Wisture: Touch-Less Hand Gesture Classification in Unmodified Smartphones Using Wi-Fi Signals

Abstract

This paper introduces Wisture, a new online machine learning solution for recognizing touch-less hand gestures on a smartphone (mobile device). Wisture relies on the standard Wi-Fi received signal strength measurements, long short-term memory recurrent neural network (RNN) learning method, thresholding filters, and a traffic induction approach. Unlike other Wi-Fi-based gesture recognition methods, the proposed method does not require a modification of the device hardware or the operating system and performs the gesture recognition without interfering with the normal operation of other smartphone applications. We discuss the characteristics of Wisture and conduct extensive experiments to compare the performance of the RNN learning method against the state-of-the-art machine learning solutions regarding both accuracy and efficiency. The experiments include a set of different scenarios with a change in spatial setup and network traffic between the smartphone and Wi-Fi access points. The results show that Wisture achieves an online gesture recognition accuracy of up to 93% (average 78%) in detecting and classifying three gestures.