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31 December 2019 Fall detection method using Wi-Fi channel state information
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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%.
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Yaxin Ran, Jiang Yu, Jun Chang, and Zheng Zhang "Fall detection method using Wi-Fi channel state information", Proc. SPIE 11384, Eleventh International Conference on Signal Processing Systems, 1138410 (31 December 2019); https://doi.org/10.1117/12.2559790
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