2015 · 290 citations · 25 references
Multimodal Human Computer InterfaceMobile SensingEngineeringAssistive TechnologyMobile InteractionCommodity Wifi CardsHand TrajectoryBiometricsEye TrackingWearable TechnologyEducationWireless NetworkingComputer ScienceMobile ComputingTechnologySignal StrengthGesture ProcessingGesture Recognition
This paper demonstrates that it is possible to leverage WiFi signals from commodity mobile devices to enable hands-free drawing in the air. While prior solutions require the user to hold a wireless transmitter, or require custom wireless hardware, or can only determine a pre-defined set of hand gestures, this paper introduces WiDraw, the first hand motion tracking system using commodity WiFi cards, and without any user wearables. WiDraw harnesses the Angle-of-Arrival values of incoming wireless signals at the mobile device to track the user's hand trajectory. We utilize the intuition that whenever the user's hand occludes a signal coming from a certain direction, the signal strength of the angle representing the same direction will experience a drop. Our software prototype using commodity wireless cards can track the user's hand with a median error lower than 5 cm. We use WiDraw to implement an in-air handwriting application that allows the user to draw letters, words, and sentences, and achieves a mean word recognition accuracy of 91%.
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Whole-home gesture recognition using wireless signals
Qifan Pu, Sidhant Gupta, Shyamnath Gollakota et al. · 2013 · 1.1K citations
Fadel Adib, Dina Katabi · 2013 · 681 citations · Full text