Showing papers by "Jacqueline Rousseau published in 2011"

Robust Video Surveillance for Fall Detection Based on Human Shape Deformation

Abstract: 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.

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

Abstract: 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 from depth map video sequences

Abstract: 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.

Age-related eye disease and mobility limitations in older adults.

Abstract: Purpose: To examine the extent of mobility limitations in patients with age-related macular degeneration (AMD), glaucoma, or Fuchs corneal dystrophy as compared to a control group of older adults with good vision. Methods: We recruited 272 patients (68 with AMD, 49 with Fuchs, 82 with glaucoma, and 73 controls) from the ophthalmology clinics of Maisonneuve-Rosemont Hospital (Montreal, Canada) to participate in a cross-sectional study from September 2009 until February 2011. Control patients who had normal visual acuity and visual field were recruited from the same clinics. Questionnaire (life space, falls, driving) and performance-based (one-legged balance test, timed Up and Go (TUG) test) mobility data were collected, visual acuity, contrast sensitivity, and visual field were measured, and the medical record was reviewed. Results: The three eye diseases were associated with different patterns of mobility limitations. Patients with glaucoma had the most types of mobility limitations as they had reduced life space scores, had worse TUG scores, were less likely to drive, and were more likely to have poor balance than the control group (P Language: en

Video Surveillance for Fall Detection

Abstract: 1.1 Context Developed countries have to face the growing population of seniors. In Canada for example, while one Canadian out of eight was older than 65 years old in 2001, this proportion will be one out of five in 2026 (PHAC, 2002), due in particular to the “baby boomers” post-world war II and the increase of life expectancy. Several studies (Chappell et al., 2004; Senate, 2009) have shown that helping elderly people staying at home is interesting from a human perspective, but also from a financial perspective. Hence the interest to develop new healthcare systems to ensure the safety of elderly people at home. Falls are one of the major risk for seniors living alone at home, often causing severe injuries. The risk is amplified if the person cannot call for help. Usually, wearable fall devices are used to detect falls. For example, an elderly person can call for help using a push button (DirectAlert, 2010), but it is useless if the person is immobilized or unconscious after the fall. Automatic wearable devices are more interesting as no human intervention is required. Some are based on accelerometers (Kangas et al., 2008; Karantonis et al., 2006) which detect the magnitude and the direction of the acceleration. Others are based on gyroscopes (Bourke & Lyons, 2008) which measure the body orientation. A combination of an accelerometer and a gyroscope was used by (Nyan et al., 2008) to detect falls at an earlier stage. The major drawback of these technologies is that these sensors are often embarrassing to wear, and require batteries which need to be replaced or recharged regularly for adequate functioning. Floor vibration-based fall detector (Alwan et al., 2006) can also be used to detect falls but depends on the floor dynamics. This idea has been successfully improved by (Zigel et al., 2009) by adding a sound sensor. They obtained high detection rates, but they admitted that low-impact real human falls may not be detected. Video surveillance offers a new and promising solution for fall detection, as no body-worn devices are needed. For this purpose, a (possibly miniaturized) camera network is placed in the elderly apartment to automatically detect a fall to prevent an emergency center or the family.

The Disability Creation Process, conceptual scheme (Fougeyrollas, Cloutier, Bergeron, Côté, Côté & St-Michel, 1998)

Abstract: Copyright information:Taken from "Rehabilitation needs for older adults with stroke living at home: perceptions of four populations"http://www.biomedcentral.com/1471-2318/7/20BMC Geriatrics 2007;7():20-20.Published online 13 Aug 2007PMCID:PMC1994951. CRIPPH 1998. . 1(418)529-9141, p.6202. *Authors of this manuscript have added subcategories to the original scheme to facilitate the understanding of text.