Abstract
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.
Highlights
With the rapid development of Human–Computer Interaction (HCI) technologies, gesture recognition is gaining increasing attentions
We propose WiHand, a location independent gesture recognition system based on commodity WiFi devices
We propose WiHand, a location independent gesture recognition system based on commodity WiFi signals
Summary
With the rapid development of Human–Computer Interaction (HCI) technologies, gesture recognition is gaining increasing attentions. While state-of-the-art systems obtain reasonable performance in their given scenario, there is a key limitation considering the existing gesture recognition systems based on commercial WiFi devices. As a matter of fact, the action recognition model trained on a specific environment can hardly perform well on actions collected in a different environment To address this challenge, we propose WiHand, a location independent gesture recognition system based on commodity WiFi devices. Virmani and Shahzad [33] used a translation function that can generate virtual samples for different positions to realize position agnostic recognition They are both labor intensive and energy consuming, making it difficult to deploy on mobile devices. We propose a deviation based gesture signal extraction algorithm utilizing the deviation changes of different subcarriers. We propose WiHand, a location independent gesture recognition system based on commodity WiFi signals.
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