A Fuzzy Inference-Based Spiking Neural Network for Behavior Estimation in Elderly Health Care System

被引：3

作者：

Shao, Shuai ^{[1
]}

Kubota, Naoyuki ^{[1
]}

机构：

[1] Tokyo Metropolitan Univ, Grad Sch Syst Design, 6-6 Asahigaoka, Hino, Tokyo 1910065, Japan

来源：

JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 2019年 / 23卷 / 03期

关键词：

population aging; sensor network; information structure space; spiking neural network; fuzzy inference system; SENSOR;

D O I：

10.20965/jaciii.2019.p0528

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 house-holds. 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.

引用

页码：528 / 535

页数：8

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