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This paper introduces Wisture, a new online machine learning solution forrecognizing touch-less dynamic hand gestures on a smartphone. Wisture relies onthe standard Wi-Fi Received Signal Strength (RSS) using a Long Short-TermMemory (LSTM) Recurrent Neural Network (RNN), thresholding filters and trafficinduction. Unlike other Wi-Fi based gesture recognition methods, the proposedmethod does not require a modification of the smartphone hardware or theoperating system, and performs the gesture recognition without interfering withthe normal operation of other smartphone applications.  We discuss the characteristics of Wisture, and conduct extensive experimentsto compare its performance against state-of-the-art machine learning solutionsin terms of both accuracy and time efficiency. The experiments include a set ofdifferent scenarios in terms of both spatial setup and traffic between thesmartphone and Wi-Fi access points (AP). The results show that Wisture achievesan online recognition accuracy of up to 94% (average 78%) in detecting andclassifying three hand gestures.

Wisture: RNN-based Learning of Wireless Signals for Gesture Recognition in Unmodified Smartphones