Citation

Belshaw M, Taati B, Snoek J, Mihailidis A. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011; 2011: 1773-1776.

Copyright

(Copyright © 2011, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/IEMBS.2011.6090506

PMID

22254671

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.

Language: en