Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors
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paper_title: Fall prevention control of passive intelligent walker based on human model

paper_content:

As aging progresses, fall accident of the person using the walker is an acute problem. It is necessary to know the situation of the userpsilas fall to prevent it. In this paper, we propose a method for estimating the userpsilas fall by modeling of the user in real time as a solid body link model, and pay attention to the center of gravity of the model. We also propose a method for controlling a passive intelligent walker to prevent the userpsilas fall according to the support polygon and the walking characteristic of the user. We experimented with passive intelligent walker in which we implement the proposed fall prevention control and show the effectiveness of the proposal method.
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paper_title: A pervasive solution for risk awareness in the context of fall prevention

paper_content:

In the present work, we introduce Fallarm, a pervasive fall prevention solution suitable for hospitals and care facilities, as well as for home settings. We applied a multifaceted intervention strategy based on closed-loop information exchange between proactive and reactive methods: comprehensive assessment protocols determine the individuals' risk of falling; an innovative device continuously monitors subjects' activities, and it provides patients with constant feedback about their actual risk. Thus, it increases their awareness; simultaneously, it realizes measures to prevent adverse events, and it reports any incident and aims to reduce the level of injury. As a result, our solution offers a comprehensive strategy for the remote management of a person's risk of falling 24 hours a day, enabling many vulnerable people to remain living independently. In this paper, we detail the architecture of our system, and we discuss the results of an experimental study we conducted to demonstrate the applicability of Fallarm in both clinical and home settings.
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paper_title: A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

paper_content:

The mobile application is capable of detecting possible falls for elderly, through the use of special sensors. The alert messages contain useful information about the people in danger, such as his/her geo location and also corresponding directions on a map. In occasions of false alerts, the supervised person is given the ability to estimate the value of importance of a possible alert and to stop it before being transmitted. This paper describes system for monitoring and fall detection of ELDERLY PEOPLE using triaxial accelerometer together with ZigBee transceiver to detect fall of ELDERLY PEOPLE. The Accidental Fall Detection System will be able to assist careers as well as the elderly, as the careers will be notified immediately to the intended person. This fall detection system is designed to detect the accidental fall of the elderly and alert the careers or their loved ones via Smart-Messaging Services (SMS) immediately. This fall detection is created using microcontroller technology as the heart of the system, the accelerometer as to detect the sudden movement or fall and the Global System for Mobile (GSM) modem, to send out SMS to the receiver
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paper_title: Automatic Fall Detection and Activity Classification by a Wearable Embedded Smart Camera

paper_content:

Robust detection of events and activities, such as falling, sitting, and lying down, is a key to a reliable elderly activity monitoring system. While fast and precise detection of falls is critical in providing immediate medical attention, other activities like sitting and lying down can provide valuable information for early diagnosis of potential health problems. In this paper, we present a fall detection and activity classification system using wearable cameras. Since the camera is worn by the subject, monitoring is not limited to confined areas, and extends to wherever the subject may go including indoors and outdoors. Furthermore, since the captured images are not of the subject, privacy concerns are alleviated. We present a fall detection algorithm employing histograms of edge orientations and strengths, and propose an optical flow-based method for activity classification. The first set of experiments has been performed with prerecorded video sequences from eight different subjects wearing a camera on their waist. Each subject performed around 40 trials, which included falling, sitting, and lying down. Moreover, an embedded smart camera implementation of the algorithm was also tested on a CITRIC platform with subjects wearing the CITRIC camera, and each performing 50 falls and 30 non-fall activities. Experimental results show the success of the proposed method.
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paper_title: Home environment risk factors for falls in older people and the efficacy of home modifications

paper_content:

Most homes contain potential hazards, and many older people attribute their falls to trips or slips inside the home or immediate home surroundings. However, the existence of home hazards alone is insufficient to cause falls, and the interaction between an older person’s physical abilities and their exposure to environmental stressors appears to be more important. Taking risks or impulsivity may further elevate falls risk. Some studies have found that environmental hazards contribute to falls to a greater extent in older vigorous people than in older frail people. This appears to be due to increased exposure to falls hazards with an increase in the proportion of such falls occurring outside the home. There may also be a non-linear pattern between mobility and falls associated with hazards. Household environmental hazards may pose the greatest risk for older people with fair balance, whereas those with poor balance are less exposed to hazards and those with good mobility are more able to withstand them. Reducing hazards in the home appears not to be an effective falls-prevention strategy in the general older population and those at low risk of falls. Home hazard reduction is effective if targeted at older people with a history of falls and mobility limitations. The effectiveness may depend on the provision of concomitant training for improving transfer abilities and other strategies for effecting behaviour change.
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paper_title: A Survey on Human Activity Recognition using Wearable Sensors

paper_content:

Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.
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paper_title: Human Activity Recognition and Pattern Discovery

paper_content:

In principle, activity recognition can be exploited to great societal benefits, especially in real-life, human centric applications such as elder care and healthcare. This article focused on recognizing simple human activities. Recognizing complex activities remains a challenging and active area of research and the nature of human activities poses different challenges. Human activity understanding encompasses activity recognition and activity pattern discovery. The first focuses on accurate detection of human activities based on a predefined activity model. An activity pattern discovery researcher builds a pervasive system first and then analyzes the sensor data to discover activity patterns.
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paper_title: Data Collection and Analysis

paper_content:

The book entitled “Data Collection and Analysis” consists of thirteen chapters in total dealing with different aspects of research data collection and analysis. The objectives of the book are to equip students to understand, evaluate and use evidence in their academic and professional work. The book is designed for students from a wide range of disciplines (including sociology, social psychology, social policy, criminology, health studies, government and politics) and practitioners and readers in a number of applied areas (for example, nurses and other medical practitioners, social workers and others in the caring professions, workers in the criminal justice system, market researchers, teachers and others in the field of education). Chapter-I looks at the issues which logically and generally in practice precede data collection itself, for example what cases to select, and how the study should be designed while Chapter-II dealt with the methods and problems of designing and undertaking sample surveys. The Chapter concludes that the quality of the inferences being made from a sample will be related to both sample size and sampling method (Page: 52). Chapter-III dealt with observational research. It defines the concept observation (Page: 57) and introduced researchers and readers to the different styles and techniques used in observational research. Chapter-IV is concerned with methods of data collection that explicitly involve interviewing or questioning individuals. The Chapter concludes that it is important to define at the outset what the ideal researcher’s objectives should be when assessing and evaluating published research (Page: 117). Chapter-V focuses on statistical sources and databases. It also examines the implications of technological developments for social research (Page: 122). The Chapter concludes that the most obvious consequences of the growth in information services is the growing number of research reports that can be produced based wholly or in part upon existing sources of information (Page: 136). Chapter-VI is studying documentary sources in considering principles to evaluate existing sources as data with focus on documentary sources in the traditional sense of textual documents which are written. Chapter-VII focuses on process that receives scant attention in many research reports: namely, the process of transforming data into variables that can be analysed to produce the information found in the results sections of such reports. In other words the Chapter looks at the extent to which the data on which research arguments are based are not ‘found in the world’, but are constructed by the researcher(s). The Chapter concludes that the majority of data sets are re-coded, re-weighted and ‘manipulated’ or otherwise ‘reinterpreted’ in a parsimonious way during data handling and coding (Page: 179). Chapter-VIII looks at how figures are laid out in tables and graphs. The contents of this Chapter will help researchers and readers to understand any differences between groups or associations between variables which may be important for the argument of the report. At the end of the Chapter it was pointed out that an observed difference or association in a sample does not prove that such a difference or association exists in the population which the sample represents; differences can occur by chance alone, given sampling error (Page: 218). Chapter-IX looks at a range of ways of outlining differences between groups or association between variables, comparing means, laying out figures in tables, computing correlations or working out lines of regression. The Chapter has concentrated mainly on ‘zero-order’ results: the direct relationship between one or more independent variables, one at time and a dependent variable (Page: 260). Chapter-X looked at the extension of statistical analysis to situations where we need to take account of more than one independent variable: in other words, where researchers need to use multivariate analyses. Chapter-XI looked at some of the strategies used by qualitative researchers for analysing unstructured data. The chapter concentrated in particular on the kind of qualitative data analysis that has been codified by Glaser and Strauss (1967) as grounded theorising, since this represents probably the most common approach in use today. Chapter-XII concerned with the use of documents in social research. The range of documents upon which social scientists have drawn includes diaries, letters, essays, personal notes, biographies and autobiographies, institutional memoranda and reports, and governmental pronouncements as in Green Papers, White Papers and Acts of Parliament. The Chapter conclude that critical research
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paper_title: Data Mining: Practical Machine Learning Tools and Techniques

paper_content:

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
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paper_title: An Introduction to Feature Extraction

paper_content:

This chapter introduces the reader to the various aspects of feature extraction covered in this book. Section 1 reviews definitions and notations and proposes a unified view of the feature extraction problem. Section 2 is an overview of the methods and results presented in the book, emphasizing novel contributions. Section 3 provides the reader with an entry point in the field of feature extraction by showing small revealing examples and describing simple but effective algorithms. Finally, Section 4 introduces a more theoretical formalism and points to directions of research and open problems.
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paper_title: Using feature construction to avoid large feature spaces in text classification

paper_content:

Feature space design is a critical part of machine learning. This is an especially difficult challenge in the field of text classification, where an arbitrary number of features of varying complexity can be extracted from documents as a preprocessing step. A challenge for researchers has consistently been to balance expressiveness of features with the size of the corresponding feature space, due to issues with data sparsity that arise as feature spaces grow larger. Drawing on past successes utilizing genetic programming in similar problems outside of text classification, we propose and implement a technique for constructing complex features from simpler features, and adding these more complex features into a combined feature space which can then be utilized by more sophisticated machine learning classifiers. Applying this technique to a sentiment analysis problem, we show encouraging improvement in classification accuracy, with a small and constant increase in feature space size. We also show that the features we generate carry far more predictive power than any of the simple features they contain.
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paper_title: An Introduction to Feature Extraction

paper_content:

This chapter introduces the reader to the various aspects of feature extraction covered in this book. Section 1 reviews definitions and notations and proposes a unified view of the feature extraction problem. Section 2 is an overview of the methods and results presented in the book, emphasizing novel contributions. Section 3 provides the reader with an entry point in the field of feature extraction by showing small revealing examples and describing simple but effective algorithms. Finally, Section 4 introduces a more theoretical formalism and points to directions of research and open problems.
---
paper_title: An Introduction to Feature Extraction

paper_content:

This chapter introduces the reader to the various aspects of feature extraction covered in this book. Section 1 reviews definitions and notations and proposes a unified view of the feature extraction problem. Section 2 is an overview of the methods and results presented in the book, emphasizing novel contributions. Section 3 provides the reader with an entry point in the field of feature extraction by showing small revealing examples and describing simple but effective algorithms. Finally, Section 4 introduces a more theoretical formalism and points to directions of research and open problems.
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paper_title: Machine Recognition of Human Activities: A Survey

paper_content:

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: Data Mining: Practical Machine Learning Tools and Techniques

paper_content:

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
---
paper_title: Data Mining: Practical Machine Learning Tools and Techniques

paper_content:

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
---
paper_title: Data Mining: Practical Machine Learning Tools and Techniques

paper_content:

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
---
paper_title: Data Mining: Practical Machine Learning Tools and Techniques

paper_content:

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
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paper_title: Training a Support Vector Machine in the Primal

paper_content:

Most literature on support vector machines (SVMs) concentrates on the dual optimization problem. In this letter, we point out that the primal problem can also be solved efficiently for both linear and nonlinear SVMs and that there is no reason for ignoring this possibility. On the contrary, from the primal point of view, new families of algorithms for large-scale SVM training can be investigated.
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paper_title: Data Mining: Practical Machine Learning Tools and Techniques

paper_content:

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
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paper_title: A survey of accuracy evaluation metrics of recommendation tasks

paper_content:

Recommender systems are now popular both commercially and in the research community, where many algorithms have been suggested for providing recommendations. These algorithms typically perform differently in various domains and tasks. Therefore, it is important from the research perspective, as well as from a practical view, to be able to decide on an algorithm that matches the domain and the task of interest. The standard way to make such decisions is by comparing a number of algorithms offline using some evaluation metric. Indeed, many evaluation metrics have been suggested for comparing recommendation algorithms. The decision on the proper evaluation metric is often critical, as each metric may favor a different algorithm. In this paper we review the proper construction of offline experiments for deciding on the most appropriate algorithm. We discuss three important tasks of recommender systems, and classify a set of appropriate well known evaluation metrics for each task. We demonstrate how using an improper evaluation metric can lead to the selection of an improper algorithm for the task of interest. We also discuss other important considerations when designing offline experiments.
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paper_title: A Survey on Human Activity Recognition using Wearable Sensors

paper_content:

Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.
---
paper_title: A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

paper_content:

The mobile application is capable of detecting possible falls for elderly, through the use of special sensors. The alert messages contain useful information about the people in danger, such as his/her geo location and also corresponding directions on a map. In occasions of false alerts, the supervised person is given the ability to estimate the value of importance of a possible alert and to stop it before being transmitted. This paper describes system for monitoring and fall detection of ELDERLY PEOPLE using triaxial accelerometer together with ZigBee transceiver to detect fall of ELDERLY PEOPLE. The Accidental Fall Detection System will be able to assist careers as well as the elderly, as the careers will be notified immediately to the intended person. This fall detection system is designed to detect the accidental fall of the elderly and alert the careers or their loved ones via Smart-Messaging Services (SMS) immediately. This fall detection is created using microcontroller technology as the heart of the system, the accelerometer as to detect the sudden movement or fall and the Global System for Mobile (GSM) modem, to send out SMS to the receiver
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paper_title: Computer Vision: Algorithms and Applications

paper_content:

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of recipes, this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.
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paper_title: A Depth-Based Fall Detection System Using a Kinect® Sensor

paper_content:

We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.
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paper_title: A Depth-Based Fall Detection System Using a Kinect® Sensor

paper_content:

We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.
---
paper_title: Automatic Fall Detection and Activity Classification by a Wearable Embedded Smart Camera

paper_content:

Robust detection of events and activities, such as falling, sitting, and lying down, is a key to a reliable elderly activity monitoring system. While fast and precise detection of falls is critical in providing immediate medical attention, other activities like sitting and lying down can provide valuable information for early diagnosis of potential health problems. In this paper, we present a fall detection and activity classification system using wearable cameras. Since the camera is worn by the subject, monitoring is not limited to confined areas, and extends to wherever the subject may go including indoors and outdoors. Furthermore, since the captured images are not of the subject, privacy concerns are alleviated. We present a fall detection algorithm employing histograms of edge orientations and strengths, and propose an optical flow-based method for activity classification. The first set of experiments has been performed with prerecorded video sequences from eight different subjects wearing a camera on their waist. Each subject performed around 40 trials, which included falling, sitting, and lying down. Moreover, an embedded smart camera implementation of the algorithm was also tested on a CITRIC platform with subjects wearing the CITRIC camera, and each performing 50 falls and 30 non-fall activities. Experimental results show the success of the proposed method.
---
paper_title: Data Mining: Practical Machine Learning Tools and Techniques

paper_content:

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
---
paper_title: A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

paper_content:

The mobile application is capable of detecting possible falls for elderly, through the use of special sensors. The alert messages contain useful information about the people in danger, such as his/her geo location and also corresponding directions on a map. In occasions of false alerts, the supervised person is given the ability to estimate the value of importance of a possible alert and to stop it before being transmitted. This paper describes system for monitoring and fall detection of ELDERLY PEOPLE using triaxial accelerometer together with ZigBee transceiver to detect fall of ELDERLY PEOPLE. The Accidental Fall Detection System will be able to assist careers as well as the elderly, as the careers will be notified immediately to the intended person. This fall detection system is designed to detect the accidental fall of the elderly and alert the careers or their loved ones via Smart-Messaging Services (SMS) immediately. This fall detection is created using microcontroller technology as the heart of the system, the accelerometer as to detect the sudden movement or fall and the Global System for Mobile (GSM) modem, to send out SMS to the receiver
---
paper_title: A hybrid human fall detection scheme

paper_content:

This paper presents a novel video-based human fall detection system that can detect a human fall in real-time with a high detection rate. This fall detection system is based on an ingenious combination of skeleton feature and human shape variation, which can efficiently distinguish “fall-down” activities from “fall-like” ones. The experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate.
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paper_title: Automatic Fall Detection and Activity Classification by a Wearable Embedded Smart Camera

paper_content:

Robust detection of events and activities, such as falling, sitting, and lying down, is a key to a reliable elderly activity monitoring system. While fast and precise detection of falls is critical in providing immediate medical attention, other activities like sitting and lying down can provide valuable information for early diagnosis of potential health problems. In this paper, we present a fall detection and activity classification system using wearable cameras. Since the camera is worn by the subject, monitoring is not limited to confined areas, and extends to wherever the subject may go including indoors and outdoors. Furthermore, since the captured images are not of the subject, privacy concerns are alleviated. We present a fall detection algorithm employing histograms of edge orientations and strengths, and propose an optical flow-based method for activity classification. The first set of experiments has been performed with prerecorded video sequences from eight different subjects wearing a camera on their waist. Each subject performed around 40 trials, which included falling, sitting, and lying down. Moreover, an embedded smart camera implementation of the algorithm was also tested on a CITRIC platform with subjects wearing the CITRIC camera, and each performing 50 falls and 30 non-fall activities. Experimental results show the success of the proposed method.
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paper_title: Aging in place: fall detection and localization in a distributed smart camera network

paper_content:

This paper presents the design, implementation and evaluation of a distributed network of smart cameras whose function is to detect and localize falls, an important application in elderly living environments. A network of overlapping smart cameras uses a decentralized procedure for computing inter-image homographies that allows the location of a fall to be reported in 2D world coordinates by calibrating only one camera. Also, we propose a joint routing and homography transformation scheme for multi-hop localization that yields localization errors of less than 2 feet using very low resolution images. Our goal is to demonstrate that such a distributed low-power system can perform adequately in this and related applications. A prototype implementation is given for low-power Agilent/UCLA Cyclops cameras running on the Crossbow MICAz platform. We demonstrate the effectiveness of the fall detection as well as the precision of the localization using a simulation of our sample implementation.
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paper_title: Video based automatic fall detection in indoor environment

paper_content:

An increase in population of elderly people living in isolated environments, leads to the need for an automated monitoring system. Fall is one of the major reasons for death of elderly people. So fall detection is an essential part of an automated indoor monitoring system. Video based automatic fall detection is robust and more reliable than other fall detection methods. Most of the available video based fall detection mechanisms are based on the extraction of motion dynamic features like velocity and intensity gradient of the person in video. These methods are computationally intensive and less accurate. This paper presents an accurate and computationally less intensive approach to detect fall, using only the static features of the person such as aspect ratio and inclination angle. Experimental results show that this method is robust in detecting all kinds of fall.
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paper_title: Motion control of intelligent passive-type Walker for fall-prevention function based on estimation of user state

paper_content:

In this paper, we introduce a passive-type walker using servo brakes referred to as RT Walker. RT Walker realizes several functions such as obstacles/steps avoidance function, path following function, gravity compensation function, variable motion characteristics function, etc., by controlling only servo brakes without using servo motors. These passive-type systems are dependable for using practically in real world environment, because of the passive dynamics with respect to the applied force/moment, simple structure, lightweight, and so on. However, the most serious problem of them is the falling accident of user, because the passive-type systems are lightweight and move easily based on the small force/moment applied by the user unintentionally. In this paper, we pay attention to a method for estimating the human state during the usage of the walker and propose a motion control algorithm for realizing a fall-prevention function based on its human state. We also implement the proposed control methods in RT Walker experimentally and illustrate the validity of them
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paper_title: Fall Detection from Human Shape and Motion History Using Video Surveillance

paper_content:

Nowadays, Western countries have to face the growing population of seniors. New technologies can help people stay at home by providing a secure environment and improving their quality of life. The use of computer vision systems offers a new promising solution to analyze people behavior and detect some unusual events. In this paper, we propose a new method to detect falls, which are one of the greatest risk for seniors living alone. Our approach is based on a combination of motion history and human shape variation. Our algorithm provides promising results on video sequences of daily activities and simulated falls.
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paper_title: Home Alone Faint Detection Surveillance System Using Thermal Camera

paper_content:

Fainting is the unwanted event occur amongst the senior citizens, patients or pregnant women. This event can cause physical injuries or even mental problems. In this paper, we proposed a simple faint detection algorithm apply into a thermal imaging camera to monitor the fainting event. In addition, this system is to allow the immediate treatment can be carried out for life saving purposes when there are people fainting. Experimental results on sample images show that the proposed surveillance system achieved high accuracy of 96.15% for poor lighting condition and 86.19% in indoor in detecting the human faint event.
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paper_title: Pyroelectric IR sensor arrays for fall detection in the older population

paper_content:

Uncooled pyroelectric sensor arrays have been studied over many years for their uses in thermal imaging applications. These arrays will only detect changes in IR flux and so systems based upon them are very good at detecting movements of people in the scene without sensing the background, if they are used in staring mode. Relatively-low element count arrays (16x 16) can be used for a variety of people sensing applications, including people counting (for safety applications), queue monitoring etc. With appropriate signal processing such systems can be also be used for the detection of particular events such as a person falling over. There is a considerable need for automatic fall detection amongst older people, but there are important limitations to some of the current and emerging technologies available for this. Simple sensors, such as I or 2 element pyroelectric infra-red sensors provide crude data that is difficult to interpret; the use of devices worn on the person, such as wrist communicator and motion detectors have potential, but are reliant on the person being able and willing to wear the device; video cameras may be seen as intrusive and require considerable human resources to monitor activity while machine-interpretation of camera images is complex and may be difficult in this application area. The use of a pyroelectric thermal array sensor was seen to have a number of potential benefits. The sensor is wall-mounted and does not require the user to wear a device. It enables detailed analysis of a subject's motion to be achieved locally, within the detector, using only a modest processor. This is possible due to the relative ease with which data from the sensor can be interpreted relative to the data generated by alternative sensors such as video devices. In addition to the cost-effectiveness of this solution, it was felt that the lack of detail in the low-level data, together with the elimination of the need to transmit data outside the detector, would help to avert feelings intrusiveness on the part of the end-user. The main benefits of this type of technology would be for older people who spend time alone in unsupervised environments. This would include people living alone in ordinary housing or in sheltered accommodation (apartment complexes for older people with local warden) and non-communal areas in residential/nursing home environments (e.g. bedrooms and ensuite bathrooms and toilets). This paper will review the development of the array, the pyroelectric ceramic material upon which it is based and the system capabilities. It will present results from the Framework 5 SIMBAD project, which used the system to monitor the movements of elderly people over a considerable period of time.
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paper_title: A smart sensor to detect the falls of the elderly

paper_content:

Falls are a major health hazard for the elderly and a major obstacle to independent living. The estimated incidence of falls for both institutionalized and independent persons aged over 75 is at least 30 percent per year. In the SIMBAD (Smart Inactivity Monitor using Array-Based Detectors) project, we've developed an intelligent fall detector based on a low-cost array of infrared detectors. A field trial and user research indicate that SIMBAD could significantly enhance the functionality and effectiveness of existing monitoring systems and community alarm systems.
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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 pervasive solution for risk awareness in the context of fall prevention

paper_content:

In the present work, we introduce Fallarm, a pervasive fall prevention solution suitable for hospitals and care facilities, as well as for home settings. We applied a multifaceted intervention strategy based on closed-loop information exchange between proactive and reactive methods: comprehensive assessment protocols determine the individuals' risk of falling; an innovative device continuously monitors subjects' activities, and it provides patients with constant feedback about their actual risk. Thus, it increases their awareness; simultaneously, it realizes measures to prevent adverse events, and it reports any incident and aims to reduce the level of injury. As a result, our solution offers a comprehensive strategy for the remote management of a person's risk of falling 24 hours a day, enabling many vulnerable people to remain living independently. In this paper, we detail the architecture of our system, and we discuss the results of an experimental study we conducted to demonstrate the applicability of Fallarm in both clinical and home settings.
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paper_title: Artificial Neural Networks as an alternative to traditional fall detection methods

paper_content:

Falls are common events among older adults and may have serious consequences. Automatic fall detection systems are becoming a popular tool to rapidly detect such events, helping family or health personal to rapidly help the person that falls. This paper presents the results obtained in the process of testing a new fall detection method, based on Artificial Neural Networks (ANN). This method intends to improve fall detection accuracy, by avoiding the traditional threshold - based fall detection methods, and introducing ANN as a suitable option on this application.Also ANN have low computational cost, this characteristic makes them easy to implement on a portable device, comfortable to be wear by the patient.
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paper_title: A reliable fall detection system based on wearable sensor and signal magnitude area for elderly residents

paper_content:

Falls are the primary cause of accidents for elderly people and often result in serious injury and health threats. It is also the main obstacle to independent living for frail and elderly people. A reliable fall detector can reduce the fear of falling and provide the user with the reassurance to maintain an independent lifestyle since the reliable and effective fall detection mechanism will provide urgent medical support and dramatically reduce the cost of medical care. In this work, we propose a fall-detecting system based on a wearable sensor and a real-time fall detection algorithm. We use a waist- mounted tri-axial accelerometer to capture movement data of the human body, and propose a fall detection method that uses the area under a signal magnitude curve to distinguish between falls and daily activities. Experimental results demonstrate the effectiveness of proposed scheme with high reliability and sensitivity on fall detection. The system is not only cost effective but also portable that fulfills the requirements of fall detection.
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paper_title: A two-threshold fall detection algorithm for reducing false alarms

paper_content:

Wireless health monitoring can be used in health care for the aged to support independent living, either at home or in sheltered housing, for as long as possible. The most important single monitoring need with respect to security and well-being of the elderly is fall detection. In this paper, a two-threshold MATLAB-algorithm for fall detection is described. The algorithm uses mainly tri-axial accelerometer and tri-axial gyroscope data measured from the waist to distinguish between fall, possible fall, and activity of daily living (ADL). The decision between fall and possible fall is done by the posture information from the waist- and ankle-worn devices ten seconds after the fall impact. By categorizing falls into these two sub-categories, an alarm is generated only in serious falls, thus leading to low false alarm rate. The impact itself is detected as the total sum vector magnitudes of both the acceleration and angular velocity exceeds their fixed thresholds. With this method, the sensitivity of the algorithm is 95.6% with the set of 68 recorded fall events. Specificity is 99.6% with the set of 231 measured ADL movements. It is further shown that the use of two thresholds gives better results than just one threshold.
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paper_title: The Future of Integrated Circuits: A Survey of Nanoelectronics

paper_content:

While most of the electronics industry is dependent on the ever-decreasing size of lithographic tran- sistors, this scaling cannot continue indefinitely. Nanoelectro- nics (circuits built with components on the scale of 10 nm) seem to be the most promising successor to lithographic based ICs. Molecular-scale devices including diodes, bistable switches, carbon nanotubes, and nanowires have been fab- ricated and characterized in chemistry labs. Techniques for self-assembling these devices into different architectures have also been demonstrated and used to build small-scale proto- types. While these devices and assembly techniques will lead to nanoscale electronics, they also have the drawback of being prone to defects and transient faults. Fault-tolerance techni- ques will be crucial to the use of nanoelectronics. Lastly, changes to the software tools that support the fabrication and use of ICs will be needed to extend them to support nano- electronics. This paper introduces nanoelectronics and reviews the current progress made in research in the areas of tech- nologies, architectures, fault tolerance, and software tools.
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paper_title: Ageing of population and health care expenditure: a red herring?

paper_content:

This paper studies the relationship between health care expenditure (HCE) and age, using longitudinal rather than cross-sectional data. The econometric analysis of HCE in the last eight quarters of life of individuals who died during the period 1983-1992 indicates that HCE depends on remaining lifetime but not on calendar age, at least beyond 65p. The positive relationship between age and HCE observed in cross-sectional data may be caused by the simple fact that at age 80, for example, there are many more individuals living in their last 2 years than at age 65. The limited impact of age on HCE suggests that population ageing may contribute much less to future growth of the health care sector than claimed by most observers. Copyright © 1999 John Wiley & Sons, Ltd.
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paper_title: Foot age estimation for fall-prevention using sole pressure by fuzzy logic

paper_content:

This paper describes a foot age estimation system using fuzzy logic. The method employs sole pressure distribution change data. The sole pressure data is acquired by a mat type load distribution sensor. The proposed method extracts step length, step center of sole pressure width, distance of single support period and time of double support period as gait features. The fuzzy degrees for young age, middle age and elderly groups are calculated from these gait features. The foot age of the walking person on the sensor is estimated by fuzzy MIN-MAX center of gravity method. In the experiment, the proposed method estimated subject ages with good correlation coefficient.
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paper_title: A pervasive solution for risk awareness in the context of fall prevention

paper_content:

In the present work, we introduce Fallarm, a pervasive fall prevention solution suitable for hospitals and care facilities, as well as for home settings. We applied a multifaceted intervention strategy based on closed-loop information exchange between proactive and reactive methods: comprehensive assessment protocols determine the individuals' risk of falling; an innovative device continuously monitors subjects' activities, and it provides patients with constant feedback about their actual risk. Thus, it increases their awareness; simultaneously, it realizes measures to prevent adverse events, and it reports any incident and aims to reduce the level of injury. As a result, our solution offers a comprehensive strategy for the remote management of a person's risk of falling 24 hours a day, enabling many vulnerable people to remain living independently. In this paper, we detail the architecture of our system, and we discuss the results of an experimental study we conducted to demonstrate the applicability of Fallarm in both clinical and home settings.
---
paper_title: Home environment risk factors for falls in older people and the efficacy of home modifications

paper_content:

Most homes contain potential hazards, and many older people attribute their falls to trips or slips inside the home or immediate home surroundings. However, the existence of home hazards alone is insufficient to cause falls, and the interaction between an older person’s physical abilities and their exposure to environmental stressors appears to be more important. Taking risks or impulsivity may further elevate falls risk. Some studies have found that environmental hazards contribute to falls to a greater extent in older vigorous people than in older frail people. This appears to be due to increased exposure to falls hazards with an increase in the proportion of such falls occurring outside the home. There may also be a non-linear pattern between mobility and falls associated with hazards. Household environmental hazards may pose the greatest risk for older people with fair balance, whereas those with poor balance are less exposed to hazards and those with good mobility are more able to withstand them. Reducing hazards in the home appears not to be an effective falls-prevention strategy in the general older population and those at low risk of falls. Home hazard reduction is effective if targeted at older people with a history of falls and mobility limitations. The effectiveness may depend on the provision of concomitant training for improving transfer abilities and other strategies for effecting behaviour change.
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paper_title: In-Home Fall Risk Assessment and Detection Sensor System

paper_content:

Falls are a major problem in older adults. A continuous, unobtrusive, environmentally mounted (i.e., embedded into the environment and not worn by the individual), in-home monitoring system that automatically detects when falls have occurred or when the risk of falling is increasing could alert health care providers and family members to intervene to improve physical function or manage illnesses that may precipitate falls. Researchers at the University of Missouri Center for Eldercare and Rehabilitation Technology are testing such sensor systems for fall risk assessment (FRA) and detection in older adults' apartments in a senior living community. Initial results comparing ground truth (validated measures) of FRA data and GAITRite System parameters with data captured from Microsoft(®) Kinect and pulse-Doppler radar are reported.
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paper_title: Motion control of intelligent passive-type Walker for fall-prevention function based on estimation of user state

paper_content:

In this paper, we introduce a passive-type walker using servo brakes referred to as RT Walker. RT Walker realizes several functions such as obstacles/steps avoidance function, path following function, gravity compensation function, variable motion characteristics function, etc., by controlling only servo brakes without using servo motors. These passive-type systems are dependable for using practically in real world environment, because of the passive dynamics with respect to the applied force/moment, simple structure, lightweight, and so on. However, the most serious problem of them is the falling accident of user, because the passive-type systems are lightweight and move easily based on the small force/moment applied by the user unintentionally. In this paper, we pay attention to a method for estimating the human state during the usage of the walker and propose a motion control algorithm for realizing a fall-prevention function based on its human state. We also implement the proposed control methods in RT Walker experimentally and illustrate the validity of them
---
paper_title: A novel fall prevention scheme for intelligent cane robot by using a motor driven universal joint

paper_content:

In this study, we propose a novel fall prevention scheme for an omni-direction type cane robot by using a DC motor driven universal joint. The cane robot which is driven by three omni-wheels is called Intelligent Cane Robot (iCane). It is designed for aiding the elderly and handicapped people walking as shown in Fig.1. The motion of cane robot is controlled for both normal and abnormal walking conditions. The user's normal walking aided by the cane robot, a concept called “Intentional Direction (ITD)” is proposed. Guided by the online estimated ITD, we apply the admittance control method in the motion control of cane robot. For the abnormal walking, we mainly studied the case of user's fall down. The center of gravity (COG) of user can be estimated from the angle of an inverted pendulum which represents human dynamic model. Fall prevention algorithm based on the relationship between user's COG and the cane is proposed. Because the size of the cane robot is small, when the robot is preventing the user falling down, firstly, the stability of the cane robot should be ensured. A universal joint which is driven by two DC motors is used to reduce the moment causing the cane robot falling over. The proposed method is verified through experiments.
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paper_title: Gait analysis of sit-to-walk motion by using portable acceleration monitor device for fall prevention

paper_content:

The purpose of this study is to investigate whether the acceleration of the center of gravity of body during sit-to-walk motion have a relationship with falling or not. In this study, we measured sit-to-walk motion of fall experienced and inexperienced subjects by using a portable acceleration monitor device that we have developed. The result of discriminant analysis by using indexes with a significant difference revealed a 90.3% correct prediction rate for falling. The results indicated possibility of fall prevention by this method.
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paper_title: Psychosocial factors associated with fall-related hip fractures

paper_content:

Background: fall-related injuries in older people are a major public health concern. This study examined the relationship between psychosocial determinants of healthy ageing and risk of fall-related hip fracture in community-dwelling older people. The purpose was to contribute evidence for promotion of healthy ageing strategies in population-based interventions for fall injury prevention. Methods: a case-control study was conducted with 387 participants, with at least two controls recruited per case. Cases of fall-related hip fracture in community-dwelling people aged 65 and older were recruited from hospital admissions in Brisbane, Australia, in 2003-2004. Community-based controls, matched by age, sex and postcode, were recruited via electoral roll sampling. A questionnaire assessing psychosocial factors, identified as determinants of healthy ageing, was administered at face-to-face interviews. Results: psychosocial factors having a significant independent protective effect on hip fracture risk included being currently married [OR: 0.44 (0.22 to 0.88)], living in present residence for 5 years or more [OR: 0.43 (0.22 to 0.84)], having private health insurance [OR: 0.49 (0.27 to 0.90)], using proactive coping strategies [OR: 0.52 (0.29 to 0.92)], having a higher level of life satisfaction [OR: 0.47 (0.27 to 0.81)], and engagement in social activities in older age [OR: 0.30 (0.17 to 0.54)]. Conclusion: this study suggests that psychosocial determinants of healthy ageing are protective in fall-related hip fracture injury in older people. Reduction in the public health burden caused by this injury may then be achieved by implementing healthy ageing strategies involving community-based approaches to enhance the psychosocial environments of older people.
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paper_title: Automatic Fall Detection and Activity Classification by a Wearable Embedded Smart Camera

paper_content:

Robust detection of events and activities, such as falling, sitting, and lying down, is a key to a reliable elderly activity monitoring system. While fast and precise detection of falls is critical in providing immediate medical attention, other activities like sitting and lying down can provide valuable information for early diagnosis of potential health problems. In this paper, we present a fall detection and activity classification system using wearable cameras. Since the camera is worn by the subject, monitoring is not limited to confined areas, and extends to wherever the subject may go including indoors and outdoors. Furthermore, since the captured images are not of the subject, privacy concerns are alleviated. We present a fall detection algorithm employing histograms of edge orientations and strengths, and propose an optical flow-based method for activity classification. The first set of experiments has been performed with prerecorded video sequences from eight different subjects wearing a camera on their waist. Each subject performed around 40 trials, which included falling, sitting, and lying down. Moreover, an embedded smart camera implementation of the algorithm was also tested on a CITRIC platform with subjects wearing the CITRIC camera, and each performing 50 falls and 30 non-fall activities. Experimental results show the success of the proposed method.
---
paper_title: Histograms of oriented gradients for human detection

paper_content:

We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.
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paper_title: A data mining approach for fall detection by using k-nearest neighbour algorithm on wireless sensor network data

paper_content:

Fall detection technology is critical for the elderly people. In order to avoid the need of full time care giving service, the actual trend is to encourage elderly to stay living autonomously in their homes as long as possible. Reliable fall detection methods can enhance life safety of the elderly and boost their confidence by immediately alerting fall cases to caregivers. This study presents an algorithm of fall detection, which detects fall events by using data-mining approach. The authors' proposed method performs detection in two steps. First, it collects the wireless sensor network (WSN) data in stream format from sensor devices. Second, it uses k-nearest neighbour algorithm, that is, well-known lazy learning algorithm to detect fall occurrences. It detects falls by identifying the fall patterns in the data stream. Experiments show that the proposed method has promising results on WSN data stream in detecting falls.
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paper_title: Real life applicable fall detection system based on wireless body area network

paper_content:

Real-time health monitoring with wearable sensors is an active area of research. In this domain, observing the physical condition of elderly people or patients in personal environments such as home, office, and restroom has special significance because they might be unassisted in these locations. The elderly people have limited physical abilities and are more vulnerable to serious physical damages even with small accidents, e.g. fall. The falls are unpredictable and unavoidable. In case of a fall, early detection and prompt notification to emergency services is essential for quick recovery. However, the existing fall detection devices are bulky and uncomfortable to wear. Also, detection system using the devices requires the higher computation overhead to detect falls from activities of daily living (ADL). In this paper, we propose a new fall detection system using one sensor node which can be worn as a necklace to provide both the comfortable wearing and low computation overhead. The proposed necklace-shaped sensor node includes tri-axial accelerometer and gyroscope sensors to classify the behaviour and posture of the detection subject. The simulated experimental results performed 5 fall scenarios 50 times by 5 persons show that our proposed detection approach can successfully distinguish between ADL and fall, with sensitivities greater than 80% and specificities of 100%.
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paper_title: PerFallD: A pervasive fall detection system using mobile phones

paper_content:

Falls are a major health risk that diminish the quality of life among elderly people. With the elderly population surging, especially with aging “baby boomers”, fall detection becomes increasingly important. However, existing commercial products and academic solutions struggle to achieve pervasive fall detection. In this paper, we propose utilizing mobile phones as a platform for pervasive fall detection system development. To our knowledge, we are the first to do so. We design a detection algorithm based on mobile phone platforms. We propose PerFallD, a pervasive fall detection system implemented on mobile phones. We implement a prototype system on the Android G1 phone and conduct experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. Experimental results show that PerFallD achieves strong detection performance and power efficiency.
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paper_title: Fall Detection on Mobile Phones Using Features from a Five-Phase Model

paper_content:

The injuries caused by falls are great threats to the elderly people. With the ability of communication and motion sensing, the mobile phone is an ideal platform to detect the occurrence of fall accidents and help the injured person receive first aid. However, the missed detection and false alarm of the monitoring software will cause annoyance to the users in real use. In this paper, we present a novel fall detection technique using features from a five-phase model which describes the state change of the user's motion during the fall. Experiment results validate the effectiveness of the algorithm and show that the features derived from the model as gravity-cross rate and non-primarily maximum and minimum points of the acceleration data are useful to improve the precision of the detection. Moreover, we implement the technique as uCare, an Android application that helps elderly people in fall prevention, detection and first aid seeking.
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paper_title: Human falling detection algorithm using back propagation neural network

paper_content:

A fall monitor system is necessary to reduce the rate of fall fatalities in elderly people. As an accelerometer has been smaller and inexpensive, it has been becoming widely used in motion detection fields. This paper proposes the falling detection algorithm based on back propagation neural network to detect the fall of elderly people. In the experiment, a tri-axial accelerometer was attached to waists of five healthy and young people. In order to evaluate the performance of the fall detection, five young people were asked to simulate four daily-life activities and four falls; walking, jumping, flopping on bed, rising from bed, front fall, back fall, left fall and right fall. The experimental results show that the proposed algorithm can potentially distinguish the falling activities from the other daily-life activities.
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paper_title: Fall detection with distributed floor-mounted accelerometers: An overview of the development and evaluation of a fall detection system within the project eHome

paper_content:

Within the project "eHome" a prototype of an assistive home system was developed, aiming to prolong the independent life of elderly people at home. Besides communication, e-access and safety relevant features, a core part of this system is an automatic fall detection, which utilizes floor-mounted accelerometers to gather body-sound signals that typically occur during a human fall. This approach targets to avoid acceptance, usability and reliability issues of available body-mounted fall detectors. The system was developed with focus on practical applicability, reliability and exploitability. The prototype was evaluated successfully in laboratory and during 507 days in real-life at homes of persons from the target group. During the laboratory trials a sensitivity of 87% and a specificity of 97.7% could be achieved for a defined fall scenario and across four tested floors. Further research is suggested to investigate floor dependencies of the fall detection performance.
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paper_title: A wearable wireless fall detection system with accelerators

paper_content:

Falls in elderly is a major health problem and a cost burden to social services. Thus automatic fall detectors are needed to support the independence and security of the elderly. The goal of this research is to develop a real-time portable wireless fall detection system, which is capable of automatically discriminating between falls and Activities of Daily Life (ADL). The fall detection system contains a portable fall-detection terminal and a monitoring centre, both of which communicate with ZigBee protocol. To extract the features of falls, falls data and ADL data obtained from young subjects are analyzed. Based on the characteristics of falls, an effective fall detection algorithm using tri-axis accelerometers is introduced, and the results show that falls can be distinguished from ADL with a sensitivity over 95% and a specificity of 100%, for a total set of 270 movements.
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paper_title: A hybrid human fall detection scheme

paper_content:

This paper presents a novel video-based human fall detection system that can detect a human fall in real-time with a high detection rate. This fall detection system is based on an ingenious combination of skeleton feature and human shape variation, which can efficiently distinguish “fall-down” activities from “fall-like” ones. The experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate.
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paper_title: A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment

paper_content:

The mobile application is capable of detecting possible falls for elderly, through the use of special sensors. The alert messages contain useful information about the people in danger, such as his/her geo location and also corresponding directions on a map. In occasions of false alerts, the supervised person is given the ability to estimate the value of importance of a possible alert and to stop it before being transmitted. This paper describes system for monitoring and fall detection of ELDERLY PEOPLE using triaxial accelerometer together with ZigBee transceiver to detect fall of ELDERLY PEOPLE. The Accidental Fall Detection System will be able to assist careers as well as the elderly, as the careers will be notified immediately to the intended person. This fall detection system is designed to detect the accidental fall of the elderly and alert the careers or their loved ones via Smart-Messaging Services (SMS) immediately. This fall detection is created using microcontroller technology as the heart of the system, the accelerometer as to detect the sudden movement or fall and the Global System for Mobile (GSM) modem, to send out SMS to the receiver
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paper_title: A two-threshold fall detection algorithm for reducing false alarms

paper_content:

Wireless health monitoring can be used in health care for the aged to support independent living, either at home or in sheltered housing, for as long as possible. The most important single monitoring need with respect to security and well-being of the elderly is fall detection. In this paper, a two-threshold MATLAB-algorithm for fall detection is described. The algorithm uses mainly tri-axial accelerometer and tri-axial gyroscope data measured from the waist to distinguish between fall, possible fall, and activity of daily living (ADL). The decision between fall and possible fall is done by the posture information from the waist- and ankle-worn devices ten seconds after the fall impact. By categorizing falls into these two sub-categories, an alarm is generated only in serious falls, thus leading to low false alarm rate. The impact itself is detected as the total sum vector magnitudes of both the acceleration and angular velocity exceeds their fixed thresholds. With this method, the sensitivity of the algorithm is 95.6% with the set of 68 recorded fall events. Specificity is 99.6% with the set of 231 measured ADL movements. It is further shown that the use of two thresholds gives better results than just one threshold.
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paper_title: CARE: A dynamic stereo vision sensor system for fall detection

paper_content:

This paper presents a recently developed dynamic stereo vision sensor system and its application for fall detection towards safety for elderly at home. The system consists of (1) two optical detector chips with 304×240 event-driven pixels which are only sensitive to relative light intensity changes, (2) an FPGA for interfacing the detectors, early data processing, and stereo matching for depth map reconstruction, (3) a digital signal processor for interpreting the sensor data in real-time for fall recognition, and (4) a wireless communication module for instantly alerting caring institutions. This system was designed for incident detection in private homes of elderly to foster safety and security. The two main advantages of the system, compared to existing wearable systems are from the application's point of view: (a) the stationary installation has a better acceptance for independent living comparing to permanent wearing devices, and (b) the privacy of the system is systematically ensured since the vision detector does not produce real images such as classic video sensors. The system can actually process about 300 kevents per second. It was evaluated using 500 fall cases acquired with a stuntman. More than 90% positive detections were reported. We will show a live demonstration during ISCAS2012 of the sensor system and its capabilities.
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paper_title: Embedded fall detection with a neural network and bio-inspired stereo vision

paper_content:

In this paper, we present a bio-inspired, purely passive, and embedded fall detection system for its application towards safety for elderly at home. Bio-inspired means the use of two optical detector chips with event-driven pixels that are sensitive to relative light intensity changes only. The two chips are used as stereo configuration which enables a 3D representation of the observed area with a stereo matching technique. In contrast to conventional digital cameras, this image sensor delivers asynchronous events instead of synchronous intensity or color images, thus, the privacy issue is systematically solved. Another advantage is that stationary installed fall detection systems have a better acceptance for independent living compared to permanently worn devices. The fall detection is done by a trained neural network. First, a meaningful feature vector is calculated from the point clouds, then the neural network classifies the actual event as fall or non-fall. All processing is done on an embedded device consisting of an FPGA for stereo matching and a DSP for neural network calculation achieving several fall evaluations per second. The results evaluation showed that our fall detection system achieves a fall detection rate of more than 96% with false positives below 5% for our prerecorded dataset consisting of 679 fall scenarios.
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paper_title: Fall detection using a Gaussian distribution of clustered knowledge, augmented radial basis neural-network, and multilayer perceptron

paper_content:

The rapidly increasing population of elderly people has posed a big challenge to research in fall prevention and detection. Substantial amounts of injuries, disabilities, traumas and deaths among elderly people due to falls have been reported worldwide. There is therefore a need for a reliable, simple, and affordable automatic fall detection system. This paper proposes a reliable fall detection algorithm using minimal information from a single waist worn wireless tri-axial accelerometer. The method proposed is to approach fall detection using digital signal processing and neural networks. This method includes the application of Discrete Wavelet Transform (DWT), Regrouping Particle Swarm Optimization (RegPSO), a proposed method called Gaussian Distribution of Clustered Knowledge (GCK), and an Ensemble of Classifiers using two different classifiers: Multilayer Perceptron Neural Network (MLP) and Augmented Radial Basis Neural Networks (ARBF). The proposed method has been tested on 8 healthy individuals in a home environment and yields promising result of up to 100% sensitivity on ingroup, 97.65% sensitivity on outgroup, and 99.56% specificity on Activities of Daily Living (ADL) data.
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paper_title: Fall Detection with Wearable Sensors--Safe (Smart Fall Detection)

paper_content:

The high rate of falls incidence among the elderly calls for the development of reliable and robust fall detection systems. A number of such systems have been proposed, with claims of fall detection accuracy of over 90% based on accelerometers and gyroscopes. However, most such fall detection algorithms have been developed based on observational analysis of the data gathered, leading to thresholds setting for fall/non-fall situations. Whilst the fall detection accuracies reported appear to be high, there is little evidence that the threshold based methods proposed generalise well with different subjects and different data gathering strategies or experimental scenarios. Moreover, few attempts appear to have been made to validate the proposed methods in real-life scenarios or to deliver robust fall decisions in real-time. The research here uses machine learning and particularly decision trees to detect 4 types of falls (forward, backward, right and left). When applied to experimental data from 8 male subjects, the accelerometers and gyroscopes based system discriminates between activities of daily living (ADLs) and falls with a precision of 81% and recall of 92%. The performance and robustness of the method proposed has been further analysed in terms its sensitivity to subject physical profile and training set size.
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paper_title: View-invariant Fall Detection for Elderly in Real Home Environment

paper_content:

We propose a novel context based human fall detection mechanism in real home environment. Fall incidents are detected using head and floor information. The centroid location of the head and feet from each frame are used to learn a context model consisting of normal head and floor blocks. Every floor block has its associated Gaussian distribution, representing a set of head blocks. This Gaussian distribution defines standard vertical distance as average height of an object at that specific floor block. The classification of blocks and average height is later used to detect a fall. Fall detection methods often detect bending situations as fall. This method is able to distinguish bending and sitting from falling. Furthermore, a fall into any direction and at any distance from camera can be detected. Evaluation results show the robustness and high accuracy of the proposed approach.
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paper_title: In-Home Fall Risk Assessment and Detection Sensor System

paper_content:

Falls are a major problem in older adults. A continuous, unobtrusive, environmentally mounted (i.e., embedded into the environment and not worn by the individual), in-home monitoring system that automatically detects when falls have occurred or when the risk of falling is increasing could alert health care providers and family members to intervene to improve physical function or manage illnesses that may precipitate falls. Researchers at the University of Missouri Center for Eldercare and Rehabilitation Technology are testing such sensor systems for fall risk assessment (FRA) and detection in older adults' apartments in a senior living community. Initial results comparing ground truth (validated measures) of FRA data and GAITRite System parameters with data captured from Microsoft(®) Kinect and pulse-Doppler radar are reported.
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paper_title: A pervasive solution for risk awareness in the context of fall prevention

paper_content:

In the present work, we introduce Fallarm, a pervasive fall prevention solution suitable for hospitals and care facilities, as well as for home settings. We applied a multifaceted intervention strategy based on closed-loop information exchange between proactive and reactive methods: comprehensive assessment protocols determine the individuals' risk of falling; an innovative device continuously monitors subjects' activities, and it provides patients with constant feedback about their actual risk. Thus, it increases their awareness; simultaneously, it realizes measures to prevent adverse events, and it reports any incident and aims to reduce the level of injury. As a result, our solution offers a comprehensive strategy for the remote management of a person's risk of falling 24 hours a day, enabling many vulnerable people to remain living independently. In this paper, we detail the architecture of our system, and we discuss the results of an experimental study we conducted to demonstrate the applicability of Fallarm in both clinical and home settings.
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paper_title: Fall prevention control of passive intelligent walker based on human model

paper_content:

As aging progresses, fall accident of the person using the walker is an acute problem. It is necessary to know the situation of the userpsilas fall to prevent it. In this paper, we propose a method for estimating the userpsilas fall by modeling of the user in real time as a solid body link model, and pay attention to the center of gravity of the model. We also propose a method for controlling a passive intelligent walker to prevent the userpsilas fall according to the support polygon and the walking characteristic of the user. We experimented with passive intelligent walker in which we implement the proposed fall prevention control and show the effectiveness of the proposal method.
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paper_title: Motion control of intelligent passive-type Walker for fall-prevention function based on estimation of user state

paper_content:

In this paper, we introduce a passive-type walker using servo brakes referred to as RT Walker. RT Walker realizes several functions such as obstacles/steps avoidance function, path following function, gravity compensation function, variable motion characteristics function, etc., by controlling only servo brakes without using servo motors. These passive-type systems are dependable for using practically in real world environment, because of the passive dynamics with respect to the applied force/moment, simple structure, lightweight, and so on. However, the most serious problem of them is the falling accident of user, because the passive-type systems are lightweight and move easily based on the small force/moment applied by the user unintentionally. In this paper, we pay attention to a method for estimating the human state during the usage of the walker and propose a motion control algorithm for realizing a fall-prevention function based on its human state. We also implement the proposed control methods in RT Walker experimentally and illustrate the validity of them
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paper_title: On Space-Time Interest Points

paper_content:

Local image features or interest points provide compact and abstract representations of patterns in an image. In this paper, we extend the notion of spatial interest points into the spatio-temporal domain and show how the resulting features often reflect interesting events that can be used for a compact representation of video data as well as for interpretation of spatio-temporal events. ::: ::: To detect spatio-temporal events, we build on the idea of the Harris and Forstner interest point operators and detect local structures in space-time where the image values have significant local variations in both space and time. We estimate the spatio-temporal extents of the detected events by maximizing a normalized spatio-temporal Laplacian operator over spatial and temporal scales. To represent the detected events, we then compute local, spatio-temporal, scale-invariant N-jets and classify each event with respect to its jet descriptor. For the problem of human motion analysis, we illustrate how a video representation in terms of local space-time features allows for detection of walking people in scenes with occlusions and dynamic cluttered backgrounds.
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paper_title: RGBD-camera based get-up event detection for hospital fall prevention

paper_content:

In this work, we develop a computer vision based fall prevention system for hospital ward application. To prevent potential falls, once the event of patient get up from the bed is automatically detected, nursing staffs are alarmed immediately for assistance. For the detection task, we use a RGBD sensor (Microsoft Kinect). The geometric prior knowledge is exploited by identifying a set of task-specific feature channels, e.g., regions of interest. Extensive motion and shape features from both color and depth image sequences are extracted. Features from multiple modalities and channels are fused via a multiple kernel learning framework for training the event detector. Experimental results demonstrate the high accuracy and efficiency achieved by the proposed system.
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paper_title: Foot age estimation for fall-prevention using sole pressure by fuzzy logic

paper_content:

This paper describes a foot age estimation system using fuzzy logic. The method employs sole pressure distribution change data. The sole pressure data is acquired by a mat type load distribution sensor. The proposed method extracts step length, step center of sole pressure width, distance of single support period and time of double support period as gait features. The fuzzy degrees for young age, middle age and elderly groups are calculated from these gait features. The foot age of the walking person on the sensor is estimated by fuzzy MIN-MAX center of gravity method. In the experiment, the proposed method estimated subject ages with good correlation coefficient.
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paper_title: A novel fall prevention scheme for intelligent cane robot by using a motor driven universal joint

paper_content:

In this study, we propose a novel fall prevention scheme for an omni-direction type cane robot by using a DC motor driven universal joint. The cane robot which is driven by three omni-wheels is called Intelligent Cane Robot (iCane). It is designed for aiding the elderly and handicapped people walking as shown in Fig.1. The motion of cane robot is controlled for both normal and abnormal walking conditions. The user's normal walking aided by the cane robot, a concept called “Intentional Direction (ITD)” is proposed. Guided by the online estimated ITD, we apply the admittance control method in the motion control of cane robot. For the abnormal walking, we mainly studied the case of user's fall down. The center of gravity (COG) of user can be estimated from the angle of an inverted pendulum which represents human dynamic model. Fall prevention algorithm based on the relationship between user's COG and the cane is proposed. Because the size of the cane robot is small, when the robot is preventing the user falling down, firstly, the stability of the cane robot should be ensured. A universal joint which is driven by two DC motors is used to reduce the moment causing the cane robot falling over. The proposed method is verified through experiments.
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paper_title: A reliable fall detection system based on wearable sensor and signal magnitude area for elderly residents

paper_content:

Falls are the primary cause of accidents for elderly people and often result in serious injury and health threats. It is also the main obstacle to independent living for frail and elderly people. A reliable fall detector can reduce the fear of falling and provide the user with the reassurance to maintain an independent lifestyle since the reliable and effective fall detection mechanism will provide urgent medical support and dramatically reduce the cost of medical care. In this work, we propose a fall-detecting system based on a wearable sensor and a real-time fall detection algorithm. We use a waist- mounted tri-axial accelerometer to capture movement data of the human body, and propose a fall detection method that uses the area under a signal magnitude curve to distinguish between falls and daily activities. Experimental results demonstrate the effectiveness of proposed scheme with high reliability and sensitivity on fall detection. The system is not only cost effective but also portable that fulfills the requirements of fall detection.
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paper_title: smartPrediction: a real-time smartphone-based fall risk prediction and prevention system

paper_content:

The high risk of falls and the substantial increase in the elderly population have recently stimulated scientific research on Smartphone-based fall detection systems. Even though these systems are helpful for fall detection, the best way to reduce the number of falls and their consequences is to predict and prevent them from happening in the first place. To address the issue of fall prevention, in this paper, we propose a fall prediction system by integrating the sensor data of Smartphones and a Smartshoe. We designed and implemented a Smartshoe that contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data in any environment. By assimilating the Smartshoe and Smartphone sensors data, we performed an extensive set of experiments to evaluate normal and abnormal walking patterns. The system can generate an alert message in the Smartphone to warn the user about the high-risk gait patterns and potentially save them from an imminent fall. We validated our approach using a decision tree with 10-fold cross validation and found 97.2% accuracy in gait abnormality detection.
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paper_title: The Barthel ADL Index: a reliability study.

paper_content:

The Barthel Index is a valid measure of disability. In this study we investigated the reliability of four different methods of obtaining the score in 25 patients: self-report, asking a trained nurse who had worked with the patient for at least one shift, and separate testing by two skilled observers within 72 hours of admission. Analysis of total (summed) scores revealed a close correlation between all four methods: a difference of 4/20 points was likely to reflect a genuine difference. In individual items, most disagreement was minor and involved the definition of middle grades. Asking an informed nurse or relative was as reliable as testing, and is quicker.
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paper_title: Development and evaluation of evidence based risk assessment tool (STRATIFY) to predict which elderly inpatients will fall: case-control and cohort studies.

paper_content:

OBJECTIVES ::: To identify clinical characteristics of elderly inpatients that predict their chance of falling (phase 1) and to use these characteristics to derive a risk assessment tool and to evaluate its power in predicting falls (phases 2 and 3). ::: ::: ::: DESIGN ::: Phase 1: a prospective case-control study. Phases 2 and 3: prospective evaluations of the derived risk assessment tool in predicting falls in two cohorts. ::: ::: ::: SETTING ::: Elderly care units of St Thomas's Hospital (phase 1 and 2) and Kent and Canterbury Hospital (phase 3). ::: ::: ::: SUBJECTS ::: Elderly hospital inpatients (aged > or = 65 years): 116 cases and 116 controls in phase 1,217 patients in phase 2, and 331 in phase 3. ::: ::: ::: MAIN OUTCOME MEASURES ::: 21 separate clinical characteristics were assessed in phase 1, including the abbreviated mental test score, modified Barthel index, a transfer and mobility score obtained by combining the transfer and mobility sections of the Barthel index, and several nursing judgements. ::: ::: ::: RESULTS ::: In phase 1 five factors were independently associated with a higher risk of falls: fall as a presenting complaint (odds ratio 4.64 (95% confidence interval 2.59 to 8.33); a transfer and mobility score of 3 or 4 (2.10 (1.22 to 3.61)); and primary nurses' judgment that a patient was agitated (20.9 (9.62 to 45.62)), needed frequent toileting (2.48 (1.08 to 5.70)), and was visually impaired (3.56 (1.26 to 10.05)). A risk assessment score (range 0-5) was derived by scoring one point for each of these five factors. In phases 2 and 3 a risk assessment score > 2 was used to define high risk: the sensitivity and specificity of the score to predict falls during the following week was 93% and 88% respectively in phase 2 and 92% and 68% respectively in phase 3. ::: ::: ::: CONCLUSION ::: This simple risk assessment tool predicted with clinically useful sensitivity and specificity a high percentage of falls among elderly hospital inpatients.
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Title: Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors

Section 1: Introduction
Description 1: Provide a background on fall-related injuries, their consequences, and common causes. Introduce the importance of fall detection (FD) and fall prevention (FP) systems, and briefly mention the types of sensors used.

Section 2: Machine Learning General Model of FD and FP Systems
Description 2: Describe the general model used by FD and FP systems, including the data collection, feature extraction, learning, and evaluation modules. Explain the key terms related to machine learning.

Section 3: Data Collection
Description 3: Explain the process of data collection, the variables needed, and the methods of gathering and cleaning the data.

Section 4: Feature Extraction
Description 4: Discuss the process of selecting relevant characteristics or attributes from the collected data. Explain feature construction and feature selection.

Section 5: Learning Module
Description 5: Describe the learning mechanisms in FD and FP systems, focusing on supervised learning algorithms such as Decision Trees, Naive Bayes, K-Nearest Neighbor, and Support Vector Machines.

Section 6: Model Evaluation
Description 6: Explain the importance of evaluating the performance of learning models in FD and FP systems. Describe common performance indicators and evaluation methods.

Section 7: Design Issues
Description 7: Discuss important aspects to consider when designing or evaluating FD and FP systems, such as obtrusiveness, occlusion, privacy, computational cost, energy consumption, noise, and threshold definition.

Section 8: Fall Detection Systems
Description 8: Present the basic structure of FD systems and the different types of sensors used. Provide an overview of various fall detection systems using external and wearable sensors.

Section 9: Fall Prevention Systems
Description 9: Introduce fall prevention systems and discuss the significant factors and strategies for fall prevention, covering environmental, physical, and psychological factors.

Section 10: Evaluation of the Systems
Description 10: Provide a qualitative evaluation of state-of-the-art FD and FP systems based on the design issues described previously. Summarize key studies and compare their approaches, strengths, and weaknesses.

Section 11: Conclusions
Description 11: Summarize the main findings of the survey, the general model of FD and FP systems, design considerations, and qualitative evaluation of various systems. Offer final thoughts and potential directions for future research.

Section 12: Author Contributions
Description 12: Specify the contributions made by each author to the manuscript.