Recently, WiFi-based gesture recognition has attracted increasing attention. Due to the sensitivity of WiFi signals to environments, an activity recognition model trained at a specific place can hardly work well for other places. To tackle this challenge, we propose WiHand, a location independent gesture recognition system based on commodity WiFi devices. Leveraging the low rank and sparse decomposition, WiHand separates gesture signal from background information, thus making it resilient to location variation. Extensive evaluations showed that WiHand can achieve an average accuracy of 93% for various locations. In addition, WiHand works well under through the wall scenario.
Towards Location Independent Gesture Recognition with Commodity WiFi Devices
