Location-Free CSI Based Activity Recognition With Angle Difference of Arrival

Device-free activity recognition is an indispensable technology in Human-Computer Interaction (HCI). The activity recognition system based on WiFi signals relying on the wide coverage of WiFi makes HCI more convenient. The previous research on WiFi-based activity recognition system has achieved high recognition accuracy. While the challenge that activity recognition is limited to fixed location and complex background, remains unresolved. In this paper, we propose a location-free activity recognition system which leverages fine-grained channel state information (CSI) to recognize same activities regardless of different locations and background. With CSI recorded in the Network Interface Card (NIC), Angle Difference of Arrival (ADoA) is reckoned to eliminate the location and background information, which is only consistent with the activity tendency. Then the Principal Component Analysis (PCA) method is utilized to reduce the dimension and followed by curve smoothing to make the signal more smoother. Furthermore, Bidirectional Long Short-Term Memory (BiLSTM) network is selected as ideal training machine to deal with issues that are highly correlated with time series. We use two commercial wireless network cards in the typical life scene, and finally achieve 93.7 % of recognition accuracy.