论文信息 - A Human State Estimation Method Using Fuzzy Based System

A Human State Estimation Method Using Fuzzy Based System

When we use a MEMS based monitoring system for monitoring human behavior, several kinds of numerical data can be measured. In order to develop an intelligent monitoring system with small size, we need to minimize the power consumption. Therefore, we need to develop a simple intelligent system. We have proposed a behavior estimation method which consists of a fuzzy rule based system to estimate the subject $B!G (Bs behavior. In the proposed method, two steps of abstraction for numerical data extract some human behaviors from numerical data. The proposed method can estimate only human behavior such as $B!H (Bwalking $B!I (B or $B!H (Bsitting $B!I (B etc. In this paper, we propose an estimation method that can estimate both human state and behavior. Our proposed method can estimate human state using both heart rate from ECG and human behavior obtained from acceleration sensors. From the results of our proposed method, we show the effectiveness of our proposed method.

[1] Hisao Ishibuchi,et al. Selecting fuzzy if-then rules for classification problems using genetic algorithms , 1995, IEEE Trans. Fuzzy Syst..

[2] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.

[3] Takayuki Fujita,et al. Human Activity Monitoring System Using MEMS Sensors and Machine Learning , 2008 .

[4] Manabu Nii,et al. Extraction of human behavior from human activity monitoring data using MEMS sensors , 2011, 2011 IEEE Workshop on Robotic Intelligence In Informationally Structured Space.

[5] H. Ishibuchi,et al. Distributed representation of fuzzy rules and its application to pattern classification , 1992 .

[6] Takayuki Fujita,et al. Action Estimation from Human Activity Monitoring Data Using Soft Computing Approach , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[7] Hideo Tanaka,et al. Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms , 1994, CVPR 1994.

[8] Hisao Ishibuchi,et al. Pattern classification by distributed representation of fuzzy rules , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.