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Fall detection method using Wi-Fi channel state information
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Paper Abstract

Aiming at the problems of high cost and complex deployment of traditional human behavior recognition method system, a method for obtaining channel state information (CSI) for human behavior recognition using commercial Wi-Fi equipment is proposed. Using the amplitude and phase characteristics in the CSI as the base signal, the power spectrum entropy is used as a new feature to build a fingerprint library. The support vector machine (SVM) based on artificial fish swarm algorithm (AFSA) is used to classify and identify the action. The optimization of the classification is achieved by optimizing the parameter penalty factor and kernel function parameters in the SVM. According to the verification of real environmental data, the average recognition rate reached 94.64%.

Paper Details

Date Published: 31 December 2019
PDF: 6 pages
Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 1138410 (31 December 2019); doi: 10.1117/12.2559790
Show Author Affiliations
Yaxin Ran, Yunnan Univ. of Information Science and Engineering (China)
Jiang Yu, Yunnan Univ. of Information Science and Engineering (China)
Jun Chang, Yunnan Univ. of Information Science and Engineering (China)
Zheng Zhang, Yunnan Univ. of Information Science and Engineering (China)


Published in SPIE Proceedings Vol. 11384:
Eleventh International Conference on Signal Processing Systems
Kezhi Mao, Editor(s)

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