A survey on fall detection: Principles and approaches

Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls.

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Challenges, issues and trends in fall detection systems

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

Fall Detection in Homes of Older Adults Using the Microsoft Kinect

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

Human fall detection on embedded platform using depth maps and wireless accelerometer

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.

We explored the perception and receptivity of elderly people regarding the introduction of an intelligent videomonitoring system (IVS) at home. Using a mixed methods design, 25 elderly people with a history of falls completed a structured interview (two questionnaires). In the year preceding the interview, 72% of the participants fell as many as seven times. Open-ended questions (qualitative data) were used to supplement the quantitative data. A content analysis (qualitative data) and a descriptive analysis (quantitative data) were carried out. The participants were 84% favourable or partially favourable to technologies which ensured home security and 96% favourable or partially favourable to the IVS. About half (48%) said that they would use it. The other participants did not wish to use it unless they had been left to live alone or if their health condition worsened. Thus the living conditions of the elderly appear to influence their perception and receptivity regarding the acceptance and use of an IVS.

An intelligent videomonitoring system for fall detection at home: perceptions of elderly people.

3D head tracking for fall detection using a single calibrated camera

In the context of the growing proportion of seniors in the western world population and the efforts provided in home care services, we have developed a computer vision system for monitoring medication intake. The system detects automatically medication intake using a single low-cost webcam. Person detection and tracking over the video sequence is done using color-based techniques while the recognition of the medication intake activity is performed using our main contribution, a three-level scenario model. Experimental results in controlled conditions are shown and we discuss improvements to our system.

Alain St-Arnaud

Papers

Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

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

An intelligent videomonitoring system for fall detection at home: perceptions of elderly people.

3D head tracking for fall detection using a single calibrated camera

Video surveillance of medication intake.