Jacqueline Rousseau

Fall Detection from Human Shape and Motion History Using Video Surveillance

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.

Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

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 persons 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.

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

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.

Monocular 3D Head Tracking to Detect Falls of Elderly People

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

Fall detection from depth map video sequences

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.

Participation in daily activities and social roles of older adults with visual impairment

Purpose. (1) to document participation in daily activities and social roles of older adults seeking services for visual impairment (VI) and compare it with that of the older population without VI or other disabilities, and (2) to explore correlates of their participation. Methods. The 64 participants (46 women) had an average age of 79.3 years (SD = 5.9 years) and presented various types of VI. Participants were interviewed at home to collect information regarding their visual function (National Eye Institute Visual Function Questionnaire-25), sociodemographic and clinical characteristics, including depressive symptoms (Geriatric Depression Scale), and participation (Assessment of Life Habits/LIFE-H). Each participant was matched with another person without disabilities randomly recruited from the community. Results for the two populations on the Life-H participation domains were compared using t-tests. In the group with VI, general information (independent variables) was examined in relation to participation main scores (dependent variables), followed by multiple linear regression analyses. Results. Participation in daily activities and social roles of participants with VI (mean ± SD (/9) = 6.8 ± 1.0 and 5.6 ± 1.6, respectively) was significantly lower than that of participants without VI (8.1 ± 0.4 and 8.3 ± 0.4) (p < 0.0001). Depressive symptoms and perceived quality of distance vision were the strongest correlates and together explained more than 65% of the variance in the participation scores of the subjects with VI. Conclusions. This study demonstrates the participation restrictions associated with VI and underlines the importance of psychological aspects in participation.

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

Explaining the Relationship between Three Eye Diseases and Depressive Symptoms in Older Adults

PURPOSE The purpose of this study is to examine whether patients with age-related eye diseases, like age-related macular degeneration (AMD), glaucoma, or Fuchs corneal dystrophy, are more likely to show signs of depression compared to a control group of older adults with good vision, and to determine whether reduced mobility mediates these relationships. METHODS We recruited 315 eligible patients (81 with AMD, 55 with Fuchs, 91 with glaucoma, and 88 controls) from the ophthalmology clinics of a Montreal hospital from September 2009 until December 2011. Depressive symptoms were assessed using the Geriatric Depression Scale Short Form (GDS-15). Life space was measured using the Life Space Assessment. Logistic regression was used to adjust for demographic, health, and social factors, and mediation was assessed using the methods of Baron and Kenny. RESULTS There were 78 people (25%) meeting the criteria for depression in the cohort. All three groups with eye disease were more likely to be depressed than the control group after adjusting for age, sex, ethnicity, education, cognitive score, limitations in activities of daily living, social support, and lens opacity (P < 0.05). Life space and limited activities due to a fear of falling appeared to mediate the relationship between eye disease and depression. CONCLUSIONS Visually limiting eye disease is associated with depression in older adults. Further research on interventions to prevent depression in patients with eye disease is warranted and should consider strategies to alleviate mobility limitation. Greater attention from families, physicians, and society to the mental health needs and mobility challenges of patients with eye disease is needed.

Video Surveillance of Medication Intake

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

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

The head trajectory is an interesting source of information for behavior recognition and can be very useful for video surveillance applications, especially for fall detection. Consequently, much work has been done to track the head in the 2D image plane using a single camera or in a 3D world using multiple cameras. Tracking the head in real-time with a single camera could be very useful for fall detection. Thus, in this article, an original method to extract the 3D head trajectory of a person in a room is proposed using only one calibrated camera. The head is represented as a 3D ellipsoid, which is tracked with a hierarchical particle filter based on color histograms and shape information. Experiments demonstrated that this method can run in quasi-real-time, providing reasonable 3D errors for a monocular system. Results on fall detection using the head 3D vertical velocity or height obtained from the 3D trajectory are also presented.