Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats
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paper_title: Human activity analysis: A review

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Human activity recognition is an important area of computer vision research. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. Most of these applications require an automated recognition of high-level activities, composed of multiple simple (or atomic) actions of persons. This article provides a detailed overview of various state-of-the-art research papers on human activity recognition. We discuss both the methodologies developed for simple human actions and those for high-level activities. An approach-based taxonomy is chosen that compares the advantages and limitations of each approach. Recognition methodologies for an analysis of the simple actions of a single person are first presented in the article. Space-time volume approaches and sequential approaches that represent and recognize activities directly from input images are discussed. Next, hierarchical recognition methodologies for high-level activities are presented and compared. Statistical approaches, syntactic approaches, and description-based approaches for hierarchical recognition are discussed in the article. In addition, we further discuss the papers on the recognition of human-object interactions and group activities. Public datasets designed for the evaluation of the recognition methodologies are illustrated in our article as well, comparing the methodologies' performances. This review will provide the impetus for future research in more productive areas.
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paper_title: Predicting smart home lighting behavior from sensors and user input using very fast decision tree with Kernel Density Estimation and improved Laplace correction

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One way to predict the behavior of smart home lighting is by using machine learning. Currently many methods of supervised learning that used for this problem, one of them is decision tree method. Very Fast Decision Tree (VFDT) as one of the decision tree method that has advantages in online machine learning that may useful in smart home, but there are still some room of improvisation that can improve accuracy of VFDT. The experiment result is obtained that VFDT is better than Naive Bayes (NB) and Artificial Neural Network (ANN) in offline and online experiment. In addition, Kernel Density Estimation (KDE) and improved Laplace correction that is used as improvisation of VFDT is able to increase the accuracy and Matthews Correlation Coefficient (MCC) of VFDT in predicting smart home lighting switch usage.
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paper_title: The Making of a Dataset for Smart Spaces

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In this paper we propose a two-phase methodology for designing datasets that can be used to test and evaluate activity recognition algorithms. The trade offs between time, cost and recognition performance is one challenge. The effectiveness of a dataset, which contrasts the incremental performance gain with the increase in time, efforts, and number and cost of sensors is another challenging area that is often overlooked. Our proposed methodology is iterative and adaptive and addresses issues of sensor use modality and its effect on overall performance. We present our methodology and provide an assessment for its effectiveness using both a simulation model and a real world deployment.
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paper_title: The MERL motion detector dataset

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Looking into the future of residential and office building Mitsubishi Electric Research Labs (MERL) has been collecting motion sensor data from a network of over 200 sensors for a year. The data is the residual traces of year in the life of a research laboratory. It contains interesting spatio-temporal structure ranging all the way from the seconds of individuals walking down hallways, the minutes in lobbies chatting with colleagues, the hours of dozens of people attending talks and meetings, the days and weeks that drive the patterns of life, to the months and seasons with their ebb and flow of visiting employees. This document describes that dataset, which contains well over 30 million raw motion records, spanning a calendar year and two floors of our research laboratory, as well as calender, weather, and some intermediate analytic results. The dataset was originally released as part of the 2007 Workshop on Massive Datasets. The dataset can be obtained from http://www.merl.com/wmd.
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paper_title: A Possibilistic Approach for Activity Recognition in Smart Homes for Cognitive Assistance to Alzheimer’s Patients

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Providing cognitive assistance to Alzheimer’s patients in smart homes is a field of research that receives a lot of attention lately. The recognition of the patient’s behavior when he carries out some activities in a smart home is primordial in order to give adequate assistance at the opportune moment. To address this challenging issue, we present a formal activity recognition framework based on possibility theory and description logics. We present initial results from an implementation of this recognition approach in a smart home laboratory.
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paper_title: An Incremental Adaptive Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments

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In this paper, we present a novel type-2 fuzzy systems based adaptive architecture for agents embedded in ambient intelligent environments (AIEs). Type-2 fuzzy systems are able to handle the different sources of uncertainty and imprecision encountered in AIEs to give a very good response. The presented agent architecture uses a one pass method to learn in a nonintrusive manner the user's particular behaviors and preferences for controlling the AIE. The agent learns the user's behavior by learning his particular rules and interval type-2 Membership Functions (MFs), these rules and MFs can then be adapted online incrementally in a lifelong learning mode to suit the changing environmental conditions and user preferences. We will show that the type-2 agents generated by our one pass learning technique outperforms those generated by genetic algorithms (GAs). We will present unique experiments carried out by different users over the course of the year in the Essex Intelligent Dormitory (iDorm), which is a real AIE test bed. We will show how the type-2 agents learnt and adapted to the occupant's behavior whilst handling the encountered short term and long term uncertainties to give a very good performance that outperformed the type-1 agents while using smaller rule bases
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paper_title: SESAME-S: Semantic Smart Home System for Energy Efficiency

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As the urgent need for efficient and sustainable energy usage becomes ever more apparent, interest in Smart Homes is on the rise. The SESAME-S project (SEmantic SmArt Metering – Services for Energy Efficient Houses) uses semantically linked data to actively assist end-consumers in making well-informed decisions and controlling their energy consumption. By integrating smart metering and home automation functionality, SESAME-S works to effectively address the potential mass market of end-consumers with an easily customizable solution that can be widely implemented in domestic or business environments, with expected savings of over 20 % from the total energy bill. The developed system is a basis for conceptualizing, demonstrating, and evaluating a variety of innovative end-consumer services and their user interface paradigms. In this paper, we present the SESAME-S system as a whole and discuss the semantically enabled services, demonstrating that such systems may have broad acceptance in the future. The data obtained through such systems will be invaluable for future global energy-efficiency strategies and businesses.
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paper_title: Recognizing human activity in smart home using deep learning algorithm

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There is a topic of interest for many researchers about human activities recognition in recent years. In this paper, we propose the research to recognize human activities by deep learning (DL) algorithm. We collected the data from participants performing activities in order to evaluate the human activities recognition results. After pre-training the deep network, the fine-tuning process begins. Compared with Hidden Markov Model (HMM) and naive Bayes Classifier (NBC), the experiment results show that the proposed deep learning algorithm is an effective way for recognizing human activities in smart home.
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paper_title: Inferring health metrics from ambient smart home data

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As the population ages, smart home technology and applications are expected to support older adults to age in place and reduce the associated economic and societal burden. This paper describes a study where the relationship between ambient sensors, permanently deployed as part of smart aware apartments, and clinically validated health questionnaires is investigated. 27 sets of ambient data were taken from a 28 day block from 13 participants all of whom were over 60 years old. Features derived from ambient sensor data were found to be significantly correlated to measures of anxiety, sleep quality, depression, loneliness, cognition, quality of life and independent living skills (IADL). Subsequently, linear discriminant analysis was shown to predict participants suffering from increased anxiety and loneliness with a high accuracy (≥70%). While the number of participants is small, this study reports that objective ambient features may be used to infer clinically validated health metrics. Such findings may be used to inform interventions for active and healthy ageing.
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paper_title: Machine Recognition of Human Activities: A Survey

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The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as content-based video annotation and retrieval, highlight extraction and video summarization require recognition of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing-robustness against errors in low-level processing, view and rate-invariant representations at midlevel processing and semantic representation of human activities at higher level processing-make this problem hard to solve. In this review paper, we present a comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications. We discuss the problem at two major levels of complexity: 1) "actions" and 2) "activities." "Actions" are characterized by simple motion patterns typically executed by a single human. "Activities" are more complex and involve coordinated actions among a small number of humans. We will discuss several approaches and classify them according to their ability to handle varying degrees of complexity as interpreted above. We begin with a discussion of approaches to model the simplest of action classes known as atomic or primitive actions that do not require sophisticated dynamical modeling. Then, methods to model actions with more complex dynamics are discussed. The discussion then leads naturally to methods for higher level representation of complex activities.
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paper_title: Developing shared home behavior datasets to advance HCI and ubiquitous computing research

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Researchers in human-computer interaction and allied fields are increasingly interested in using new sensing capabilities to create context-aware interfaces and devices for the home. Data from sensors worn on the body or installed in an environment can be used by algorithms to infer what activities the home occupant may be engaged in and enable applications to respond accordingly. This one-day CHI'09 workshop would convene a multidisciplinary group of researchers to discuss strategies for creating community resources that might accelerate research on development of home technologies. In particular, the participants will discuss how to collaboratively gather high quality synchronized data streams from real homes, as well as qualitative material about home occupants and their behaviors. The resultant datasets could facilitate work on context modeling and enable researchers in other areas of HCI to explore contextual factors influencing the use of technology in naturalistic settings. The outcome of the workshop will be a community index of existing shared datasets of home behavior and guidelines for those interested in creating and disseminating new datasets.
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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: Studying always-on electricity feedback in the home

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The recent emphasis on sustainability has made consumers more aware of their responsibility for saving resources, in particular, electricity. Consumers can better understand how to save electricity by gaining awareness of their consumption beyond the typical monthly bill. We conducted a study to understand consumers' awareness of energy consumption in the home and to determine their requirements for an interactive, always-on interface for exploring data to gain awareness of home energy consumption. In this paper, we describe a three-stage approach to supporting electricity conservation routines: raise awareness, inform complex changes, and maintain sustainable routines. We then present the findings from our study to support design implications for energy consumption feedback interfaces.
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paper_title: ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home

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This paper presents ElectriSense, a new solution for automatically detecting and classifying the use of electronic devices in a home from a single point of sensing. ElectriSense relies on the fact that most modern consumer electronics and fluorescent lighting employ switch mode power supplies (SMPS) to achieve high efficiency. These power supplies continuously generate high frequency electromagnetic interference (EMI) during operation that propagates throughout a home's power wiring. We show both analytically and by in-home experimentation that EMI signals are stable and predictable based on the device's switching frequency characteristics. Unlike past transient noise-based solutions, this new approach provides the ability for EMI signatures to be applicable across homes while still being able to differentiate between similar devices in a home. We have evaluated our solution in seven homes, including one six-month deployment. Our results show that ElectriSense can identify and classify the usage of individual devices with a mean accuracy of 93.82%.
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paper_title: Supervised classification of activities of daily living in health smart homes using SVM

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By 2050, about a third of the French population will be over 65. To face this modification of the population, the current studies of our laboratory focus on the monitoring of elderly people at home. This aims at detect, as early as possible, a loss of autonomy by objectivizing criterions such as the international ADL or the French AGGIR scales implementing automatic classification of the different Activities of Daily Living. A Health Smart Home is used to achieve this goal. This flat includes different sensors. The data from the various sensors were used to classify each temporal frame into one of the activities of daily living that has been previously learnt (seven activities: hygiene, toilets, eating, resting, sleeping, communication and dressing/undressing). This is done using Support Vector Machines. We performed an experimentation with 13 young and healthy subjects to learn the model of activities and then we tested the classification algorithm (cross-validation) on real data.
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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: CASAS: A Smart Home in a Box

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The CASAS architecture facilitates the development and implementation of future smart home technologies by offering an easy-to-install lightweight design that provides smart home capabilities out of the box with no customization or training.
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paper_title: Activity Recognition: Approaches, Practices and Trends

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Activity recognition has attracted increasing attention as a number of related research areas such as pervasive computing, intelligent environments and robotics converge on this critical issue. It is also driven by growing real-world application needs in such areas as ambient assisted living and security surveillance. This chapter aims to provide an overview on existing approaches, current practices and future trends on activity recognition. It is intended to provide the necessary material to inform relevant research communities of the latest developments in this field in addition to providing a reference for researchers and system developers who are working towards the design and development of activity-based context aware applications. The chapter first reviews the existing approaches and algorithms that have been used for activity recognition in a number of related areas. It then describes the practice and lifecycle of the ontology-based approach to activity recognition that has recently been under vigorous investigation. Finally the chapter presents emerging research on activity recognition by outlining various issues and directions the field will take.
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paper_title: Using a Live-In Laboratory for Ubiquitous Computing Research

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Ubiquitous computing researchers are increasingly turning to sensor-enabled living laboratories for the study of people and technologies in settings more natural than a typical laboratory. We describe the design and operation of the PlaceLab, a new live-in laboratory for the study of ubiquitous technologies in home settings. Volunteer research participants individually live in the PlaceLab for days or weeks at a time, treating it as a temporary home. Meanwhile, sensing devices integrated into the fabric of the architecture record a detailed description of their activities. The facility generates sensor and observational datasets that can be used for research in ubiquitous computing and other fields where domestic contexts impact behavior. We describe some of our experiences constructing and operating the living laboratory, and we detail a recently generated sample dataset, available online to researchers.
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paper_title: MavHome: an agent-based smart home

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The goal of the MavHome (Managing An Intelligent Versatile Home) project is to create a home that acts as an intelligent agent. In this paper we introduce the MavHome architecture. The role of prediction algorithms within the architecture is discussed, and a meta-predictor is presented which combines the strengths of multiple approaches to inhabitant action prediction. We demonstrate the effectiveness of these algorithms on smart home data.
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paper_title: Location-Based Activity Recognition using Relational Markov Networks

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In this paper we define a general framework for activity recognition by building upon and extending Relational Markov Networks. Using the example of activity recognition from location data, we show that our model can represent a variety of features including temporal information such as time of day, spatial information extracted from geographic databases, and global constraints such as the number of homes or workplaces of a person. We develop an efficient inference and learning technique based on MCMC. Using GPS location data collected by multiple people we show that the technique can accurately label a person's activity locations. Furthermore, we show that it is possible to learn good models from less data by using priors extracted from other people's data.
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paper_title: Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques

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One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study, we use smart home and wearable sensors to collect data, while ( $n = 84$ ) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Our machine learning classifiers reach an accuracy of 0.97 with an area under the ROC curve value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant.
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paper_title: Smart-Home Technologies to Assist Older People to Live Well at Home

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Background: With the rapid population ageing that is occurring world-wide, there is increasing interest in “smart home” technologies that can assist older adults to continue living at home with safety and independence. This systematic review and critical evaluation of the world wide literature assesses the effectiveness and feasibility of smart-home technologies for promoting independence, health, well-being and quality of life, in older adults. Methods: A total of 1877 “smart home” publications were identified by the initial search of peer reviewed journals. Of these, 21 met our inclusion criteria for the review and were subject to data extraction and quality assessment. Results: Smart-home technologies included different types of active and passive sensors, monitoring devices, robotics and environmental control systems. One study assessed effectiveness of a smart home technology. Sixteen reported on the feasibility of smart-home technology and four were observational studies. Conclusion: Older adults were reported to readily accept smart-home technologies, especially if they benefited physical activity, independence and function and if privacy concerns were addressed. Given the modest number of objective analyses, there is a need for further scientific analysis of a range of smart home technologies to promote community living.
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paper_title: PreHeat: controlling home heating using occupancy prediction

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Home heating is a major factor in worldwide energy use. Our system, PreHeat, aims to more efficiently heat homes by using occupancy sensing and occupancy prediction to automatically control home heating. We deployed PreHeat in five homes, three in the US and two in the UK. In UK homes, we controlled heating on a per-room basis to enable further energy savings. We compared PreHeat's prediction algorithm with a static program over an average 61 days per house, alternating days between these conditions, and measuring actual gas consumption and occupancy. In UK homes PreHeat both saved gas and reduced MissTime (the time that the house was occupied but not warm). In US homes, PreHeat decreased MissTime by a factor of 6-12, while consuming a similar amount of gas. In summary, PreHeat enables more efficient heating while removing the need for users to program thermostat schedules.
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paper_title: Smart homes and their users: a systematic analysis and key challenges

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Published research on smart homes and their users is growing exponentially, yet a clear understanding of who these users are and how they might use smart home technologies is missing from a field being overwhelmingly pushed by technology developers. Through a systematic analysis of peer-reviewed literature on smart homes and their users, this paper takes stock of the dominant research themes and the linkages and disconnects between them. Key findings within each of nine themes are analysed, grouped into three: (1) views of the smart home--functional, instrumental, socio-technical; (2) users and the use of the smart home--prospective users, interactions and decisions, using technologies in the home; and (3) challenges for realising the smart home--hardware and software, design, domestication. These themes are integrated into an organising framework for future research that identifies the presence or absence of cross-cutting relationships between different understandings of smart homes and their users. The usefulness of the organising framework is illustrated in relation to two major concerns--privacy and control--that have been narrowly interpreted to date, precluding deeper insights and potential solutions. Future research on smart homes and their users can benefit by exploring and developing cross-cutting relationships between the research themes identified.
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paper_title: People, sensors, decisions: Customizable and adaptive technologies for assistance in healthcare

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The ratio of healthcare professionals to care recipients is dropping at an alarming rate, particularly for the older population. It is estimated that the number of persons with Alzheimer's disease, for example, will top 100 million worldwide by the year 2050 [Alzheimer's Disease International 2009]. It will become harder and harder to provide needed health services to this population of older adults. Further, patients are becoming more aware and involved in their own healthcare decisions. This is creating a void in which technology has an increasingly important role to play as a tool to connect providers with recipients. Examples of interactive technologies range from telecare for remote regions to computer games promoting fitness in the home. Currently, such technologies are developed for specific applications and are difficult to modify to suit individual user needs. The future potential economic and social impact of technology in the healthcare field therefore lies in our ability to make intelligent devices that are customizable by healthcare professionals and their clients, that are adaptive to users over time, and that generalize across tasks and environments. A wide application area for technology in healthcare is for assistance and monitoring in the home. As the population ages, it becomes increasingly dependent on chronic healthcare, such as assistance for tasks of everyday life (washing, cooking, dressing), medication taking, nutrition, and fitness. This article will present a summary of work over the past decade on the development of intelligent systems that provide assistance to persons with cognitive disabilities. These systems are unique in that they are all built using a common framework, a decision-theoretic model for general-purpose assistance in the home. In this article, we will show how this type of general model can be applied to a range of assistance tasks, including prompting for activities of daily living, assistance for art therapists, and stroke rehabilitation. This model is a Partially Observable Markov Decision Process (POMDP) that can be customized by end-users, that can integrate complex sensor information, and that can adapt over time. These three characteristics of the POMDP model will allow for increasing uptake and long-term efficiency and robustness of technology for assistance.
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paper_title: A tutorial on human activity recognition using body-worn inertial sensors

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The last 20 years have seen ever-increasing research activity in the field of human activity recognition. With activity recognition having considerably matured, so has the number of challenges in designing, implementing, and evaluating activity recognition systems. This tutorial aims to provide a comprehensive hands-on introduction for newcomers to the field of human activity recognition. It specifically focuses on activity recognition using on-body inertial sensors. We first discuss the key research challenges that human activity recognition shares with general pattern recognition and identify those challenges that are specific to human activity recognition. We then describe the concept of an Activity Recognition Chain (ARC) as a general-purpose framework for designing and evaluating activity recognition systems. We detail each component of the framework, provide references to related research, and introduce the best practice methods developed by the activity recognition research community. We conclude with the educational example problem of recognizing different hand gestures from inertial sensors attached to the upper and lower arm. We illustrate how each component of this framework can be implemented for this specific activity recognition problem and demonstrate how different implementations compare and how they impact overall recognition performance.
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paper_title: Activity Recognition on Streaming Sensor Data

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Many real-world applications that focus on addressing needs of a human, require information about the activities being performed by the human in real-time. While advances in pervasive computing have led to the development of wireless and non-intrusive sensors that can capture the necessary activity information, current activity recognition approaches have so far experimented on either a scripted or pre-segmented sequence of sensor events related to activities. In this paper we propose and evaluate a sliding window based approach to perform activity recognition in an on line or streaming fashion; recognizing activities as and when new sensor events are recorded. To account for the fact that different activities can be best characterized by different window lengths of sensor events, we incorporate the time decay and mutual information based weighting of sensor events within a window. Additional contextual information in the form of the previous activity and the activity of the previous window is also appended to the feature describing a sensor window. The experiments conducted to evaluate these techniques on real-world smart home datasets suggests that combining mutual information based weighting of sensor events and adding past contextual information to the feature leads to best performance for streaming activity recognition.
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paper_title: Unsupervised detection and analysis of changes in everyday physical activity data

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Display Omitted A framework for detecting and analyzing changes in physical activity time series data.A method to evaluate if the detected changes are representative of a significant lifestyle modification.Evaluation based on synthetic data and real-world Fitbit data collected from older adults. Sensor-based time series data can be utilized to monitor changes in human behavior as a person makes a significant lifestyle change, such as progress toward a fitness goal. Recently, wearable sensors have increased in popularity as people aspire to be more conscientious of their physical health. Automatically detecting and tracking behavior changes from wearable sensor-collected physical activity data can provide a valuable monitoring and motivating tool. In this paper, we formalize the problem of unsupervised physical activity change detection and address the problem with our Physical Activity Change Detection (PACD) approach. PACD is a framework that detects changes between time periods, determines significance of the detected changes, and analyzes the nature of the changes. We compare the abilities of three change detection algorithms from the literature and one proposed algorithm to capture different types of changes as part of PACD. We illustrate and evaluate PACD on synthetic data and using Fitbit data collected from older adults who participated in a health intervention study. Results indicate PACD detects several changes in both datasets. The proposed change algorithms and analysis methods are useful data mining techniques for unsupervised, window-based change detection with potential to track users physical activity and motivate progress toward their health goals.
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paper_title: Technologies for an aging society: a systematic review of "smart home" applications.

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Objectives A “smart home” is a residence wired with technology features that monitor the well-being and activities of their residents to improve overall quality of life, increase independence and prevent emergencies. This type of informatics applications targeting older adults, people with disabilities or the general population is increasingly becoming the focus of research worldwide. The aim of this study was to provide a comprehensive review of health related smart home projects and discuss human factors and other challenges. MethodsTo cover not only the medical but also the social sciences and electronics literature, we conducted extensive searches across disciplines (e.g., Medline , Embase , CINAHL, PsycINFO, Electronics and Communications Abstracts, Web of Science etc.). In order to be inclusive of all new initiatives and efforts in this area given the innovativeness of the concept, we manually searched for relevant references in the retrieved articles as well as published books on smart homes and gerontechnology Results A total of 114 publications (including papers, abstracts and web pages) were identified and reviewed to identify the overarching projects. Twenty one smart home projects were identified (71% of the projects include technologies for functional monitoring, 67% for safety monitoring, 47% for physiological monitoring, 43% for cognitive support or sensory aids, 19% for monitoring security and 19% to increase social interaction). Evidence for their impact on clinical outcomes is lacking. Conclusions The field of smart homes is a growing informatics domain. Several challenges including not only technical but also ethical ones need to be addressed.
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paper_title: Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures

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The electricity industry is now at the verge of a new era—an era that promises, through the evolution of the existing electrical grids to smart grids, more efficient and effective power management, better reliability, reduced production costs, and more environmentally friendly energy generation. Numerous initiatives across the globe, led by both industry and academia, reflect the mounting interest around not only the enormous benefits but also the great risks introduced by this evolution. This paper focuses on issues related to the security of the smart grid and the smart home, which we present as an integral part of the smart grid. Based on several scenarios, we aim to present some of the most representative threats to the smart home/smart grid environment. The threats detected are categorized according to specific security goals set for the smart home/smart grid environment, and their impact on the overall system security is evaluated. A review of contemporary literature is then conducted with the aim of presenting promising security countermeasures with respect to the identified specific security goals for each presented scenario. An effort to shed light on open issues and future research directions concludes this paper.
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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: Testing non-wearable fall detection methods in the homes of older adults.

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In this paper, we describe two longitudinal studies in which fall detection sensor technology was tested in the homes of older adults. The first study tested Doppler radar, a two-webcam system, and a depth camera system in ten apartments for two years. This continuous data collection allowed us to investigate the real-world setting of target users and compare the advantages and limitations of each sensor modality. Based on this study, the depth camera was chosen for a current ongoing study in which depth camera systems have been installed in 94 additional older adult apartments. We include a discussion of the different sensor systems, the pros and cons of each, and results of the fall detection and false alarms in the older adult homes.
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paper_title: Correct by Construction Security Approach to Design Fault Tolerant Smart Homes for Disabled People

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Abstract Smart homes dedicated to people with disabilities, specially those with dementia, are critical systems which need to remain safe and adapted to the user. However the control part of these systems, that is ensuring their safety, is both difficult to design as well as to verify without appropriate tools. Formal techniques have been used to cope with the verification problem, but this paper proposes a new way to specify smart home safety which also eases the design aspect. It enables the use of a correct by construction technique – Discrete Controller Synthesis – to automatically build from constraints a maximally permissive safety controller.
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paper_title: Wearable key: device for personalizing nearby environment

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This paper describes a system that allows users who obtain a "wearable ID key" to personalize dynamically ubiquitous computers by simply touching them. We call the concept of providing personalized service by touch "Active Personalization". For active personalization, users only have to wear a digital key and do not have to carry around other computers. When users touch ubiquitous computers with their wearable key, the keyholes of the ubiquitous computers recognize their IDs and can personalize the computers. We developed a new network technology between keys and keyholes that enables digital information to be carried through a person's body based on a near-field technology we call TouchNet.
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paper_title: Biometric Authentication: System Security and User Privacy

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While biometric systems aren't foolproof, the research community has made significant strides to identify vulnerabilities and develop measures to counter them.
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paper_title: Digital home technologies and transformation of households

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The basic issue I examine here is whether and how contemporary home life is being transformed with the arrival of new digital technologies. As new technologies diffuse into the home, new terminology has begun to emerge as, for example, in smart homes, home automation, digital home, digital living, networked home, home of the future, smart appliances and so on. To simplify the terminology, in this paper, I will use the term “digital home technologies” and smart home technologies interchangeably to describe all of them. Although digital home technologies have developed in different directions because of the types of industry players involved, some common themes underlie these developments. They all seem to point to a great sense of anticipation that home life as we have understood in the past two or three decades will undergo some fundamental changes. It is claimed that some of the changes may be the result of advances at the technological frontier. It is generally acknowledged that the digital home idea has been around for at least a decade and people had known about its potential possibility even in the mid-1980s from the prototypes built in the USA, UK and Scandinavia. Embedded in the concept of digital home are smart appliances, multimedia systems, energy devices, sensors, lighting systems, sensors and control systems, and home robots which manifest basic qualities of programmable machine intelligence. However, their implementation has not been very successful and has been a little slow. Recent developments seem to suggest that digital home concepts are closer to reality and must be taken seriously. To put these developments in a historical perspective, one can trace all such advances to the early 1980s with the introduction of the PC into the home. This was also the period when various electronic gadgets entered the domestic space: VCRs, microwave ovens, answering machines, cable TV to name important few. A lot has happened since then. For example, the technological scene changed dramatically with the arrival of the Internet connecting the household to the external environment in some fundamental ways. The introduction of mobile phones and wireless technologies has further opened up the technological boundaries. The possibilities seem endless. In this ever increasing technological frenzy, some caution must be exercised as new technologies knock on the door to gain acceptance by families. Our previous studies show that families are reluctant to “overtechnologize” their homes, but at the same time are quite open to technologies that fit with their current patterns of behaviors and possibly add value to the family life. It is this balance between too much and too little technology that one must seek and it is also what motivates our thinking in this area.
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paper_title: An Investigative Study for Smart Home Security: Issues, Challenges and Countermeasures

paper_content:

Home security should be a top concern for everyone who owns or rents a home. Moreover, safe and secure residential space is the necessity of every individual as most of the family members are working. The home is left unattended for most of the day-time and home invasion crimes are at its peak as constantly monitoring of the home is difficult. Another reason for the need of home safety is specifically when the elderly person is alone or the kids are with baby-sitter and servant. Home security system i.e. HomeOS is thus applicable and desirable for resident’s safety and convenience. This will be achieved by turning your home into a smart home by intelligent remote monitoring. Smart home comes into picture for the purpose of controlling and monitoring the home. It will give you peace of mind, as you can have a close watch and stay connected anytime, anywhere. But, is common man really concerned about home security? An investigative study was done by conducting a survey to get the inputs from different people from diverse backgrounds. The main motivation behind this survey was to make people aware of advanced HomeOS and analyze their need for security. This paper also studied the necessity of HomeOS investigative study in current situation where the home burglaries are rising at an exponential rate. In order to arrive at findings and conclusions, data were analyzed. The graphical method was employed to identify the relative significance of home security. From this analysis, we can infer that the cases of having kids and aged person at home or location of home contribute significantly to the need of advanced home security system. At the end, the proposed system model with its flow and the challenges faced while implementing home security systems are also discussed.
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paper_title: Acoustic fall detection using Gaussian mixture models and GMM supervectors

paper_content:

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: Using Ground Reaction Forces from Gait Analysis: Body Mass as a Weak Biometric

paper_content:

Ground reaction forces generated during normal walking have recently been used to identify and/or classify individuals based upon the pattern of the forces observed over time. One feature that can be extracted from vertical ground reaction forces is body mass. This single feature has identifying power comparable to other studies that use multiple and more complex features. This study contributes to understanding the role of body mass in identification by (1) quantifying the accuracy and precision with which body mass can be obtained using vertical ground reaction forces, (2) quantifying the distribution of body mass across a population larger than has previously been studied in relation to gait analysis, and (3) quantifying the expected identification capabilities of systems using body mass as a weak biometric. Our results show that body mass can be measured in a fraction of a second with less than a 1 kilogram standard deviation of error.
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paper_title: Anthropometric and human gait identification using skeleton data from Kinect sensor

paper_content:

This work investigates the use of 3D skeleton data retrieved from Microsoft Kinect sensor to obtain gait kinematic parameters from human walk for biometric identification. Several subjects were captured walking in front of the Kinect sensor resulting in a data set containing several 3D points from skeleton joints of each person. These points were used to extract anthropometric information and calculate angles described by lower joints (hips, knees and ankles) during walk in order to extract gait kinematic parameters. Statistical descriptors were used in the resulting curves to define attributes to train machine learning models (K-Nearest Neighbor and Multilayer Perceptron) and the models tested for its efficacy in identifying individuals from the attributes. The results were validated using the 10-fold cross-validation method and showed an overall high rate of accuracy.
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paper_title: A neural network approach towards reinforcing smart home security

paper_content:

In today's context, it is common to leave the house unattended as people are busy catching up with their tight daily schedule. Therefore, most people have chosen the home security system as the most reliable way to protect their home. However, the existing security mechanism provided by smart home is lack of intelligence for higher level decision making and action taking. Furthermore, the current security management of smart home is usually predefined by user. Thus, for consequences that are out of the defined scopes, chances to trigger false alarm are very high. Besides, the environment of each smart home is difference and the culture is also different from country to country, so the predefined security instructions may not work well or they may not suitable for all scenarios. Therefore, an intelligent algorithm with self learning ability must be adopted for smart home system in order to take adequate actions against the evaluated environment and decide for the necessary actions. Neural networks have been proposed in this paper where they are adaptive statistical models based on an analogy with the structure of the brain. The intelligence of the smart home should be able to analyze the information gathered by the sensors and detectors and to respond based on the analysis and the experiences gained via the neural networks. Neural network is chosen because they have the learning ability to estimate the parameters of some populations using a small number of examples at a time. When come to neural network learning process, there are several learning algorithms that could be applied, which can learn through pattern recognition, movement recognition, action recognition and so forth.
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paper_title: Ubiquitous Health Monitoring at Home – Sensing of Human Biosignals on Flooring, on Tatami Mat, in the Bathtub, and in the Lavatory

paper_content:

In the graying society, it is important to monitor health-related biosignal with sensors in the living environment for the sake of emergency response and long-term health management. In order to use biosignal data monitoring systems daily at home, noninvasive monitoring and system maintenance are crucial. We propose a method of estimating the sleep stages of sleeping subjects through noninvasive measurement of heartbeat and respiration using a pneumatic method and an air mattress. However, the method incurs maintenance for periodically refilling the air of the mattress. In this paper, another pneumatic method, which uses an air tube made of the silicon rubber instead of the air mattress, is proposed. The change in S/N ratio in heartbeat and respiration signals, under greater background noise, are compared for the following: in a room with wooden flooring; in a room with tatami mats; in a bathtub; and in a lavatory. The results show that both the heartbeat and respiration can be measured with the S/N ratio of around 30 dB, and the signal of each heartbeat can also be confirmed provided the maximum background noise in the room with wooden flooring, in the room with tatami mats, in the bathtub, and in the lavatory are 0.1 m/s 2,0.9 m/s2, 100 mI/s, and 0.1 m/s 2, respectively.
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paper_title: Behaviometrics for multiple residents in a smart environment

paper_content:

Smart homes and ambient intelligence show great promise in the fields of medical monitoring, energy efficiency and ubiquitous computing applications. Their ability to adapt and react to the people relying on them positions these systems to be invaluable tools for our aging populations. This work introduces and explores solutions for issues surrounding real world multiple inhabitant smart home situations. Dealing with multiple residents without requiring wireless tracking devices, while paying heed to privacy concerns, is a difficult proposition at best. ::: The Center for Advanced Studies in Adaptive Systems research group has developed and tested a number of novel technologies to address the issues of multiple inhabitants within a smart home context using inexpensive, low profile, privacy sensitive sensors. These smart home implementations, when combined with artificial intelligence tools, are designed to provide localization, tracking, and identification through behaviometric approaches that are useful and deployable in real world situations. They have been evaluated using unscripted living spaces with multiple residents, and their capabilities explored as a means of benefiting other modeling tools, such as detecting the Activities of Daily Living. ::: Given the complex nature and diverse needs of smart home technologies, the tools presented here are by no means definitive solutions to handling multiple resident smart environment situations. However, they do provide a strong working base for the continued development of smart environments with demonstrable benefits on real-world implementations.
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paper_title: Instrumental Activities of Daily Living as a Potential Marker of Frailty A Study of 7364 Community-Dwelling Elderly Women (the EPIDOS Study)

paper_content:

Background. A number of clinical conditions have been shown to be associated with frailty in elderly people. We hypothesized that incapacities on the Instrumental Activities of Daily Living (IADLs) scale could make it possible to identify this population. We investigated the associations between IADL incapacities and the various known correlates of frailty in a cohort of community-dwelling elderly women. Methods. Cross-sectional analysis was carried out on the data from 7364 women aged over 75 years (EPIDOS Study). The IADL was the dependent variable. Sociodemographic, medical, and psychological performance measures were obtained during an assessment visit. Falls in the previous 6 months and fear of falling were also ascertained. Body composition was measured by dual-energy x-ray absorptiometry. The factors associated with disability in at least one IADL were included in a logistic regression model. Results. Thirty-two percent of the population studied had disability in at least one IADL item. This group was significantly older (81.7 � 4.1 yr vs 79.8 � 3.4 yr), had more frequent histories of heart disease, stroke, depression or diabetes, and was socially less active ( p � .001). These associations persisted after multivariate analysis. Cognitive impairment as assessed by the Pfeiffer test (Pfeiffer score � 8) was closely associated with disabilities on the IADL (OR 3.101, 95% confidence interval [CI] 2.19‐4.38). Falls and fear of falling were also more frequent in the group of women with an abnormal IADL ( p � .001) but only fear of falling remained significantly associated with incapacities on at least one IADL item after logistic regression (OR 1.47, 95% CI 1.28‐1.69). Women with disability on at least one IADL item also had lower bone mineral density, this was independent of the other factors. Conclusion. Our results confirmed that women with disability on at least one IADL item are frailer because they had more associated disorders, poorer cognitive function and more frequent falls. Disabilities on this scale could be a good tool for identifying individuals at risk of frailty among elderly persons living at home and in apparent good health. This finding requires confirmation by longitudinal studies.
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paper_title: Automatic Assessment of Cognitive Impairment Through Electronic Observation of Object Usage

paper_content:

Indications of cognitive impairments such as dementia and traumatic brain injury (TBI) are often subtle and may be frequently missed by primary care physicians. We describe an experiment where we unobtrusively collected sensor data as individuals with TBI performed a routine daily task (making coffee). We computed a series of four features of the sensor data that were increasingly representative of the task, and that we hypothesized might correlate with severity of cognitive impairment. Our main result is a significant correlation between the most representational feature and an apparent indicator of general neuropsychological integrity, namely, the first principal component of a standard suite of neuropsychological assessments. We also found suggestive but preliminary evidence of correlations between the computed features and a number of the individual tests in the assessment suite; this evidence can be used as the basis of larger-scale studies to validate significance.
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paper_title: An Investigative Study for Smart Home Security: Issues, Challenges and Countermeasures

paper_content:

Home security should be a top concern for everyone who owns or rents a home. Moreover, safe and secure residential space is the necessity of every individual as most of the family members are working. The home is left unattended for most of the day-time and home invasion crimes are at its peak as constantly monitoring of the home is difficult. Another reason for the need of home safety is specifically when the elderly person is alone or the kids are with baby-sitter and servant. Home security system i.e. HomeOS is thus applicable and desirable for resident’s safety and convenience. This will be achieved by turning your home into a smart home by intelligent remote monitoring. Smart home comes into picture for the purpose of controlling and monitoring the home. It will give you peace of mind, as you can have a close watch and stay connected anytime, anywhere. But, is common man really concerned about home security? An investigative study was done by conducting a survey to get the inputs from different people from diverse backgrounds. The main motivation behind this survey was to make people aware of advanced HomeOS and analyze their need for security. This paper also studied the necessity of HomeOS investigative study in current situation where the home burglaries are rising at an exponential rate. In order to arrive at findings and conclusions, data were analyzed. The graphical method was employed to identify the relative significance of home security. From this analysis, we can infer that the cases of having kids and aged person at home or location of home contribute significantly to the need of advanced home security system. At the end, the proposed system model with its flow and the challenges faced while implementing home security systems are also discussed.
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paper_title: Unobtrusive In-Home Detection of Time Spent Out-of-Home With Applications to Loneliness and Physical Activity

paper_content:

Loneliness is a common condition in elderly associated with severe health consequences including increased mortality, decreased cognitive function, and poor quality of life. Identifying and assisting lonely individuals is therefore increasingly important-especially in the home setting-as the very nature of loneliness often makes it difficult to detect by traditional methods. One critical component in assessing loneliness unobtrusively is to measure time spent out-of-home, as loneliness often presents with decreased physical activity, decreased motor functioning, and a decline in activities of daily living, all of which may cause decrease in the amount of time spent outside the home. Using passive and unobtrusive in-home sensing technologies, we have developed a methodology for detecting time spent out-of-home based on logistic regression. Our approach was both sensitive (0.939) and specific (0.975) in detecting time out-of-home across over 41 000 epochs of data collected from four subjects monitored for at least 30 days each in their own homes. In addition to linking time spent out-of-home to loneliness, (r = -0.44, p = 0.011) as measured by the UCLA Loneliness Index, we demonstrate its usefulness in other applications such as uncovering general behavioral patterns of elderly and exploring the link between time spent out-of-home and physical activity ( r = 0.415, p = 0.031), as measured by the Berkman Social Disengagement Index. Language: en
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paper_title: Activities of daily living studied longitudinally between 70 and 76 years of age

paper_content:

Functional performance, i.e. personal and instrumental activities of daily living (P-ADL, I-ADL), was studied in a population of 70-year-old persons followed to the age of 76, and with an intervention period included between the ages of 70 and 73. At age 70 (n = 617), 83% were independent, 13% were dependent in I-ADL and 4% dependent in I + P-ADL. Among the independent subjects, the 6-year outcome in mortality was 13%. Dependence at 70 predicted mortality as well as institutionalization, and the risk was higher for those dependent in P + I-ADL than for persons dependent in I-ADL only. Of participant survivors the incidence of disability was 30% (8% between 70 and 73, 26% between 73 and 76 years of age) and was dominated by dependence in I-ADLs. The intervention did not lead to less dependence in ADL at age 76. Gender differences were found at age 76 in cooking, bathing and dressing, males being more dependent in such activities. At 70, 73 and 76 years of age, assistance given by relatives dominated.
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paper_title: In-home walking speeds and variability trajectories associated with mild cognitive impairment

paper_content:

Objective:To determine whether unobtrusive long-term in-home assessment of walking speed and its variability can distinguish those with mild cognitive impairment (MCI) from those with intact cognition. Methods:Walking speed was assessed using passive infrared sensors fixed in series on the ceiling of the homes of elderly individuals participating in the Intelligent Systems for Assessing Aging Change (ISAAC) cohort study. Latent trajectory models were used to analyze weekly mean speed and walking speed variability (coefficient of variation [COV]). Results:ISAAC participants living alone included 54 participants with intact cognition, 31 participants with nonamnestic MCI (naMCI), and 8 participants with amnestic MCI at baseline, with a mean follow-up of 2.6 ± 1.0 years. Trajectory models identified 3 distinct trajectories (fast, moderate, and slow) of mean weekly walking speed. Participants with naMCI were more likely to be in the slow speed group than in the fast ( p = 0.01) or moderate ( p = 0.04) speed groups. For COV, 4 distinct trajectories were identified: group 1, the highest baseline and increasing COV followed by a sharply declining COV; groups 2 and 3, relatively stable COV; and group 4, the lowest baseline and decreasing COV. Participants with naMCI were more likely to be members of either highest or lowest baseline COV groups (groups 1 or 4), possibly representing the trajectory of walking speed variability for early- and late-stage MCI, respectively. Conclusion:Walking speed and its daily variability may be an early marker of the development of MCI. These and other real-time measures of function may offer novel ways of detecting transition phases leading to dementia. GLOSSARYaMCI: amnestic mild cognitive impairmentBIC: Bayesian information criteriaCIRS: Cumulative Illness Rating ScaleCOV: coefficient of variationFAQ: Functional Activities QuestionnaireISAAC: Intelligent Systems for Assessing Aging ChangeMCI: mild cognitive impairmentnaMCI: nonamnestic mild cognitive impairmentUPDRS: Unified Parkinson's Disease Rating ScaleWAIS: Wechsler Adult Intelligence ScaleWMH: white matter hyperintensity
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paper_title: One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes

paper_content:

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step toward automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.
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paper_title: Activities of daily living in patients with dementia: clinical relevance, methods of assessment and effects of treatment.

paper_content:

Disability, characterised by the loss of ability to perform activities of daily living (ADL), is a defining feature of dementia that results in growing caregiver burden and the eventual need for alternative care or nursing home placement. Functional decline in patients with dementia can also result from causes other than dementia, such as comorbid medical and psychiatric illnesses and sensory impairment. ADL consists of instrumental ADL (IADL) [complex higher order skills, such as managing finances] and basic ADL (BADL) [self-maintenance skills, such as bathing]. Assessment of IADL and BADL is recommended to establish a diagnosis of dementia. Functional assessment also helps the healthcare provider to offer appropriate counselling regarding safety concerns and need for custodial care. Functional capacity measures have been used increasingly in pharmacological trials of patients with Alzheimer's disease (AD) and related dementias, although at the present time these measures are generally not primary outcome measures. Functional impairment is not a uniform construct; rather, it is multifaceted and can be measured with various clinical instruments. Many scales have been validated for use in patients with AD for characterising functional impairment and evaluating the efficacy of treatment. Research to date indicates that cholinesterase inhibitors have the potential for modest but meaningful beneficial effects on ADL in patients with mild-to-moderate AD. Memantine also has promising beneficial effects on functional abilities in persons with moderate-to-severe AD. Assessment of ADL as a primary efficacy measure using a validated scale that is non-gender biased and cross-nationally relevant is recommended in new treatment trials of patients with AD and related dementias.
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paper_title: Modeling patterns of activities using activity curves

paper_content:

Pervasive computing offers an unprecedented opportunity to unobtrusively monitor behavior and use the large amount of collected data to perform analysis of activity-based behavioral patterns. In this paper, we introduce the notion of an activity curve, which represents an abstraction of an individual's normal daily routine based on automatically-recognized activities. We propose methods to detect changes in behavioral routines by comparing activity curves and use these changes to analyze the possibility of changes in cognitive or physical health. We demonstrate our model and evaluate our change detection approach using a longitudinal smart home sensor dataset collected from 18 smart homes with older adult residents. Finally, we demonstrate how big data-based pervasive analytics such as activity curve-based change detection can be used to perform functional health assessment. Our evaluation indicates that correlations do exist between behavior and health changes and that these changes can be automatically detected using smart homes, machine learning, and big data-based pervasive analytics.
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paper_title: Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour

paper_content:

In this paper, we have described a solution for supporting independent living of the elderly by means of equipping their home with a simple sensor network to monitor their behaviour. Standard home automation sensors including movement sensors and door entry point sensors are used. By monitoring the sensor data, important information regarding any anomalous behaviour will be identified. Different ways of visualizing large sensor data sets and representing them in a format suitable for clustering the abnormalities are also investigated. In the latter part of this paper, recurrent neural networks are used to predict the future values of the activities for each sensor. The predicted values are used to inform the caregiver in case anomalous behaviour is predicted in the near future. Data collection, classification and prediction are investigated in real home environments with elderly occupants suffering from dementia.
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paper_title: Four Instrumental Activities of Daily Living Score as a Predictor of One-year Incident Dementia

paper_content:

The purpose of the study was to estimate the risk of one-year incident dementia in elderly community dwellers, according to their performance on a four Instrumental Activities of Daily Living (IADL) score. From a random sample of 2792 French elderly community dwellers included in the PAQUID study, 1804 non-demented subjects were interviewed by specifically trained psychologists about their performance on telephone use, use of transport, responsibility for medication intake, and budget management, and then followed up. One year later 21 of these subjects had a diagnosis of incident dementia, according to DSM-IIIR criteria and confirmed by a neurologist. The odds ratio for incident dementia, adjusted for age, increased from 10.6 for subjects dependent in one IADL to 318.4 for those dependent in the four IADLs (p < 0.0001), compared with independent subjects. These four IADLs could be useful for screening subjects at high risk of incident dementia.
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paper_title: Algorithm to automatically detect abnormally long periods of inactivity in a home

paper_content:

An algorithm has been developed to automatically construct individual models of normal activity within a home using motion sensor data. Alerts can be generated when a period of inactivity exceeds a normal length for a particular residence. Alerting frequency has been optimized on a total of 650 days of real data from four homes of seniors who live independently. Results suggest that an inexpensive system that does not require the occupant to push any buttons or wear any devices could nonetheless alert within hours if a senior is unusually inactive. Further, such algorithms may facilitate widespread deployment of smart home technology to persons with different behavior patterns and home layouts by using automatic learning in place of potentially tedious manual configuration.
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paper_title: Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: A machine learning approach

paper_content:

Automatic fall detection will promote independent living and reduce the consequences of falls in the elderly by ensuring people can confidently live safely at home for linger. In laboratory studies inertial sensor technology has been shown capable of distinguishing falls from normal activities. However less than 7% of fall-detection algorithm studies have used fall data recorded from elderly people in real life. The FARSEEING project has compiled a database of real life falls from elderly people, to gain new knowledge about fall events and to develop fall detection algorithms to combat the problems associated with falls. We have extracted 12 different kinematic, temporal and kinetic related features from a data-set of 89 real-world falls and 368 activities of daily living. Using the extracted features we applied machine learning techniques and produced a selection of algorithms based on different feature combinations. The best algorithm employs 10 different features and produced a sensitivity of 0.88 and a specificity of 0.87 in classifying falls correctly. This algorithm can be used distinguish real-world falls from normal activities of daily living in a sensor consisting of a tri-axial accelerometer and tri-axial gyroscope located at L5.
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paper_title: Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

paper_content:

Faced with the growing population of seniors, developed countries need to establish new healthcare systems to ensure the safety of elderly people at home. Computer vision provides a promising solution to analyze personal behavior and detect certain unusual events such as falls. In this paper, a new method is proposed to detect falls by analyzing human shape deformation during a video sequence. A shape matching technique is used to track the person's silhouette along the video sequence. The shape deformation is then quantified from these silhouettes based on shape analysis methods. Finally, falls are detected from normal activities using a Gaussian mixture model. This paper has been conducted on a realistic data set of daily activities and simulated falls, and gives very good results (as low as 0% error with a multi-camera setup) compared with other common image processing methods.
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paper_title: Acoustic fall detection using a circular microphone array

paper_content:

Falling is a common health problem for elderly. It is reported that more than one third of adults 65 and older fall each year in the United States. To address the problem, we are currently developing an acoustic fall detection system, FADE, which automatically detects a fall and reports it to the caregiver. In a previous version, FADE used a 3-microphone linear array to eliminate the false alarms produced by sounds produced well above the floor level. To improve the fall detection in noisy and reverberant environments, we replaced the linear array by an 8-microphone circular array that can provide a better 3-D estimation of the sound location. Preliminary experiments show that the sound location estimation performed by the circular array is reliable and robust to interference. We obtained encouraging classification results on a pilot dataset with 55 falls and 120 non-fall sounds.
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paper_title: A Smart and Passive Floor-Vibration Based Fall Detector for Elderly

paper_content:

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

paper_content:

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: Falls in older people: risk factors and strategies for prevention.

paper_content:

Part I. Epidemiology and Risk Factors for Falls: 1. Epidemiology of falls and fall-related injuries 2. Postural stability and falls 3. Gait patterns and falls 4. Sensory and neuromuscular risk factors for falls 5. Psychological factors and falls 6. Medical risk factors for falls 7. Medications as risk factors for falls 8. Environmental risk factors for falls 9. The relative importance of falls risk factors - an evidence-based summary Part II. Strategies for Prevention: Overview: falls prevention 10. Exercise interventions to prevent falls 11. Exercise interventions to improve physical functioning 12. Medical management of older people at risk of falls 13. Assistive devices and falls prevention 14. Modifying the environment to prevent falls 15. Prevention of falls in hospitals and residential aged care facilities 16. A physiological profile approach to falls risk assessment and prevention 17. Falls prevention strategies - from research into practice Part III. Research Issues in Falls Prevention: 18. Falls in older people: future directions for research.
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paper_title: Fall Detection in Homes of Older Adults Using the Microsoft Kinect

paper_content:

A method for detecting falls in the homes of older adults using the Microsoft Kinect and a two-stage fall detection system is presented. The first stage of the detection system characterizes a person's vertical state in individual depth image frames, and then segments on ground events from the vertical state time series obtained by tracking the person over time. The second stage uses an ensemble of decision trees to compute a confidence that a fall preceded on a ground event. Evaluation was conducted in the actual homes of older adults, using a combined nine years of continuous data collected in 13 apartments. The dataset includes 454 falls, 445 falls performed by trained stunt actors and nine naturally occurring resident falls. The extensive data collection allows for characterization of system performance under real-world conditions to a degree that has not been shown in other studies. Cross validation results are included for standing, sitting, and lying down positions, near (within 4 m) versus far fall locations, and occluded versus not occluded fallers. The method is compared against five state-of-the-art fall detection algorithms and significantly better results are achieved.
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paper_title: SVM to detect the presence of visitors in a smart home environment

paper_content:

With the rising age of the population, there is increased need to help elderly maintain their independence. Smart homes, employing passive sensor networks and pervasive computing techniques, enable the unobtrusive assessment of activities and behaviors of the elderly which can be useful for health state assessment and intervention. Due to the multiple health benefits associated with socializing, accurately tracking whether an individual has visitors to their home is one of the more important aspects of elders' behaviors that could be assessed with smart home technology. With this goal, we have developed a preliminary SVM model to identify periods where untagged visitors are present in the home. Using the dwell time, number of sensor firings, and number of transitions between major living spaces (living room, dining room, kitchen and bathroom) as features in the model, and self report from two subjects as ground truth, we were able to accurately detect the presence of visitors in the home with a sensitivity and specificity of 0.90 and 0.89 for subject 1, and of 0.67 and 0.78 for subject 2, respectively. These preliminary data demonstrate the feasibility of detecting visitors with in-home sensor data, but highlight the need for more advanced modeling techniques so the model performs well for all subjects and all types of visitors.
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paper_title: Unobtrusive In-Home Detection of Time Spent Out-of-Home With Applications to Loneliness and Physical Activity

paper_content:

Loneliness is a common condition in elderly associated with severe health consequences including increased mortality, decreased cognitive function, and poor quality of life. Identifying and assisting lonely individuals is therefore increasingly important-especially in the home setting-as the very nature of loneliness often makes it difficult to detect by traditional methods. One critical component in assessing loneliness unobtrusively is to measure time spent out-of-home, as loneliness often presents with decreased physical activity, decreased motor functioning, and a decline in activities of daily living, all of which may cause decrease in the amount of time spent outside the home. Using passive and unobtrusive in-home sensing technologies, we have developed a methodology for detecting time spent out-of-home based on logistic regression. Our approach was both sensitive (0.939) and specific (0.975) in detecting time out-of-home across over 41 000 epochs of data collected from four subjects monitored for at least 30 days each in their own homes. In addition to linking time spent out-of-home to loneliness, (r = -0.44, p = 0.011) as measured by the UCLA Loneliness Index, we demonstrate its usefulness in other applications such as uncovering general behavioral patterns of elderly and exploring the link between time spent out-of-home and physical activity ( r = 0.415, p = 0.031), as measured by the Berkman Social Disengagement Index. Language: en
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paper_title: A neural network approach towards reinforcing smart home security

paper_content:

In today's context, it is common to leave the house unattended as people are busy catching up with their tight daily schedule. Therefore, most people have chosen the home security system as the most reliable way to protect their home. However, the existing security mechanism provided by smart home is lack of intelligence for higher level decision making and action taking. Furthermore, the current security management of smart home is usually predefined by user. Thus, for consequences that are out of the defined scopes, chances to trigger false alarm are very high. Besides, the environment of each smart home is difference and the culture is also different from country to country, so the predefined security instructions may not work well or they may not suitable for all scenarios. Therefore, an intelligent algorithm with self learning ability must be adopted for smart home system in order to take adequate actions against the evaluated environment and decide for the necessary actions. Neural networks have been proposed in this paper where they are adaptive statistical models based on an analogy with the structure of the brain. The intelligence of the smart home should be able to analyze the information gathered by the sensors and detectors and to respond based on the analysis and the experiences gained via the neural networks. Neural network is chosen because they have the learning ability to estimate the parameters of some populations using a small number of examples at a time. When come to neural network learning process, there are several learning algorithms that could be applied, which can learn through pattern recognition, movement recognition, action recognition and so forth.
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paper_title: Modeling visit behaviour in smart homes using unsupervised learning

paper_content:

Many algorithms on health monitoring from ambient sensor networks assume that only a single person is present in the home. We present an unsupervised method that models visit behaviour. A Markov modulated multidimensional non-homogeneous Poisson process (M3P2) is described that allows us to model weekly and daily variations and to combine multiple data streams, namely the front-door sensor transitions and the general sensor transitions. The results from nine months of sensor data collected in the apartment of an elderly person show that our model outperforms the standard Markov modulated Poisson process (MMPP).
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paper_title: Sensor-based Bayesian detection of anomalous living patterns in a home setting

paper_content:

In this paper, we present an automated behavior analysis system developed to assist the elderly and individuals with disabilities who live alone, by learning and predicting standard behaviors to improve the efficiency of their healthcare. Established behavioral patterns have been recorded using wireless sensor networks composed by several event-based sensors that captured raw measures of the actions of each user. Using these data, behavioral patterns of the residents were extracted using Bayesian statistics. The behavior was statistically estimated based on three probabilistic features we introduce, namely sensor activation likelihood, sensor sequence likelihood, and sensor event duration likelihood. Real data obtained from different home environments were used to verify the proposed method in the individual analysis. The results suggest that the monitoring system can be used to detect anomalous behavior signs which could reflect changes in health status of the user, thus offering an opportunity to intervene if required.
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paper_title: Anomaly Detection in Elderly Daily Behavior in Ambient Sensing Environments

paper_content:

Current ubiquitous computing applications for smart homes aim to enhance people’s daily living respecting age span. Among the target groups of people, elderly are a population eager for “choices for living arrangements”, which would allow them to continue living in their homes but at the same time provide the health care they need. Given the growing elderly population, there is a need for statistical models able to capture the recurring patterns of daily activity life and reason based on this information. We present an analysis of real-life sensor data collected from 40 different households of elderly people, using motion, door and pressure sensors. Our objective is to automatically observe and model the daily behavior of the elderly and detect anomalies that could occur in the sensor data. For this purpose, we first introduce an abstraction layer to create a common ground for home sensor configurations. Next, we build a probabilistic spatio-temporal model to summarize daily behavior. Anomalies are then defined as significant changes from the learned behavioral model and detected using a cross-entropy measure. We have compared the detected anomalies with manually collected annotations and the results show that the presented approach is able to detect significant behavioral changes of the elderly.
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paper_title: Anomaly detection: A survey

paper_content:

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
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paper_title: Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare

paper_content:

Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR).
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paper_title: Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour

paper_content:

In this paper, we have described a solution for supporting independent living of the elderly by means of equipping their home with a simple sensor network to monitor their behaviour. Standard home automation sensors including movement sensors and door entry point sensors are used. By monitoring the sensor data, important information regarding any anomalous behaviour will be identified. Different ways of visualizing large sensor data sets and representing them in a format suitable for clustering the abnormalities are also investigated. In the latter part of this paper, recurrent neural networks are used to predict the future values of the activities for each sensor. The predicted values are used to inform the caregiver in case anomalous behaviour is predicted in the near future. Data collection, classification and prediction are investigated in real home environments with elderly occupants suffering from dementia.
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paper_title: Data Mining for Hierarchical Model Creation

paper_content:

In this paper, we examine the problem of learning inhabitant behavioral models in intelligent environments. We maintain that inhabitant interactions in smart environments can be automated using a data-driven approach to generate hierarchical inhabitant models and learn decision policies. To validate this hypothesis, we have designed the ProPHeT decision-learning algorithm that learns a strategy for controlling a smart environment based on sensor observation, power line control, and the generated hierarchical model. The performance of the algorithm is evaluated using real data collected from our MavHome smart home and smart office environments.
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paper_title: Human activity analysis: A review

paper_content:

Human activity recognition is an important area of computer vision research. Its applications include surveillance systems, patient monitoring systems, and a variety of systems that involve interactions between persons and electronic devices such as human-computer interfaces. Most of these applications require an automated recognition of high-level activities, composed of multiple simple (or atomic) actions of persons. This article provides a detailed overview of various state-of-the-art research papers on human activity recognition. We discuss both the methodologies developed for simple human actions and those for high-level activities. An approach-based taxonomy is chosen that compares the advantages and limitations of each approach. Recognition methodologies for an analysis of the simple actions of a single person are first presented in the article. Space-time volume approaches and sequential approaches that represent and recognize activities directly from input images are discussed. Next, hierarchical recognition methodologies for high-level activities are presented and compared. Statistical approaches, syntactic approaches, and description-based approaches for hierarchical recognition are discussed in the article. In addition, we further discuss the papers on the recognition of human-object interactions and group activities. Public datasets designed for the evaluation of the recognition methodologies are illustrated in our article as well, comparing the methodologies' performances. This review will provide the impetus for future research in more productive areas.
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paper_title: Forecasting behavior in smart homes based on sleep and wake patterns

paper_content:

BACKGROUND ::: The goal of this research is to use smart home technology to assist people who are recovering from injuries or coping with disabilities to live independently. ::: ::: ::: OBJECTIVE ::: We introduce an algorithm to model and forecast wake and sleep behaviors that are exhibited by the participant. Furthermore, we propose that sleep behavior is impacted by and can be modeled from wake behavior, and vice versa. ::: ::: ::: METHODS ::: This paper describes the Behavior Forecasting (BF) algorithm. BF consists of 1) defining numeric values that reflect sleep and wake behavior, 2) forecasting wake and sleep values from past behavior, 3) analyzing the effect of wake behavior on sleep and vice versa, and 4) improving prediction performance by using both wake and sleep scores. ::: ::: ::: RESULTS ::: The BF method was evaluated with data collected from 20 smart homes. We found that regardless of the forecasting method utilized, wake behavior and sleep behavior can be modeled with a minimum accuracy of 84%. Additionally, normalizing the wake and sleep scores drastically improves the accuracy to 99%. ::: ::: ::: CONCLUSIONS ::: The results show that we can effectively model wake and sleep behaviors in a smart environment. Furthermore, wake behaviors can be predicted from sleep behaviors and vice versa.
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paper_title: Multi-class active learning for image classification

paper_content:

One of the principal bottlenecks in applying learning techniques to classification problems is the large amount of labeled training data required. Especially for images and video, providing training data is very expensive in terms of human time and effort. In this paper we propose an active learning approach to tackle the problem. Instead of passively accepting random training examples, the active learning algorithm iteratively selects unlabeled examples for the user to label, so that human effort is focused on labeling the most “useful” examples. Our method relies on the idea of uncertainty sampling, in which the algorithm selects unlabeled examples that it finds hardest to classify. Specifically, we propose an uncertainty measure that generalizes margin-based uncertainty to the multi-class case and is easy to compute, so that active learning can handle a large number of classes and large data sizes efficiently. We demonstrate results for letter and digit recognition on datasets from the UCI repository, object recognition results on the Caltech-101 dataset, and scene categorization results on a dataset of 13 natural scene categories. The proposed method gives large reductions in the number of training examples required over random selection to achieve similar classification accuracy, with little computational overhead.
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paper_title: Experiments with a New Boosting Algorithm

paper_content:

In an earlier paper, we introduced a new "boosting" algorithm called AdaBoost which, theoretically, can be used to significantly reduce the error of any learning algorithm that con- sistently generates classifiers whose performance is a little better than random guessing. We also introduced the related notion of a "pseudo-loss" which is a method for forcing a learning algorithm of multi-label concepts to concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems. We performed two sets of experiments. The first set compared boosting to Breiman's "bagging" method when used to aggregate various classifiers (including decision trees and single attribute- value tests). We compared the performance of the two methods on a collection of machine-learning benchmarks. In the second set of experiments, we studied in more detail the performance of boosting using a nearest-neighbor classifier on an OCR problem.
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paper_title: A Framework for Supervising Lifestyle Diseases Using Long-Term Activity Monitoring

paper_content:

Activity monitoring of a person for a long-term would be helpful for controlling lifestyle associated diseases. Such diseases are often linked with the way a person lives. An unhealthy and irregular standard of living influences the risk of such diseases in the later part of one's life. The symptoms and the initial signs of these diseases are common to the people with irregular lifestyle. In this paper, we propose a novel healthcare framework to manage lifestyle diseases using long-term activity monitoring. The framework recognizes the user's activities with the help of the sensed data in runtime and reports the irregular and unhealthy activity patterns to a doctor and a caregiver. The proposed framework is a hierarchical structure that consists of three modules: activity recognition, activity pattern generation and lifestyle disease prediction. We show that it is possible to assess the possibility of lifestyle diseases from the sensor data. We also show the viability of the proposed framework.
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paper_title: Typical Behavior Patterns Extraction and Anomaly Detection Algorithm Based on Accumulated Home Sensor Data

paper_content:

In this paper, we propose a method consists of two components, behavior patterns extraction and anomaly detection algorithm in daily life. To begin with, sensor data are accumulated in a room environment and behavior description labels are assigned for each data segment using HMM(hidden Markov model) and k-means method. An HMM is composed every day based on sensor data segments of the day. The behavior description label at each time segment is determined by likelihood of the segment computed using the HMM. In anomaly detection step, typical behavior sequences are acquired using probabilistic density of behavior occurrence and behavior successive time. Each probabilistic density is composed based on accumulating labeled- data using sequential discounting Laplace estimation and sequential discounting expectation and maximization algorithms. When a new datum comes, if typical behavior data change largely, the data is detected as anomaly. The proposed method is verified by a long-time activity detection sensor data taken at a house of elderly person.
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paper_title: Holmes: A Comprehensive Anomaly Detection System for Daily In-home Activities

paper_content:

Advances in wireless sensor networks have enabled the monitoring of daily activities of elderly people. The goal of these monitoring applications is to learn normal behavior in terms of daily activities and look for any deviation, i.e., Anomalies, so that alerts can be sent to relatives or caregivers. However, human behavior is very complex, and many existing anomaly detection systems are too simplistic which cause many false alarms, resulting in unreliable systems. We present Holmes, a comprehensive anomaly detection system for daily in-home activities. Holmes accurately learns a resident's normal behavior by considering variability in daily activities based not only on a per day basis, but also considering specific days of the week, different time periods such as per week and per month, and collective, temporal, and correlation based features. This approach of learning complicated normal behaviors reduces false alarms. Also, based on resident and expert feedback, Holmes learns semantic rules that explain specific variations of activities in specific scenarios to further reduce false alarms. We evaluate Holmes using data collected from our own deployed system, public data sets, and data collected by a senior safety system provider company from an elderly resident's home. Our evaluation shows that compared to state of the art systems, Holmes reduces false positives and false negatives by at least 46% and 27%, respectively.
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paper_title: Rare Event Detection and Propagation in Wireless Sensor Networks

paper_content:

Rarely occurring events present unique challenges to energy constrained systems designed for long term sensing of their occurrence or effect. Unlike periodic sampling or query based sensing systems, longevity cannot be achieved simply by adjusting the sensing nodes’ duty cycle until an equitable balance between data density and network lifetime is established. The low probability of occurrence and random nature of rare events makes it difficult to guarantee duty cycled battery powered sensing nodes will be energised when events occur. Equally, it is usually considered impractical to leave the sensing nodes energised at all times if the network is to have an acceptably long operational life. In the past decade and a half, wireless sensor network research has addressed this aspect of rare event sensing by investigating techniques including synchronised duty cycling of redundant nodes, passive sensing, duplicate message suppression, and energy efficient network protocols. Researchers have also demonstrated the efficacy of harvesting energy from the environment to extend operational life. Here we survey existing rare event detection and propagation techniques, and suggest areas suitable for continued research.
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paper_title: One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes

paper_content:

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step toward automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.
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paper_title: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition

paper_content:

Approaches and algorithms for activity recognition have recently made substantial progress due to advancements in pervasive and mobile computing, smart environments and ambient assisted living. Nevertheless, it is still difficult to achieve real-time continuous activity recognition as sensor data segmentation remains a challenge. This paper presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The paper first analyzes the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of knowledge-driven activity recognition based on ontologies. The presented approach has been implemented in a prototype system and evaluated in a number of experiments. Results have shown average recognition accuracy above 83% in all experiments for real time activity recognition, which proves the approach and the underlying model.
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paper_title: Sensor-Based Activity Recognition

paper_content:

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: Optimised probabilistic active learning ( OPAL ) For fast , non-myopic , cost-sensitive active classification

paper_content:

In contrast to ever increasing volumes of automatically generated data, human annotation capacities remain limited. Thus, fast active learning approaches that allow the efficient allocation of annotation efforts gain in importance. Furthermore, cost-sensitive applications such as fraud detection pose the additional challenge of differing misclassification costs between classes. Unfortunately, the few existing cost-sensitive active learning approaches rely on time-consuming steps, such as performing self-labelling or tedious evaluations over samples. We propose a fast, non-myopic, and cost-sensitive probabilistic active learning approach for binary classification. Our approach computes the expected reduction in misclassification loss in a labelling candidate's neighbourhood. We derive and use a closed-form solution for this expectation, which considers the possible values of the true posterior of the positive class at the candidate's position, its possible label realisations, and the given labelling budget. The resulting myopic algorithm runs in the same linear asymptotic time as uncertainty sampling, while its non-myopic counterpart requires an additional factor of $$O(m \cdot \log m)$$O(m·logm) in the budget size. The experimental evaluation on several synthetic and real-world data sets shows competitive or better classification performance and runtime, compared to several uncertainty sampling- and error-reduction-based active learning strategies, both in cost-sensitive and cost-insensitive settings.
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paper_title: A Review on Video-Based Human Activity Recognition

paper_content:

This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems.
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paper_title: Hidden State Conditional Random Field for Abnormal Activity Recognition in Smart Homes

paper_content:

As the number of elderly people has increased worldwide, there has been a surge of research into assistive technologies to provide them with better care by recognizing their normal and abnormal activities. However, existing abnormal activity recognition (AAR) algorithms rarely consider sub-activity relations when recognizing abnormal activities. This paper presents an application of the Hidden State Conditional Random Field (HCRF) method to detect and assess abnormal activities that often occur in elderly persons’ homes. Based on HCRF, this paper designs two AAR algorithms, and validates them by comparing them with a feature vector distance based algorithm in two experiments. The results demonstrate that the proposed algorithms favorably outperform the competitor, especially when abnormal activities have same sensor type and sensor number as normal activities.
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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: Active Learning with Confidence

paper_content:

Active learning is a machine learning approach to achieving high-accuracy with a small amount of labels by letting the learning algorithm choose instances to be labeled. Most of previous approaches based on discriminative learning use the margin for choosing instances. We present a method for incorporating confidence into the margin by using a newly introduced online learning algorithm and show empirically that confidence improves active learning.
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paper_title: Unsupervised Rare Pattern Mining: A Survey

paper_content:

Association rule mining was first introduced to examine patterns among frequent items. The original motivation for seeking these rules arose from need to examine customer purchasing behaviour in supermarket transaction data. It seeks to identify combinations of items or itemsets, whose presence in a transaction affects the likelihood of the presence of another specific item or itemsets. In recent years, there has been an increasing demand for rare association rule mining. Detecting rare patterns in data is a vital task, with numerous high-impact applications including medical, finance, and security. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for rare pattern mining. We investigate the problems in finding rare rules using traditional association rule mining. As rare association rule mining has not been well explored, there is still specific groundwork that needs to be established. We will discuss some of the major issues in rare association rule mining and also look at current algorithms. As a contribution, we give a general framework for categorizing algorithms: Apriori and Tree based. We highlight the differences between these methods. Finally, we present several real-world application using rare pattern mining in diverse domains. We conclude our survey with a discussion on open and practical challenges in the field.
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paper_title: Modelling of Behavioural Patterns for Abnormality Detection in the Context of Lifestyle Reassurance

paper_content:

As a consequence of the growing number of older and vulnerable people, health and care providers are increasingly considering new approaches to support people in their own homes. In this context, lifestyle reassurance analyses data collected from a range of sensors to determine a person's `routine' and highlights any important changes. This paper proposes a new approach for detection of individual deviation from normal behaviour focusing on building probabilistic models of behaviour based on a set of activity attributes. Models are trained using only normal behaviour. Variations from the models are considered as abnormal behaviours and these can be highlighted for subsequent review or intervention. Case study experiments with real life data suggest that some users' activities follow regular patterns and that these patterns can be learned with probabilistic models.
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paper_title: An Introduction to the Bootstrap

paper_content:

Introduction The Accuracy of a Sample Mean Random Samples and Probabilities The Empirical Distribution Function and the Plug-In Principle Standard Errors and Estimated Standard Errors The Bootstrap Estimate of Standard Error Bootstrap Standard Errors: Some Examples More Complicated Data Structures Regression Models Estimates of Bias The Jackknife Confidence Intervals Based on Bootstrap "Tables" Confidence Intervals Based on Bootstrap Percentiles Better Bootstrap Confidence Intervals Permutation Tests Hypothesis Testing with the Bootstrap Cross-Validation and Other Estimates of Prediction Error Adaptive Estimation and Calibration Assessing the Error in Bootstrap Estimates A Geometrical Representation for the Bootstrap and Jackknife An Overview of Nonparametric and Parametric Inference Further Topics in Bootstrap Confidence Intervals Efficient Bootstrap Computations Approximate Likelihoods Bootstrap Bioequivalence Discussion and Further Topics Appendix: Software for Bootstrap Computations References
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paper_title: A Smart Spoofing Face Detector by Display Features Analysis

paper_content:

In this paper, a smart face liveness detector is proposed to prevent the biometric system from being “deceived” by the video or picture of a valid user that the counterfeiter took with a high definition handheld device (e.g., iPad with retina display). By analyzing the characteristics of the display platform and using an expert decision-making core, we can effectively detect whether a spoofing action comes from a fake face displayed in the high definition display by verifying the chromaticity regions in the captured face. That is, a live or spoof face can be distinguished precisely by the designed optical image sensor. To sum up, by the proposed method/system, a normal optical image sensor can be upgraded to a powerful version to detect the spoofing actions. The experimental results prove that the proposed detection system can achieve very high detection rate compared to the existing methods and thus be practical to implement directly in the authentication systems.
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paper_title: Virtual U: defeating face liveness detection by building virtual models from your public photos

paper_content:

In this paper, we introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, we show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. Our framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g., raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. The synthetic face of the user is displayed on the screen of the VR device, and as the device rotates and translates in the real world, the 3D face moves accordingly. To an observing face authentication system, the depth and motion cues of the display match what would be expected for a human face. ::: ::: We argue that such VR-based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera-based authentication systems: Unless they incorporate other sources of verifiable data, systems relying on color image data and camera motion are prone to attacks via virtual realism. To demonstrate the practical nature of this threat, we conduct thorough experiments using an end-to-end implementation of our approach and show how it undermines the security of several face authentication solutions that include both motion-based and liveness detectors.
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paper_title: Using malware for software-defined networking–based smart home security management through a taint checking approach

paper_content:

Numerous security concerns exist in smart home systems in which Internet of Things devices are connected through a home network to enable control using a centralised gateway with a handset device f...
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paper_title: Graph-Based Anomaly Detection Applied to Homeland Security Cargo Screening.

paper_content:

Protecting our nation’s ports is a critical challenge for homeland security and requires the research, development and deployment of new technologies that will allow for the efficient securing of shipments entering this country. Most approaches look only at statistical irregularities in the attributes of the cargo, and not at the relationships of this cargo to others. However, anomalies detected in these relationships could add to the suspicion of the cargo, and therefore improve the accuracy with which we detect suspicious cargo. This paper proposes an improvement in our ability to detect suspicious cargo bound for the U.S. through a graph based anomaly detection approach. Using anonymized data received from the Department of Homeland Security, we demonstrate the effectiveness of our approach and its usefulness to a homeland security analyst who is tasked with uncovering illegal and potentially dangerous cargo shipments.
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paper_title: A Review on Security in Smart Home Development

paper_content:

A smart home or building is a home or building, usually a new one that is equipped with special structured wiring to enable occupants to remotely control or program an array of automated home electronic devices by entering a single command. Conventional security systems keep homeowners, and their property, safe from intruders. A smart home security system, however, offers many more benefits. Home automation technology notifies homeowners of any problems, so that they can investigate. In this paper, we discuss smart home and security, we also review the tool related to smart home security.
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paper_title: Face Biometrics Under Spoofing Attacks: Vulnerabilities, Countermeasures, Open Issues, and Research Directions

paper_content:

Among tangible threats and vulnerabilities facing current biometric systems are spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access and advantages. Recently, an increasing attention has been given to this research problem. This can be attested by the growing number of articles and the various competitions that appear in major biometric forums. We have recently participated in a large consortium (TABULARASA) dealing with the vulnerabilities of existing biometric systems to spoofing attacks with the aim of assessing the impact of spoofing attacks, proposing new countermeasures, setting standards/protocols, and recording databases for the analysis of spoofing attacks to a wide range of biometrics including face, voice, gait, fingerprints, retina, iris, vein, electro-physiological signals (EEG and ECG). The goal of this position paper is to share the lessons learned about spoofing and anti-spoofing in face biometrics, and to highlight open issues and future directions.
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paper_title: Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures

paper_content:

The electricity industry is now at the verge of a new era—an era that promises, through the evolution of the existing electrical grids to smart grids, more efficient and effective power management, better reliability, reduced production costs, and more environmentally friendly energy generation. Numerous initiatives across the globe, led by both industry and academia, reflect the mounting interest around not only the enormous benefits but also the great risks introduced by this evolution. This paper focuses on issues related to the security of the smart grid and the smart home, which we present as an integral part of the smart grid. Based on several scenarios, we aim to present some of the most representative threats to the smart home/smart grid environment. The threats detected are categorized according to specific security goals set for the smart home/smart grid environment, and their impact on the overall system security is evaluated. A review of contemporary literature is then conducted with the aim of presenting promising security countermeasures with respect to the identified specific security goals for each presented scenario. An effort to shed light on open issues and future research directions concludes this paper.
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paper_title: Graph-based anomaly detection

paper_content:

Anomaly detection is an area that has received much attention in recent years. It has a wide variety of applications, including fraud detection and network intrusion detection. A good deal of research has been performed in this area, often using strings or attribute-value data as the medium from which anomalies are to be extracted. Little work, however, has focused on anomaly detection in graph-based data. In this paper, we introduce two techniques for graph-based anomaly detection. In addition, we introduce a new method for calculating the regularity of a graph, with applications to anomaly detection. We hypothesize that these methods will prove useful both for finding anomalies, and for determining the likelihood of successful anomaly detection within graph-based data. We provide experimental results using both real-world network intrusion data and artificially-created data.
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paper_title: Less is More: Building Selective Anomaly Ensembles

paper_content:

Ensemble techniques for classification and clustering have long proven effective, yet anomaly ensembles have been barely studied. In this work, we tap into this gap and propose a new ensemble approach for anomaly mining, with application to event detection in temporal graphs. Our method aims to combine results from heterogeneous detectors with varying outputs, and leverage the evidence from multiple sources to yield better performance. However, trusting all the results may deteriorate the overall ensemble accuracy, as some detectors may fall short and provide inaccurate results depending on the nature of the data in hand. This suggests that being selective in which results to combine is vital in building effective ensembles---hence "less is more". ::: In this paper we propose SELECT; an ensemble approach for anomaly mining that employs novel techniques to automatically and systematically select the results to assemble in a fully unsupervised fashion. We apply our method to event detection in temporal graphs, where SELECT successfully utilizes five base detectors and seven consensus methods under a unified ensemble framework. We provide extensive quantitative evaluation of our approach on five real-world datasets (four with ground truth), including Enron email communications, New York Times news corpus, and World Cup 2014 Twitter news feed. Thanks to its selection mechanism, SELECT yields superior performance compared to individual detectors alone, the full ensemble (naively combining all results), and an existing diversity-based ensemble.
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paper_title: Anomaly detection in wireless sensor networks: A survey

paper_content:

Since security threats to WSNs are increasingly being diversified and deliberate, prevention-based techniques alone can no longer provide WSNs with adequate security. However, detection-based techniques might be effective in collaboration with prevention-based techniques for securing WSNs. As a significant branch of detection-based techniques, the research of anomaly detection in wired networks and wireless ad hoc networks is already quite mature, but such solutions can be rarely applied to WSNs without any change, because WSNs are characterized by constrained resources, such as limited energy, weak computation capability, poor memory, short communication range, etc. The development of anomaly detection techniques suitable for WSNs is therefore regarded as an essential research area, which will enable WSNs to be much more secure and reliable. In this survey paper, a few of the key design principles relating to the development of anomaly detection techniques in WSNs are discussed in particular. Then, the state-of-the-art techniques of anomaly detection in WSNs are systematically introduced, according to WSNs' architectures (Hierarchical/Flat) and detection technique categories (statistical techniques, rule based, data mining, computational intelligence, game theory, graph based, and hybrid, etc.). The analyses and comparisons of the approaches that belong to a similar technique category are represented technically, followed by a brief discussion towards the potential research areas in the near future and conclusion.
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paper_title: Security Analysis of Emerging Smart Home Applications

paper_content:

Recently, several competing smart home programming frameworks that support third party app development have emerged. These frameworks provide tangible benefits to users, but can also expose users to significant security risks. This paper presents the first in-depth empirical security analysis of one such emerging smart home programming platform. We analyzed Samsung-owned SmartThings, which has the largest number of apps among currently available smart home platforms, and supports a broad range of devices including motion sensors, fire alarms, and door locks. SmartThings hosts the application runtime on a proprietary, closed-source cloud backend, making scrutiny challenging. We overcame the challenge with a static source code analysis of 499 SmartThings apps (called SmartApps) and 132 device handlers, and carefully crafted test cases that revealed many undocumented features of the platform. Our key findings are twofold. First, although SmartThings implements a privilege separation model, we discovered two intrinsic design flaws that lead to significant overprivilege in SmartApps. Our analysis reveals that over 55% of SmartApps in the store are overprivileged due to the capabilities being too coarse-grained. Moreover, once installed, a SmartApp is granted full access to a device even if it specifies needing only limited access to the device. Second, the SmartThings event subsystem, which devices use to communicate asynchronously with SmartApps via events, does not sufficiently protect events that carry sensitive information such as lock codes. We exploited framework design flaws to construct four proof-of-concept attacks that: (1) secretly planted door lock codes, (2) stole existing door lock codes, (3) disabled vacation mode of the home, and (4) induced a fake fire alarm. We conclude the paper with security lessons for the design of emerging smart home programming frameworks.
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paper_title: A survey of methods for time series change point detection

paper_content:

Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.
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paper_title: Graph-Based Analysis of Nuclear Smuggling Data

paper_content:

Much of the data that is collected and analyzed today is structural, consisting not only of entities but also of relationships between the entities. As a result, analysis applications rely on automated structural data mining approaches to find patterns and concepts of interest. This ability to analyze structural data has become a particular challenge in many security-related domains. In these domains, focusing on the relationships between entities in the data is critical to detect important underlying patterns. In this study we apply structural data mining techniques to automate analysis of nuclear smuggling data. In particular, we choose to model the data as a graph and use graph-based relational learning to identify patterns and concepts of interest in the data. In this article, we identify the analysis questions that are of importance to security analysts and describe the knowledge representation and data mining approach that we adopt for this challenge. We analyze the results using the Russian nuclear...
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paper_title: Graph based anomaly detection and description: a survey

paper_content:

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised versus (semi-)supervised approaches, for static versus dynamic graphs, for attributed versus plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the `why', of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field.
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<format>
Title: Smart secure homes: a survey of smart home technologies that sense, assess, and respond to security threats

Section 1: Introduction
Description 1: Provide an overview of smart home technologies, their evolution, and their importance in maintaining the safety and security of residents.

Section 2: Scenario 1: Intruder detection
Description 2: Describe a scenario involving the detection of an intruder and illustrate how smart home technology can manage the situation.

Section 3: Scenario 2: Health event detection
Description 3: Discuss the role of smart home technology in detecting health events, using an example to highlight its significance.

Section 4: Scenario 3: Building system failure detection
Description 4: Explain how smart home technology can detect building system failures and mitigate related security threats, using a relevant scenario.

Section 5: Sensing threats
Description 5: Examine the different types of sensing technologies available in smart homes to detect threats, including video cameras, audio sensors, and biometrics.

Section 6: Acting in response to threats
Description 6: Detail the various actions smart homes can take in response to detected threats, such as alerting homeowners and taking autonomous actions.

Section 7: Detecting and assessing threats
Description 7: Discuss methods and technologies used to analyze and assess sensor data to detect potential security threats, with a focus on resident-based and home-based detection.

Section 8: Detecting resident-based target states
Description 8: Highlight approaches used to detect specific resident-based security-related situations, such as periods of inactivity and falls.

Section 9: Detecting home-based target states
Description 9: Explore methods for detecting specific states within the home that indicate security needs, such as the presence of intruders and visitors.

Section 10: Detecting home-based anomalies
Description 10: Investigate techniques for detecting broader classes of anomalies in smart homes, utilizing standard methods and more advanced approaches.

Section 11: Detecting activity anomalies
Description 11: Look into deeper levels of anomaly detection by analyzing activities performed within the home and identifying deviations from normal routines.

Section 12: Security research in related areas
Description 12: Review research in related fields that can contribute to developing secure smart homes, including anomaly detection and hacking prevention techniques.

Section 13: Conclusions and future research directions
Description 13: Summarize the survey's findings, highlight the importance of continuing research, and suggest future directions for enhancing smart home security.
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