Recently, researchers around the world have been striving to develop and modernize human–computer interaction systems by exploiting advances in modern communication systems. The priority in this field involves exploiting radio signals so human–computer interaction will require neither special devices nor vision-based technology. In this context, hand gesture recognition is one of the most important issues in human–computer interfaces. In this paper, we present a novel device-free WiFi-based gesture recognition system (WiGeR) by leveraging the fluctuations in the channel state information (CSI) of WiFi signals caused by hand motions. We extract CSI from any common WiFi router and then filter out the noise to obtain the CSI fluctuation trends generated by hand motions. We design a novel and agile segmentation and windowing algorithm based on wavelet analysis and short-time energy to reveal the specific pattern associated with each hand gesture and detect duration of the hand motion. Furthermore, we design a fast dynamic time warping algorithm to classify our system’s proposed hand gestures. We implement and test our system through experiments involving various scenarios. The results show that WiGeR can classify gestures with high accuracy, even in scenarios where the signal passes through multiple walls.
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