Vision-Based Human Activity Recognition System Using Depth Silhouettes: A Smart Home System for Monitoring the Residents

Abstract

The increasing number of elderly people living independently needs especial care in the form of smart home monitoring system that provides monitoring, recording and recognition of daily human activities through video cameras, which offer smart lifecare services at homes. Recent advancements in depth video technologies have made human activity recognition (HAR) realizable for elderly healthcare applications. This study proposes a depth video-based HAR system to utilize skeleton joints features which recognize daily activities of elderly people in indoor environments. Initially, depth maps are processed to track human silhouettes and produce body joints information in the form of skeleton, resulting in a set of 23 joints per each silhouette. Then, from the joints information, skeleton joints features are computed as a centroid point with magnitude and joints distance features. Finally, using these features, hidden Markov model is trained to recognize various human activities. Experimental results show superior recognition rate, resulting up to the mean recognition rate of 84.33% for nine daily routine activities of the elderly.