The ever evolving informatics technology has gradually bounded human and computer in a compact way. Understanding user behavior becomes a key enabler in many fields such as sedentary-related healthcare, human-computer interaction (HCI) and affective computing. Traditional sensor-based and vision-based user behavior analysis approaches are obtrusive in general, hindering their usage in realworld. Therefore, in this article, we first introduce WiFi signal as a new source instead of sensor and vision for unobtrusive user behaviors analysis. Then we design BeSense, a contactless behavior analysis system leveraging signal processing and computational intelligence over WiFi channel state information (CSI). We prototype BeSense on commodity low-cost WiFi devices and evaluate its performance in realworld environments. Experimental results have verified its effectiveness in recognizing user behaviors.
In this paper, we present WoSense, a device-free and real-time behavior analysis system leveraging only WiFi infrastructures. WoSense aims to remotely recognize various human behaviors like surfing, gaming and working around computers, which are considered to be an essential part of our daily lives both at work and at home. The key of WoSense is to exploit the signal distortions on channel data caused by gestures like finger and hand movements, and then identify possible behaviors via the composite of gestures. Therefore, two critical challenges need to be tackled: how to enhance such insignificant distortions led by micro gestures, how to segment the continuous signals according to different gestures in a real-time manner? For the former, instead of relying on empirical studies like our rivals, WoSense offers a Fresnel zone based model with theoretic understandings between the gestures and signal distortions. For the latter, WoSense employs a light-weight automatic segmentation algorithm exploring the variance feature of channel data. We prototype WoSense on the commodity low-cost WiFi devices and evaluate its performance in extensive real- world experiments. WoSense achieves an average 96.77 accuracy for distinguishing the typing and mousing gestures, and 92.5 accuracy for recognizing four different behaviors, i.e., stationary, surfing, gaming and working.