Abstract
Nowadays, the aging society is one of big problems in Japan, but the number of caregivers and therapists for elderly people is not enough. Recently, we can get various types of information services easily with the development of information technology, robot technology and network technology. Especially the introduction of robots instead of caregivers and therapists for elderly people is one of the possible solutions. For such a system, human behavior recognition is one of important techniques for a robot to interact with elderly people. Most of previous methods for human behavior recognition are based on off-line statistic approaches for modeling human behaviors. In this paper, we discuss an on-line learning method for human behavior recognition using sensor networks. The developed sysem is composed of sensor network system, robot system, human interface system, and database management server. First of all, we apply a spiking neural network as a learning architecture based on the time series of measured data. Furthermore, we propose a hierarchical learning structure using the spiking neural network for classification of the spatiotemporal patterns. Finally, we discuss the effectiveness of the proposed method through experimental results of human behavior recognition in a living room.
