Citation

Popescu M, Mahnot A. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009; 1: 3505-3508.

Affiliation

Health Management and Informatics Department, University of Missouri, Columbia, MO 65211, USA.

Copyright

(Copyright © 2009, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/IEMBS.2009.5334521

PMID

19964801

Abstract

Falling represents a major health concern for the elderly. To address this concern we proposed in a previous paper an acoustic fall detection system, FADE, composed of a microphone array and a motion detector. FADE may help the elderly living alone by alerting a caregiver as soon as a fall is detected. A crucial component of FADE is the classification software that labels an event as a fall or part of the daily routine based on its sound signature. A major challenge in the design of the classifier is that it is almost impossible to obtain realistic fall sound signatures for training purposes. To address this problem we investigate a type of classifier, one-class classifier, that requires only examples from one class (i.e., non-fall sounds) for training. In our experiments we used three one-class (OC) classifiers: nearest neighbor (OCNN), SVM (OCSVM) and Gaussian mixture (OCGM). We compared the results of OC to the regular (two-class) classifiers on two datasets.

Language: en