Recognizing human activities from multi-modal sensors

Authors:
Shu Chen;Yan Huang
Affiliations:
Department of Computer Science & Engineering, University of North Texas, Denton, TX;Department of Computer Science & Engineering, University of North Texas, Denton, TX
Venue:
ISI'09 Proceedings of the 2009 IEEE international conference on Intelligence and security informatics
Year:
2009

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Abstract

This paper describes a method of detecting and monitoring human activities which are extremely useful for understanding human behaviors and recognizing human interactions in a social network. By taking advantage of current wireless sensor network technologies, physical activities can be recognized through classifying multi-modal sensors data. The result shows that high recognition accuracy on a dataset of 6 daily activities of one carrier can be achieved by using suitable classifiers.