Fall detection method using Wi-Fi channel state information

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%.