Human presence detection and activity event classification are of importance to a variety of context-awareness applications such as e-Healthcare, security and low impact building. However, existing Radio Frequency IDentification (RFID) tags, wearables and passive infrared (PIR) approaches require the user to carry dedicated electronic devices, suffer low detection accuracy and false alarm problems. This paper proposes a novel system for non-invasive human sensing by analyzing the Doppler information contained in the human reflections of WiFi signal. Doppler information is insensitive to the stationary objects, thus no requirement on scenario-specific calibration that makes it ideal for human sensing. We also introduce the time-frequency domain feature vectors of WiFi Doppler information for the Support Vector Machine (SVM) classifier towards activity event recognition. The proposed methodology is evaluated on a Software Defined Radio (SDR) system together with the experiment of five different events. The results indicate the proposed system is sufficient for indoor context-awareness with 95% overall accuracy in event classification and 84.8% PPV & 93.3% TPR in human presence detection which outperforms the traditional Received Signal Strength (RSS) approach (62.5% PPV and 83.3% TPR).
- Passive Radar
- Activity Recognition
ASJC Scopus subject areas
Li, W., Tan, B., & Piechocki, R. (2018). A WiFi-Based Passive Sensing System for Human Presence and Activity Event Classification. IET Wireless Sensor Systems, 8(6), 276-283. https://doi.org/10.1049/iet-wss.2018.5113