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Indexed by:会议论文

Date of Publication:2017-01-01

Included Journals:CPCI-S

Volume:2017-December

Page Number:1-6

Abstract:WiFi-based Human activity recognition has attracted attention in the human-computer interaction, smart homes, and security monitoring fields. We first construct a WiFi-based activity dataset, namely WiAR, to provide a benchmark for existing works. Then, we leverage the moving variance of CSI to detect the start and end of activity. Moreover, we present K-means-based subcarrier selection mechanism according to subcarrier's sensitivity on human activity to enhance the robustness of human activity recognition. Finally, we leverage several classification algorithms to evaluate the performance of WiAR. Our results show that WiAR satisfies primary demand and achieves an average accuracy of greater than 93% using SVM, 80% using kNN, Random forest, and Decision tree.