![]() 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. Firstly, by using machine learning, CDHAR applies kernel density estimation (KDE) to obtain adaptive detection thresholds to complete the extraction of activity duration. 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. (2) A sole classifier is used to complete the recognition, resulting in poor robustness and relatively low recognition accuracy. ![]() 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. 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. 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. ![]() 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.
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