Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
This paper proposes a method of human states estimation based on sensor networks. From a point of view of human state estimation, most of the previous researches depend on given knowledge, such as template matching. However, it is difficult to adjust flexibly to complicated environments such as houses or offices. Therefore, temporal features extractions and learning methods are very important. We propose a learning method of fuzzy spiking neural network based on a time series of measured data. Furthermore, we discuss the effectiveness of the proposed method through an experimental result in a living room.
