A Practical Approach Implementing a Wearable Human Activity Detector for the Elderly Care

Abstract

Human activity recognition is widely researched in the various filed these days. For the aged care, the one of the most important activities of old people is fall, since it causes often serious physical and psychological results. Many researchers have studied human activity recognition techniques in various domains; however none released to a commercial product satisfying the old people requirements, which are comfortable to wear it, weight-lighted and having exact accuracy to detect emergency activity and longer battery durance. Thus, to address them, we propose a practical approach procedure for getting best minimum feature sets and classification accuracy. We also do experiments for comparing the two features reduction techniques and four classification techniques in order to discriminate five each basic human activities, such as fall for the aged care, walking, hand related shocks, walking with walker and lastly steady activity which includes no movement and slow arbitrary hand and body motions.