Device-free Human Activity Recognition Based on Random Subspace Classifier Ensemble

摘要

In recent years, Channel State Information (CSI)-based Human activity recognition (HAR) has received extensive attention for its low cost and privacy protection property. Existing CSI-based HAR systems utilize single classifier to accomplish recognition, which results in poor robustness and relatively low recognition accuracy. In this paper, a random subspace classifier ensemble method is proposed for classification, which utilizes the frequency domain feature instead of the time domain feature and each kind of feature is selected in the same amount. We evaluate its performance both in typical indoor and outdoor environment. Our results show, the recognition accuracy can reach 91.2% and 90.2% in outdoor and indoor environment, respectively.