Citation

Yu M, Rhuma A, Naqvi S, Wang L, Chambers J. IEEE Trans. Inf. Technol. Biomed. 2012; 16(6): 1274-1286.

Copyright

(Copyright © 2012, Institute of Electrical and Electronics Engineers)

DOI

10.1109/TITB.2012.2214786

PMID

22922730

Abstract

We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.

Language: en