A multi-sensor approach for People Fall Detection in home environment

A. Leone 1 G. Diraco 1 C. Distante 1 P. Siciliano 1 M. Malfatti 2 L. Gonzo 2 M. Grassi 3 A. Lombardi 3 G. Rescio 3 P. Malcovati 3 V. Libal 4 J. Huang 4 G. Potamianos 4
1 IMM - Istituto per la Microelettronica e Microsistemi [Catania]
2 Integrated Optical Sensor Group
3 Department of Electrical Engineering
4 IBM Thomas J. Watson Research Center
Abstract : This paper presents a hardware and software framework for reliable fall detection in the home environment, with particular focus on the protection and assistance to the elderly. The integrated prototype includes three di®erent sensors: a 3D Time-Of-Flight range camera, a wearable MEMS accelerometer and a microphone. These devices are connected with custom interface circuits to a central PC that collects and processes the information with a multi-threading approach. For each of the three sensors, an optimized algorithm for fall-detection has been developed and benchmarked on a collected mulitimodal database. This work is expected to lead to a multi-sensory approach employing appropriate fusion techniques aiming to improve system precision and recall.
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Communication dans un congrès
Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2 2008, Oct 2008, Marseille, France. 2008
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A. Leone, G. Diraco, C. Distante, P. Siciliano, M. Malfatti, et al.. A multi-sensor approach for People Fall Detection in home environment. Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications - M2SFA2 2008, Oct 2008, Marseille, France. 2008. 〈inria-00326739〉

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