Features extraction and analysis for device-free human activity recognition based on channel statement information in b5G wireless communications

Abstract

Abstract Features extraction and analysis for human activity recognition (HAR) have been studied for decades in the 5th generation (5G) and beyond the 5th generation (B5G) era. Nowadays, with the extensive use of unmanned aerial vehicles (UAVs) in the civil field, integrating wireless signal receivers on UAVs could be a better choice to receive hearable signals more conveniently. In recent years, the HAR system based on CSI based on WiFi radar has received widespread attention due to its low cost and privacy protection property. However, in the existing CSI-based HAR system, there are two disadvantages: (1) The detection threshold is manually set, which limits its adaptability and immediacy in different wireless environments. (2) A sole classifier is used to complete the recognition, resulting in poor robustness and relatively low recognition accuracy. In this paper, we propose a CSI-based device-free HAR (CDHAR) system with WiFi-sensing radar integrated on UAVs to recognize everyday human activities. Firstly, by using machine learning, CDHAR applies kernel density estimation (KDE) to obtain adaptive detection thresholds to complete the extraction of activity duration. Second, we proposed a random subspace classifier ensemble method for classification, which applies the frequency domain feature instead of the time domain feature, and we choose each kind of feature in the same amount. Finally, we prototype CDHAR on commercial WiFi devices and evaluate its performance in both indoor environment and outdoor environments. The experiment results tell that even if experimental scenario varies, the accuracy of activity durations extraction can reach 98% and 99.60% whether in outdoor or indoor environments. According to the extracted data, the recognition accuracy in outdoor and indoor environments can reach 91.2% and 90.2%, respectively. CDHAR ensures high recognition accuracy while improving the adaptability and instantaneity.

Keywords

• Features extraction and analysis
• Channel state information
• Human activity recognition
• Machine learning