The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

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A review of wearable sensors and systems with application in rehabilitation.

With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.

Enhanced Computer Vision With Microsoft Kinect Sensor: A Review

Over the past few generations, expectations of comfort, cleanliness and convenience have altered radically, but these dramatic changes have largely gone unnoticed. This intriguing book brings together the sociology of consumption and technology to investigate the evolution of these changes, as well the social meaning of the practices themselves. Homes, offices, domestic appliances and clothes play a crucial role in our lives, but not many of us question exactly how and why we perform so many daily rituals associated with them. Showers, heating, air-conditioning and clothes washing are simply accepted as part of our normal, everyday lives, but clearly this was not always the case. When did the daily shower become de rigueur? What effect has air conditioning had on the siesta at one time an integral part of Mediterranean life and culture? This book interrogates the meaning and supposed normality of these practices and draws disturbing conclusions. There is clear evidence supporting the view that routine consumption is controlled by conceptions of normality and profoundly shaped by cultural and economic forces. Shove maintains that habits are not just changing, but are changing in ways that imply escalating and standardizing patterns of consumption. This shrewd and engrossing analysis shows just how far the social meanings and practices of comfort, cleanliness and convenience have eluded us.

Comfort, cleanliness and conveniencethe social organization ofnormality

HANDBOOK OF MIXED METHODS IN SOCIAL & BEHAVIORAL RESEARCH (Book)

A survey on fall detection: Principles and approaches

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

Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

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.

Fall Detection from Human Shape and Motion History Using Video Surveillance

According to the demographic evolution in industrialized countries, more and more elderly people will experience falls at home and will require emergency services. The main problem comes from fall-prone elderly living alone at home. To resolve this lack of safety, we propose a new method to detect falls at home, based on a multiple-cameras network for reconstructing the 3-D shape of people. Fall events are detected by analyzing the volume distribution along the vertical axis, and an alarm is triggered when the major part of this distribution is abnormally near the floor during a predefined period of time, which implies that a person has fallen on the floor. This method was validated with videos of a healthy subject who performed 24 realistic scenarios showing 22 fall events and 24 cofounding events (11 crouching position, 9 sitting position, and 4 lying on a sofa position) under several camera configurations, and achieved 99.7% sensitivity and specificity or better with four cameras or more. A real-time implementation using a graphic processing unit (GPU) reached 10 frames per second (fps) with 8 cameras, and 16 fps with 3 cameras.

Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution

Faced with the growing population of seniors, Western societies need to think about new technologies to ensure the safety of elderly people at home. Computer vision provides a good solution for healthcare systems because it allows a specific analysis of people behavior. Moreover, a system based on video surveillance is particularly well adapted to detect falls. We present a new method to detect falls using a single camera. Our approach is based on the 3D trajectory of the head, which allows us to distinguish falls from normal activities using 3D velocities. I. INTRODUCTION As other Western countries, Canada's population is grow- ing older. According to the Public Health Agency of Canada (7), one Canadian out of eight was older than 65 years old in 2001. In 2026, this proportion will be one out of five. Moreover, in 1996, 93% of all seniors resided in private households, and among them, 29% lived alone (7). Faced this reality, new technologies are developed to help them live in a more secure environment at home. Falls are one of the most dangerous situation at home. Almost 62% of injury-related hospitalizations for seniors are the result of falls (8). The first concerned are older people living alone because the situation can be aggravated if they cannot call for help, being unconscious or immobilized. Nowadays, the favored solution is to use wearable fall detectors like accelerometers or help buttons. However, older people often forget to wear them, and a help button is useless if the person is unconscious after the fall. Computer vision systems offer a new solution for fall detection which overcome these limitations. Some research has been done to detect falls using image sensors. The easiest method to detect a fall is based on the shape of the person's silhouette or bounding box, but this method can be inaccurate, depending on the relative position of the person, camera, and perhaps occluding objects. Indeed, if the camera is placed sideways, the point of view can be affected by object occlusions. To overcome this problem, some researchers put the camera in the ceiling. For instance, Lee and Mihailidis (11) detect a fall using the shape of the person's silhouette, and Nait-Charif and McKenna (12) detect inactivity outside the normal zones of inactivity like chairs or sofas. Sixsmith and Johnson (15) use an infrared sensor This work was supported by the Natural Sciences and Engineering

Monocular 3D head tracking to detect falls of elderly people.

Falls are one of the major risks for seniors living alone at home. Computer vision systems, which do not require to wear sensors, offer a new and promising solution for fall detection. In this work, an occlusion robust method is presented based on two features: human centroid height relative to the ground and body velocity. Indeed, the first feature is an efficient solution to detect falls as the vast majority of falls ends on the ground or near the ground. However, this method can fail if the end of the fall is completely occluded behind furniture. Fortunately, these cases can be managed by using the 3D person velocity computed just before the occlusion.

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Jacqueline Rousseau

Papers

Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

Fall Detection from Human Shape and Motion History Using Video Surveillance

Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution

Monocular 3D head tracking to detect falls of elderly people.

Fall detection from depth map video sequences