BeSense: Leveraging WiFi Channel Data and Computational Intelligence for Behavior Analysis
This addresses the problem of obtrusiveness in behavior analysis for applications like healthcare and HCI, but it is incremental as it adapts existing computational intelligence methods to a new data type.
The paper tackled unobtrusive user behavior analysis by introducing WiFi channel state information as a new data source, and the BeSense system achieved effective behavior recognition in real-world environments.
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.