Citation

Khan ZA, Sohn W. Journal of the Chinese Institute of Engineers 2013; 36(4): 441-451.

Copyright

(Copyright © 2013)

DOI

10.1080/02533839.2012.731880

PMID

unavailable

Abstract

This article presents a vision-based human activity recognition (HAR) system to recognize abnormal activities among a set of daily-life activities of elderly people. A novel method is proposed for feature extraction by the combination of R-transform, principal component analysis (PCA) and independent component analysis (ICA). R-transform is used for periodic, scale, and translation invariant feature extraction. PCA is used for dimensions reduction and ICA is used for independent features extraction. The features are processed by k-means to get observation sequences which are used by hidden Markov model for HAR. The dataset consisting of six abnormal activities (backward fall, forward fall, chest pain, headache, vomit, and faint) and three normal activities (walk, raise one hand, and raise both hands) from the view angles (90 degrees, -90 degrees, 45 degrees, -45 degrees, and 0 degrees) is produced. Experimental results show average recognition rate of 86.86% for abnormal and normal activities from different view angles by our proposed system using (R-transform + PCA + ICA) as compared to R-transform, PCA, and ICA methods.

Language: en