Recognizing human activities from multi-modal sensors

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.