Citation

Bourke AK, O'Donovan KJ, Nelson J, Olaighin GM. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2008; 1: 2832-2835.

Affiliation

Wireless Access Research Centre, Department of Electronic and Computer Engineering, University of Limerick, Ireland.

Copyright

(Copyright © 2008, IEEE (Institute of Electrical and Electronics Engineers))

DOI

unavailable

PMID

19163295

Abstract

Falls in the elderly population are a major problem for today's society. The immediate automatic detection of such events would help reduce the associated consequences of falls. This paper describes the development of an accurate, accelerometer-based fall detection system to distinguish between Activities of Daily Living (ADL) and falls. It has previously been shown that falls can be distinguished from normal ADL through vertical velocity thresholding using an optical motion capture system. In this study however accurate vertical velocity profiles of the trunk were generated by simple signal processing of the signals from a tri-axial accelerometer (TA).

Language: en