There has been a great interest in recognizing gestures using wireless communication signals. We are motivated in detecting extremely fine, subtle finger gestures with WiFi signals. We envision this technology to find applications in finger-gesture control, disabled-friendly devices, physical therapy etc. The requirements of mm-level sensitivity and user-friendly feature using existing WiFi signals pose great challenges. Here, we present Mudra, a fine-grained finger gesture recognition system which leverages WiFi signals to enable a near-human-to-machine interaction with finger motion.

Mudra uses a two-antenna receiver to detect and recognize finger gesture. It uses the signals received from one antenna to cancel the signal from the other. This "cancellation" is extremely sensitive to and enables us detect small variation in channel due to finger movements. Since Mudra decodes gestures with existing WiFi transmissions, Mudra enables gesture recognition without sacrificing WiFi transmission opportunities. Besides, Mudra is user-friendly with no need of user training. To demonstrate Mudra, we implement prototype on the NI-based SDR platform and use COTS WiFi adapter. We evaluate Mudra in a typical office environment. The results show that our system can achieve 96% accuracy.