. The PHR uses current medical informatics standards to store and transmit training data to health care professionals and to provide a maximum of interoperability with their information systems. We have integrated these components in a service oriented platform design that is located in the home environment of the user.
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paper_title: Sensor networks for medical care

paper_content:

Sensor networks have the potential to greatly impact many aspects of medical care. By outfitting patients with wireless, wearable vital sign sensors, collecting detailed real-time data on physiological status can be greatly simplified. However, there is a significant gap between existing sensor network systems and the needs of medical care. In particular, medical sensor networks must support multicast routing topologies, node mobility, a wide range of data rates and high degrees of reliability, and security. This paper describes our experiences with developing a combined hardware and software platform for medical sensor networks, called CodeBlue. CodeBlue provides protocols for device discovery and publish/subscribe multihop routing, as well as a simple query interface that is tailored for medical monitoring. We have developed several medical sensors based on the popular MicaZ and Telos mote designs, including a pulse oximeter, EKG and motion-activity sensor. We also describe a new, miniaturized sensor mote designed for medical use. We present initial results for the CodeBlue prototype demonstrating the integration of our medical sensors with the publish/subscribe routing substrate. We have experimentally validated the prototype on our 30-node sensor network testbed, demonstrating its scalability and robustness as the number of simultaneous queries, data rates, and transmitting sensors are varied. We also study the effect of node mobility, fairness across multiple simultaneous paths, and patterns of packet loss, confirming the system’s ability to maintain stable routes despite variations in node location and
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paper_title: ECG telemonitoring during home-based cardiac rehabilitation in heart failure patients

paper_content:

We assessed ECGs recorded during home-based telemonitored cardiac rehabilitation (HTCR) in stable patients with heart-failure. The study included 75 patients with heart failure (NYHA II, III), with a mean age of 56 years. They participated in an eight-week programme of home cardiac rehabilitation which was telemonitored with a device which recorded 16-s fragments of their ECG. These fragments were transmitted via mobile phone to a monitoring centre. The times of the automatic ECG recordings were pre-set and coordinated with the cardiac rehabilitation. Patients were able to make additional recordings when they felt unwell using a tele-event-Holter ECG facility. During the study, 5757 HTCR sessions were recorded and 11,534 transmitted ECG fragments were evaluated. Most ECGs originated from the automatic recordings. Singular supraventricular and ventricular premature beats and ventricular couplets were detected in 16%, 69% and 16% of patients, respectively. Twenty ECGs were recorded when patients felt unwell: non sustained ventricular tachycardia occurred in three patients and paroxysmal atrial fibrillation episode in two patients. Heart failure patients undergoing HTCR did not develop any arrhythmia which required a change of the procedure, confirming it was safe. Cardiac rehabilitation at home was improved by utilizing the tele-event-Holter ECG facility.
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paper_title: Wearable Assistant for Parkinson’s Disease Patients With the Freezing of Gait Symptom

paper_content:

In this paper, we present a wearable assistant for Parkinson's disease (PD) patients with the freezing of gait (FOG) symptom. This wearable system uses on-body acceleration sensors to measure the patients' movements. It automatically detects FOG by analyzing frequency components inherent in these movements. When FOG is detected, the assistant provides a rhythmic auditory signal that stimulates the patient to resume walking. Ten PD patients tested the system while performing several walking tasks in the laboratory. More than 8 h of data were recorded. Eight patients experienced FOG during the study, and 237 FOG events were identified by professional physiotherapists in a post hoc video analysis. Our wearable assistant was able to provide online assistive feedback for PD patients when they experienced FOG. The system detected FOG events online with a sensitivity of 73.1% and a specificity of 81.6%. The majority of patients indicated that the context-aware automatic cueing was beneficial to them. Finally, we characterize the system performance with respect to the walking style, the sensor placement, and the dominant algorithm parameters.
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paper_title: Ubiquitous Rehabilitation Center: An Implementation of a Wireless Sensor Network Based Rehabilitation Management System

paper_content:

This paper documents the implementation of a system, the ubiquitous rehabilitation center, which integrates a Zigbee-based wireless network with sensors that monitor patients and rehabilitation machines. These sensors interface with Zigbee motes which in turn interface with a server application that manages all aspects of the rehabilitation center and allows rehabilitation specialists to assign prescriptions to patients. Patients carry out prescriptions while the system monitors and collects all pertinent session data, storing it in a database. The rehabilitation specialist is then able to use trend-based analysis techniques on collected data in order to evaluate a patient's condition. Specialists then assign further prescriptions based on this evaluation. Consequently patients are treated more effectively while potentially spending less time in rehabilitation. This paper demonstrates how the ubiquitous rehabilitation center improves on the traditional rehabilitation center by highlighting the differences in rehabilitation methods using the two systems in an ACL rehabilitation test case.
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paper_title: BASE - An interactive technology solution to deliver balance and strength exercises to older adults

paper_content:

There is a high prevalence of falls in older adults. It has been recognised that a highly challenging balance and strength retraining program can reduce the incidence of falls significantly. This paper describes the design and initial evaluation of a home-based interactive technology solution to deliver a personalised, physiotherapist prescribed exercise program to older adults. We adopted a user centred design process to ensure such technology is easy to use, acting as a facilitator to completing the exercise program, rather than an inhibitor. Initial usability findings, in addition to participant attitudes towards such a system, are outlined.
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paper_title: Ambient Intelligence: The Evolution of Technology, Communication and Cognition Towards the Future of Human-Computer Interaction

paper_content:

The metaphor of Ambient Intelligence (AmI) tries to picture a vision of the future where all of us will be surrounded by 'intelligent' electronic environments, and this ambient has claims to being sensitive and responsive to our needs. Ambient Intelligence without invasion of privacy represents a long-term vision for the EU Information Society Technologies Research programme. A strong multi-disciplinary and collaborative approach is a key requirement for large-scale technology innovation and the development of effective applications. Up to now, most of the books and papers related to AmI focus their analysis on the technology potential only. An important feature of this volume is the link between the technology - through the concepts of ubiquitous computing and intelligent interface - and the human experience of interacting in the world - through a neuro-psychological vision centred on the concept of 'presence'. Presence - the sense of being there - is the experience of projecting one's mind through media to other places, people and designed environments.The combination of recent discoveries in cognitive neuroscience - which make it possible to acquire a better understanding of the human aspects of presence, and the breakthroughs at the level of the enabling technologies make it increasingly possible to build novel systems based on this understanding. The goal of this volume is to assess the technologies and processes that are behind the AmI vision, in order to help the development of state-of-the-art applications. More in detail, this volume aims at supporting researchers and scientists, interested in the understanding and exploiting the potential of AmI.
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paper_title: A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation

paper_content:

Background ::: Recent technological advances in integrated circuits, wireless communications, and physiological sensing allow miniature, lightweight, ultra-low power, intelligent monitoring devices. A number of these devices can be integrated into a Wireless Body Area Network (WBAN), a new enabling technology for health monitoring.
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paper_title: Investigation of gait features for stability and risk identification in elders

paper_content:

Today, eldercare demands a greater degree of versatility in healthcare. Automatic monitoring devices and sensors are under development to help senior citizens achieve greater autonomy, and, as situations arise, alert healthcare providers. In this paper, we study gait patterns based on extracted silhouettes from image sequences. Three features are investigated through two different image capture perspectives: shoulder level, spinal incline, and silhouette centroid. Through the evaluation of fourteen image sequences representing a range of healthy to frail gait styles, features are extracted and compared to validation results using a Vicon motion capture system. The results obtained show promise for future studies that can increase both the accuracy of feature extraction and pragmatism of machine monitoring for at-risk elders.
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paper_title: Brain-Operated Assistive Devices: the ASPICE Project

paper_content:

The ASPICE project aims at the development of a system which allows the neuromotor disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system pivots around a software controller running on a personal computer, which offers to the user a proper interface to communicate through input interfaces matched with the individual's residual abilities. The system uses the user's input to control domotic devices - such as remotely controlled lights, TV sets, etc. - and a Sony AIBO robot. At this time, the system is under clinical validation, that will provide assessment through patients' feedback and guidelines for customized system installation
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paper_title: The DAT project: a smart home environment for people with disabilities

paper_content:

The DAT project is a research initiative that aims at building up a smart home environment where people with disabilities can improve their abilities to cope with daily life activities by means of technologically advanced home automation solutions. The project has a threefold purpose. The smart home will be used as a physical setting, where clients with disabilities can follow individual programs aimed at improving their independence in the home environment. The smart house will also be used as a demonstration an educational laboratory where anybody interested can get knowledge of the latest advancements in the field of home automation and tele-care. Finally, the smart home will be used as research laboratory for testing and developing new clinical protocols and innovative solutions in the field of environmental control and home care. This article describes the architecture of the smart home, the design of the home automation system, and the research programs associated with the DAT project
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paper_title: A Pervasive Visual–Haptic Framework for Virtual Delivery Training

paper_content:

Thanks to the advances of voltage regulator (VR) technologies and haptic systems, virtual simulators are increasingly becoming a viable alternative to physical simulators in medicine and surgery, though many challenges still remain. In this study, a pervasive visual-haptic framework aimed to the training of obstetricians and midwives to vaginal delivery is described. The haptic feedback is provided by means of two hand-based haptic devices able to reproduce force-feedbacks on fingers and arms, thus enabling a much more realistic manipulation respect to stilus-based solutions. The interactive simulation is not solely driven by an approximated model of complex forces and physical constraints but, instead, is approached by a formal modeling of the whole labor and of the assistance/intervention procedures performed by means of a timed automata network and applied to a parametrical 3-D model of the anatomy, able to mimic a wide range of configurations. This novel methodology is able to represent not only the sequence of the main events associated to either a spontaneous or to an operative childbirth process, but also to help in validating the manual intervention as the actions performed by the user during the simulation are evaluated according to established medical guidelines. A discussion on the first results as well as on the challenges still unaddressed is included.
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paper_title: Advanced robotic residence for the elderly/the handicapped: realization and user evaluation

paper_content:

A novel advanced robotic residence, Intelligent Sweet Home (ISH), is developed at KAIST, Korea for testing advanced concepts for independent living of the elderly and the physically handicapped. The work focuses on human-friendly technical solutions for motion/mobility assistance, health monitoring, and advanced human-machine interfaces that provide easy control of both assistive devices and home-installed appliances. To improve the inhabitant's comfort, an intelligent bed, intelligent wheelchair and mechatronic transfer robot were developed. And, various interfaces based on hand gestures and voice, and health monitoring system were studied. This paper emphasizes the realization scheme of Intelligent Sweet Home and user evaluation by a physically handicapped person.
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paper_title: Robotic smart house to assist people with movement disabilities

paper_content:

This paper introduces a new robotic smart house, Intelligent Sweet Home, developed at KAIST in Korea, which is based on several robotic agents and aims at testing advanced concepts for independent living of the elderly and people with disabilities. The work focuses on technical solutions for human-friendly assistance in motion/mobility and advanced human-machine interfaces that provide simple control of all assistive robotic systems and home-installed appliances. The smart house concept includes an intelligent bed, intelligent wheelchair, and robotic hoist for effortless transfer of the user between bed and wheelchair. The design solutions comply with most of the users' requirements and suggestions collected by a special questionnaire survey of people with disabilities. The smart house responds to the user's commands as well as to the recognized intentions of the user. Various interfaces, based on hand gestures, voice, body movement, and posture, have been studied and tested. The paper describes the overall system structure and explains the design and functionality of some main system components.
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paper_title: perCues: Trails of Persuasion for Ambient Intelligence

paper_content:

The realization of the ambient intelligence (AmI) vision will have a profound impact on our everyday lives and society. AmI applied in contexts like homes or public spaces will not only affect individual users but influence entire groups of users. The question is how we can apply such technologies to persuade groups and individual users. Our approach is to design AmI environments by borrowing a concept which works very well in biological and social systems: Collective Intelligence (CI). The intelligence of a group surpasses the individual intelligences and leads to improved problem solving capabilities of individuals and groups. From nature we borrow examples of cues in the environment to stimulate goal directed collective intelligence (perCues). The application of perCues in AmI environments helps to persuade users to reach a common goal like decreasing environmental pollution. Adopting CI for AmI we blaze a trail for the design of persuasive AmI environments.
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paper_title: International survey on the Dance Dance Revolution game

paper_content:

Despite the growing popularity of physically interactive game-playing, no user studies have been conducted on dance gaming (one of the most popular forms of playing via full-body movements). An online questionnaire was used to study various factors related to Dance Dance Revolution (DDR) gaming. In total, 556 respondents from 22 countries of ages 12 to 50 filled in a questionnaire which examined the players' gaming background, playing styles and skills, motivational and user experience factors, social issues, and physical effects of dance gaming, and taking part in dance-gaming related activities. The results show that playing DDR has a positive effect on the social life and physical health of players, as it improves endurance, muscle strength and sense of rhythm, and creates a setting where new friends can be found.
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paper_title: perFrames: Persuasive Picture Frames for Proper Posture

paper_content:

Poor sitting habits and bad sitting posture are often the cause for musculoskeletal disorders like back pain. Also office employees are affected, because they carry out the majority of their work sitting in front of computers. Therefore we aim at sensitizing and motivating office employees regarding preventive healthcare. We have developed a persuasive interface in form of an interactive picture frame which integrates unobtrusively into the working environment --- the perFrame. This frame contains a moving portrait of a person the employee loves or likes. It provides affective feedback in order to persuade employees for better sitting habits while working with a computer. We conducted a preliminary in-situ study, deploying these picture frames on the desktops of eight office employees. The results highlight the employees' acceptance of our application as well as its potential to foster awareness and achieve persuasion regarding healthy behavior in the office.
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paper_title: The Intelligent e-Therapy System: A New Paradigm for Telepsychology and Cybertherapy.

paper_content:

ABSTRACT One of the main drawbacks of computer-assisted psychology tools developed up to now is related to the real time customisation and adaptation of the content to each patient depending on his/her activity. In this paper we propose a new approach for mental e-health treatments named Intelligent e-Therapy (eIT) with capabilities for ambient intelligence and ubiquitous computing. From a technical point of view, an eIT system is based on four fundamental axes: ambient intelligence for capturing physiological, psychological and contextual information of the patient; persuasive computing for changing/reinforcing behaviours; ubiquitous computing for using the system at any place, and at any time; and multiple technological platforms support.
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paper_title: Ambient Intelligence and Persuasive Technology: The Blurring Boundaries Between Human and Technology

paper_content:

The currently developing fields of Ambient Intelligence and Persuasive Technology bring about a convergence of information technology and cognitive science. Smart environments that are able to respond intelligently to what we do and that even aim to influence our behaviour challenge the basic frameworks we commonly use for understanding the relations and role divisions between human beings and technological artifacts. After discussing the promises and threats of these technologies, this article develops alternative conceptions of agency, freedom, and responsibility that make it possible to better understand and assess the social roles of Ambient Intelligence and Persuasive Technology. The central claim of the article is that these new technologies urge us to blur the boundaries between humans and technologies also at the level of our conceptual and moral frameworks.
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paper_title: Designing motivation using persuasive ambient mirrors

paper_content:

In this article, we describe four case studies of ubiquitous persuasive technologies that support behavior change through personalized feedback reflecting a user's current behavior or attitude. The first case study is Persuasive Art, reflecting the current status of a user's physical exercise in artistic images. The second system, Virtual Aquarium, reflects a user's toothbrushing behavior in a Virtual Aquarium. The third system, Mona Lisa Bookshelf, reflects the situation of a shared bookshelf on a Mona Lisa painting. The last case study is EcoIsland, reflecting cooperative efforts toward reducing CO2 emissions as a set of virtual islands shared by a neighborhood. Drawing from the experience of designing and evaluating these systems, we present guidelines for the design of persuasive ambient mirrors: systems that use visual feedback to effect changes in users' everyday living patterns. In particular, we feature findings in choosing incentive systems, designing emotionally engaging feedback, timing feedback, and persuasive interaction design. Implications for current design efforts as well as for future research directions are discussed.
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paper_title: Persuasion in ambient intelligence

paper_content:

Although the field of persuasive technologies has lately attracted a lot of attention, only recently the notion of ambient persuasive technologies was introduced. Ambient persuasive technologies can be integrated into every aspect of life, and as such have greater persuasive power than the traditional box like machines. This article discusses ambient persuasion and poses a model that structures the knowledge from social sciences on persuasion, attitude change, and behavior change. Using this model the challenges of ambient persuasive technologies to fulfill its persuasive promises are identified. From the ambient persuasion model it is clear that ambient persuasive technologies can go beyond traditional persuasive technologies by being context and situational aware, by interpreting individual differences between users, and by being a social actor in their own right.
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paper_title: Motivating People in Smart Environments

paper_content:

In this paper we discuss the possibility to extend PORTIA, a persuasion system currently applied in human-agent dialogs, to support ambient persuasion. We have identified a fitness center as an appropriate smart environment in which ambient persuasion strategies can be applied. According to the Ubiquitous Computing vision, in the fitness center the user is surrounded by several connected devices that cooperate in the persuasion process, each of them with the most appropriate strategy, mode of persuasion, style of communication according to the context. To this aim we propose a multi-agent system able to support this distributed and intelligent approach to persuasion that allows to follow the user during the gradual change from the initial attitude to sustain of long term behaviours.
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paper_title: Objectively Monitoring Wellbeing through Pervasive Technology

paper_content:

Wellbeing is an underlying theme in many local and national policies and procedures outlined by governments and health care services. In recent years a person’s wellbeing has been largely monitored through the use of subjective rating scales or other retrospective interview methods. This position paper considers how technology can help to monitor wellbeing more objectively and within the individual’s naturalistic environment. For this purpose, we introduce and discuss the design of the Wearable Acoustic Monitor (WAM). The WAM provides support in monitoring aspects of social and emotional wellbeing through the provision of information about a person’s level of social interaction and vocal features of emotionality. We further reflect on the ethical and privacy issues that are crucial for the design of digital devices capturing audio data to explore wellbeing.
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paper_title: AffectAura: Emotional wellbeing reflection system

paper_content:

Emotional health is of huge importance to our quality of life. However, monitoring emotional wellbeing is challenging. AffectAura is an emotional prosthetic that allows users to reflect on their emotional states over long periods of time. The system continuously predicts user's valence, arousal and engagement based on information gathered from a multimodal sensor setup. The interface combines these predictions with rich contextual information and allows the user to explore the data. AffectAura has been validated on 100's of hours of data recorded from multiple people who found the system allowed them to reason forward and backward in time about their emotional experiences. This project illustrates the first longitudinally evaluated emotional memory system.
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paper_title: Computer-mediated emotional regulation: Detection of emotional changes using non-parametric cumulative sum

paper_content:

It has been demonstrated that negative emotions have adverse effects on the immune system of a person. This contributes to increased morbidity and mortality in the elderly population and has a direct impact on quality of life. Positive emotions on the other hand may not only undo the harmful effects of negative emotions but also protect against certain diseases. Hence the use of technology to facilitate emotional regulation that reduces negative emotions may be a good way to promote self-care and support well-being. In this paper we present the early design stages of an emotion detection system that aims to support remote support and self-regulation in situations of intense emotional distress. We provide evidence of the suitability of non-parametric cumulative sum (CUSUM) to indentify emotional changes from neutral to non-neutral and vice versa in real time.
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paper_title: Agent-based ambient intelligence for healthcare

paper_content:

Healthcare professionals, working in hospitals experience a high level of mobility due to their need for accessing patients' clinical records, medical devices distributed throughout the premises, and colleagues with whom they collaborate. In this paper, we present how autonomous agents provide capabilities of intelligence and proactivity to healthcare environments furnished with ubiquitous computing and medical devices, resulting thus, in an ambient intelligence (AmI) system. Autonomous agents enable ubiquitous technology to respond to users' particular conditions and demands. To support the building of this type of intelligent systems, we created the SALSA development framework. SALSA is a middleware designed to support the development of AmI environments based on autonomous agents. We illustrate the facilities provided by SALSA and its flexibility to iteratively implement an AmI system for a healthcare application scenario.
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paper_title: GerAmi: Improving Healthcare Delivery in Geriatric Residences

paper_content:

Many countries face an ever-growing need to supply constant care and support for their disabled and elderly populations. In this paper, we've developed geriatric ambient intelligence, an intelligent environment that integrates multiagent systems, mobile devices, RFID, and Wi-Fi technologies to facilitate management and control of geriatric residences. At GerAmi's core is the geriatric agent (GerAg), a deliberative agent that incorporates a case-based planning (CBP) mechanism to optimize work schedules and provide up-to-date patient and facility data. We've successfully implemented a system prototype at a care facility for Alzheimer patients.
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paper_title: Integrating context-aware public displays into a mobile hospital information system

paper_content:

Hospitals are convenient settings for deployment of ubiquitous computing technology. Not only are they technology-rich environments, but their workers experience a high level of mobility resulting in information infrastructures with artifacts distributed throughout the premises. Hospital information systems (HISs) that provide access to electronic patient records are a step in the direction of providing accurate and timely information to hospital staff in support of adequate decision-making. This has motivated the introduction of mobile computing technology in hospitals based on designs which respond to their particular conditions and demands. Among those conditions is the fact that worker mobility does not exclude the need for having shared information artifacts at particular locations. In this paper, we extend a handheld-based mobile HIS with ubiquitous computing technology and describe how public displays are integrated with handheld and the services offered by these devices. Public displays become aware of the presence of physicians and nurses in their vicinity and adapt to provide users with personalized, relevant information. An agent-based architecture allows the integration of proactive components that offer information relevant to the case at hand, either from medical guidelines or previous similar cases.
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paper_title: Activity Recognition for the Smart Hospital

paper_content:

Although researchers have developed robust approaches for estimating, location, and user identity, estimating user activities has proven much more challenging. Human activities are so complex and dynamic that it's often unclear what information is even relevant for modeling activities. Robust approaches to recognize user activities requires identifying the relevant information to be sensed and the appropriate sensing technologies. In our effort to develop an approach for automatically estimating hospital-staff activities, we trained a discrete hidden Markov model (HMM) to map contextual information to a user activity. We trained the model and evaluated it using data captured from almost 200 hours of detailed observation and documentation of hospital workers. In this article, we discuss our approach, the results, and how activity recognition could empower our vision of the hospital as a smart environment.
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paper_title: Ontology-based intelligent fuzzy agent for diabetes application

paper_content:

It is widely pointed out that classical ontologies are not sufficient to deal with imprecise and vague knowledge for some real world applications, but the fuzzy ontology can effectively solve data and knowledge with uncertainty. In this paper, an ontology-based intelligent fuzzy agent (OIFA), including a fuzzy markup language (FML) generating mechanism, a FML parser, a fuzzy inference mechanism, and a semantic decision making mechanism, is proposed to apply to the semantic decision making for diabetes domain. In addition, a FML-based definition is considered modeling the knowledge base and rule base of the fuzzy objects and inference operators. The experimental results show that the proposed method is feasible for diabetes semantic decision-making.
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paper_title: New research perspectives on Ambient Intelligence

paper_content:

Ten years of AmI research have led to many new insights and understandings about the way highly interactive environments should be designed to meet the requirement of being truly unobtrusive and supportive from an end-user perspective. Probably the most revealing finding is the fact that, in addition to cognitive intelligence and computing, also elements from social intelligence and design play a dominant role in the realization of the vision. In this paper we discuss these novel insights and their resulting impact on the AmI research landscape. We introduce a number of new AmI research perspectives that are related to social intelligence and in addition we argue that new ways of working are required applying the concept of Experience Research resulting in a true user-centered approach to Ambient Intelligence.
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paper_title: Interoperable and adaptive fuzzy services for ambient intelligence applications

paper_content:

In Ambient Intelligence (AmI) vision, people should be able to seamlessly and unobtrusively use and configure the intelligent devices and systems in their ubiquitous computing environments without being cognitively and physically overloaded. In other words, the user should not have to program each device or connect them together to achieve the required functionality. However, although it is possible for a human operator to specify an active space configuration explicitly, the size, sophistication, and dynamic requirements of modern living environment demand that they have autonomous intelligence satisfying the needs of inhabitants without human intervention. This work presents a proposal for AmI fuzzy computing that exploits multiagent systems and fuzzy theory to realize a long-life learning strategy able to generate context-aware-based fuzzy services and actualize them through abstraction techniques in order to maximize the users' comfort and hardware interoperability level. Experimental results show that proposed approach is capable of anticipating user's requirements by automatically generating the most suitable collection of interoperable fuzzy services.
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paper_title: Effects of Electromagnetic Radiations from Cell Phones: A Critical Study

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The increasing use of mobile phones has resulted in larger human exposure to radio frequency electromagnetic fields. Although the electromagnetic fields from mobile phones are weak, the high number of exposed persons, together with some stimulating but inconclusive scientific results, has raised concerns about possible health hazards. Most of the biological studies showed that radio frequency modulated electromagnetic fields (like Global System for Mobile Communications (GSM 900 MHz and 1800 MHz)) induced a stress response in the cells, which can potentially change the blood-brain barrier. The potential health effects of human-made electromagnetic fields (EMFs) have been a topic of scientific interest since the late 1800s, particularly in the last twenty years. Electromagnetic fields are natural phenomena that have always been present on earth. However, during the twentieth century, environmental exposure to human-made EMFs increased steadily, predominantly due to increased electricity and wireless technology use. Nearly all people are exposed to a complex mix of different types of weak electric and magnetic fields, both at home and at work. Electrical currents exist naturally in the human body and are an essential part of normal body function. Nerves relay signals by transmitting electric impulses, and most biochemical reactions, from those associated with digestion to those involved in brain activity, proceed by means of rearranging charged particles. This present paper shows the possible effects of electromagnetic radiations emitted from cellular phones on human and living organisms. Therefore, a complete study has been carried out to discuss the serious worries of using mobile phones. The paper has been divided into four parts: part I gives the basis introduction and related studies of cell phones, part II presents the technology behind the cellular networks, part III discusses the basic hazards of using mobile phones, and danger of cellular phone and their prolonged effects, and part IV gives the conclusions and precautions, which should be taken into account while using the cell phones to avoid any serious health related risks.
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paper_title: Living in a World of Smart Everyday Objects—Social, Economic, and Ethical Implications

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ABSTRACT Visions of Pervasive Computing and ambient intelligence involve integrating tiny microelectronic processors and sensors into everyday objects in order to make them “smart.” Smart things can explore their environment, communicate with other smart things, and interact with humans, therefore helping users to cope with their tasks in new, intuitive ways. Although many concepts have already been tested out as prototypes in field trials, the repercussions of such extensive integration of computer technology into our everyday lives are difficult to predict. This article is a first attempt to classify the social, economic, and ethical implications of this development.
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paper_title: A privacy framework for mobile health and home-care systems

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In this paper, we consider the challenge of preserving patient privacy in the context of mobile healthcare and home-care systems, that is, the use of mobile computing and communications technologies in the delivery of healthcare or the provision of at-home medical care and assisted living. This paper makes three primary contributions. First, we compare existing privacy frameworks, identifying key differences and shortcomings. Second, we identify a privacy framework for mobile healthcare and home-care systems. Third, we extract a set of privacy properties intended for use by those who design systems and applications for mobile healthcare and home-care systems, linking them back to the privacy principles. Finally, we list several important research questions that the community should address. We hope that the privacy framework in this paper can help to guide the researchers and developers in this community, and that the privacy properties provide a concrete foundation for privacy-sensitive systems and applications for mobile healthcare and home-care systems.
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paper_title: Perspectives of ambient intelligence in the home environment

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Ambient Intelligence is a vision of the future information society stemming from the convergence of ubiquitous computing, ubiquitous communication and intelligent user-friendly interfaces. It offers an opportunity to realise an old dream, i.e. the smart or intelligent home. Will it fulfil the promises or is it just an illusion--offering apparently easy living while actually increasing the complexity of life? This article touches upon this question by discussing the technologies, applications and social implications of ambient intelligence in the home environment. It explores how Ambient Intelligence may change our way of life. It concludes that there are great opportunities for Ambient Intelligence to support social developments and modern lifestyles. However, in order to gain wide acceptance a delicate balance is needed: the technology should enhance the quality of life but not be seeking domination. It should be reliable and controllable but nevertheless adaptive to human habits and changing contexts.
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paper_title: Safeguards in a world of ambient intelligence

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Copy the following link for free access to the first chapter of this title: http://www.springerlink.com/content/j23468h304310755/fulltext.pdf This book is a warning. It aims to warn policy-makers, industry, academia, civil society organisations, the media and the public about the threats and vulnerabilities facing our privacy, identity, trust, security and inclusion in the rapidly approaching world of ambient intelligence (AmI). In the near future, every manufactured product our clothes, money, appliances, the paint on our walls, the carpets on our floors, our cars, everything will be embedded with intelligence, networks of tiny sensors and actuators, which some have termed smart dust. The AmI world is not far off. We already have surveillance systems, biometrics, personal communicators, machine learning and more. AmI will provide personalised services and know more about us on a scale dwarfing anything hitherto available. In the AmI vision, ubiquitous computing, communications and interfaces converge and adapt to the user. AmI promises greater user-friendliness in an environment capable of recognising and responding to the presence of different individuals in a seamless, unobtrusive and often invisible way. While most stakeholders paint the promise of AmI in sunny colours, there is a dark side to AmI. This book aims to illustrate the threats and vulnerabilities by means of four dark scenarios. The authors set out a structured methodology for analysing the four scenarios, and then identify safeguards to counter the foreseen threats and vulnerabilities. They make recommendations to policy-makers and other stakeholders about what they can do to maximise the benefits from ambient intelligence and minimise the negative consequences.
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paper_title: Balancing Smartness and Privacy for the Ambient Intelligence

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Ambient Intelligence (AmI) will introduce large privacy risks. Stored context histories are vulnerable for unauthorized disclosure, thus unlimited storing of privacy-sensitive context data is not desirable from the privacy viewpoint. However, high quality and quantity of data enable smartness for the AmI, while less and coarse data benefit privacy. This raises a very important problem to the AmI, that is, how to balance the smartness and privacy requirements in an ambient world. In this article, we propose to give to donors the control over the life cycle of their context data, so that users themselves can balance their needs and wishes in terms of smartness and privacy.
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paper_title: Privacy by Design - Principles of Privacy-Aware Ubiquitous Systems

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This paper tries to serve as an introductory reading to privacy issues in the field of ubiquitous computing. It develops six principles for guiding system design, based on a set of fair information practices common in most privacy legislation in use today: notice, choice and consent, proximity and locality, anonymity and pseudonymity, security, and access and recourse. A brief look at the history of privacy protection, its legal status, and its expected utility is provided as a background.
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Title: A Survey on Ambient Intelligence in Healthcare

Section 1: INTRODUCTION
Description 1: This section introduces the concept of Ambient Intelligence (AmI) and its relevance to healthcare, including key characteristics and technological underpinnings.

Section 2: Healthcare Challenges
Description 2: This section outlines the current challenges faced in the healthcare sector, particularly due to rising costs and aging populations, and discusses how AmI can address these challenges.

Section 3: SUPPORTING INFRASTRUCTURE AND TECHNOLOGY
Description 3: This section details the infrastructure and technologies that support AmI systems in healthcare, including body area networks (BANs), dense/mesh sensor networks, and recent trends in sensor technology.

Section 4: ALGORITHMS AND METHODS
Description 4: This section introduces the computational methodologies essential for developing AmI healthcare applications, such as activity recognition, behavioral pattern discovery, anomaly detection, planning and scheduling, and decision support systems.

Section 5: APPLICATIONS
Description 5: This section discusses various AmI applications in healthcare, including continuous health and behavioral monitoring, emergency detection, assisted living, therapy and rehabilitation, persuasive wellbeing applications, emotional wellbeing, and smart hospitals.

Section 6: Artificial Intelligence
Description 6: This section explores the role of artificial intelligence in healthcare, detailing its use in diagnosis, prognosis, medical training, and how AmI can enhance AI methodologies.

Section 7: Design and Human Factors
Description 7: This section addresses the design considerations and human factors that must be taken into account when developing AmI systems to ensure they improve quality of life without adverse effects.

Section 8: Security and Infrastructure
Description 8: This section discusses the security issues and infrastructural requirements for AmI systems, emphasizing the need for safeguards to protect data privacy and system security.

Section 9: Social and Ethical Issues
Description 9: This section highlights the social and ethical considerations surrounding the use of AmI in healthcare, such as ensuring accessibility, avoiding overreliance, and maintaining patient communication.

Section 10: CONCLUSION
Description 10: This section summarizes the findings of the survey, reflecting on the potential and challenges of AmI in healthcare, and expresses confidence in achieving the vision of AmI through continued interdisciplinary research.