Robust gesture recognition method toward intelligent environment using Wi-Fi signals

Wi-Fi-based gesture recognition represents an emerging paradigm of human–computer interaction. While deep learning-based and model-based solutions are crucial for improving accuracy and generalization performance, they often depend heavily on extensive datasets and the widespread deployment of sensing devices. In this paper, we address the challenge involving limited sensing data and devices. We propose a signal-based gesture recognition method that leverages gesture coding based on stroke trajectory and feature representation based on dynamic phase changes. Specifically, we develop an endpoint detection algorithm to ensure precise gesture recognition. Additionally, a subcarrier selection algorithm is designed to select optimal subcarriers, capturing comprehensive gesture information. Extensive experiments are conducted to evaluate the performance. Results demonstrate an average accuracy of 93.67% for six gestures. This approach effectively mitigates the impact of location and environment factors on gesture characteristics, and reduces dependence on large quantity of samples and transceiver devices, obviating the need for model training.