NDLI: Towards a Single Sensor Passive Solution for Automated Fall Detection

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Towards a Single Sensor Passive Solution for Automated Fall Detection

Content Provider	PubMed Central
Author	Belshaw, Michael Babak, Taati Snoek, Jasper Alex, Mihailidis
Copyright Year	2011
Abstract	Falling in the home is one of the major challenges to independent living among older adults. The associated costs, coupled with a rapidly growing elderly population, are placing a burden on healthcare systems worldwide that will swiftly become unbearable. To facilitate expeditious emergency care, we have developed an artificially intelligent camera-based system that automatically detects if a person within the field-of-view has fallen. The system addresses concerns raised in earlier work and the requirements of a widely deployable in-home solution. The presented prototype utilizes a consumer-grade camera modified with a wide-angle lens. Machine learning techniques applied to carefully engineered features allow the system to classify falls at high accuracy while maintaining invariance to lighting, environment and the presence of multiple moving objects. This paper describes the system, outlines the algorithms used and presents empirical validation of its effectiveness.
Related Links	http://dx.doi.org/10.1109/iembs.2011.6090506
Starting Page	1773
File Format	PDF
ISSN	1557170X
Journal	Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference
Volume Number	2011
Language	English
Publisher Date	2011-08-01
Access Restriction	Open
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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