Interdisciplinary design of a pervasive fall handling system

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Camera Based Fall Detection Using Multiple Features Validated with Real Life Video

More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again unaided. The lack of timely aid can lead to severe complications such as dehydration, pressure ulcers and death. A camera based fall detection system can provide a solution. In this paper we compare four different fall features extracted from the dominant foreground object, as well as various combinations thereof. All tests are executed using real life data, which has been recorded at the home of 4 elderly, containing 24 falls. Experiments indicate that a fall detector based on a combination of aspect ratio, head speed and fall angle is preferred. The preliminary detector, which still has a substantial false alarm rate with a precision of 0.257(±0.073) and a promising recall of 0.896(±0.194), gives insights for further improvement as is discussed.Debard G., Karsmakers P., Deschodt M., Vlaeyen E., Van den Bergh J., Dejaeger E., Milisen K., Goedemé T., Tuytelaars T., Vanrumste B., ''Camera based fall detection using multiple features validated with real life video'', Workshop proceedings 7th international conference on intelligent environments - IE 2011, vol. 10, pp. 441-450, July 25-28, 2011, Nottingham, UK.status: publishe

The detection of abnormal events in activities of daily living using a Kinect sensor

The growing elderly population presents a challenge on the resources of carers and assisted living communities. This has led to various projects in remote automated home monitoring in order to keep the elderly in their own home environment longer. These have the promise of alleviating the strain on support services, and the benefits of keeping people in their existing familiar community environment. Such monitoring typically involves a myriad of sensors attached to the environment and person, so as to acquire rich enough data to determine the actions of the person being monitored. In this research, an algorithm based around the Microsoft Kinect, its 3D camera and person detection features, is presented for monitoring activities of daily living. The Kinect was originally released as a device to improve human interaction in gaming for the Xbox 360 game console. In this approach, training Data is obtained and then preprocessed using Kinect’s in build person recognition. Collection of training data is necessary is because the process in which Activities of Daily Living (ADLs) are completed varies from person to person, and therefore no generic template to recognise ADLs exists. The research attempts to infer representations of ADLs through features related to the spatial position of the person in the field of view of the Kinect camera, which is divided into a grid of several data points. Once this representation or “ADL signature” is obtained, ADLs are classified live in unknown data using a combination of distance measures, and abnormal events are detected amongst these ADLs using an automatically generated threshold value. This system has the potential to replace the various sensors in the camera’s field of view, and provide a system that accurately analyses the behaviours of the elderly to provide doctors and carers with valuable observational data