A Fuzzy Inference-Based Spiking Neural Network for Behavior Estimation in Elderly Health Care System
Shuai Shao and Naoyuki Kubota
Graduate School of Systems Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
In recent years, population aging has become an important social issue. We hope to achieve an elderly health care system through technical means. In this study, we developed an elderly health care system. We chose to use environmental sensors to estimate the behavior of older adults. We found that traditional methods have difficulty solving the problem of excessive indoor environmental differences in different households. Therefore, we provide a fuzzy spike neural network. By modifying the sensitivity of input using a fuzzy inference system, we can solve the problem without additional training. In the experiment, we used temperature and humidity data to make an estimation of behavior in the bathroom. The results show that the system can estimate behavior with 97% accuracy and 78% sensitivity.
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