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

Ding, Xue; Yu, Xiao; Zhong, Yi; Xie, Weiliang; Cai, Bowen; You, Minglei; Jiang, Ting

Abstract

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.

Citation

Ding, X., Yu, X., Zhong, Y., Xie, W., Cai, B., You, M., & Jiang, T. (2024). Robust gesture recognition method toward intelligent environment using Wi-Fi signals. Measurement, 231, Article 114525. https://doi.org/10.1016/j.measurement.2024.114525

• Journal Article Type: Article
• Acceptance Date: Mar 17, 2024
• Online Publication Date: Mar 20, 2024
• Publication Date: May 31, 2024
• Deposit Date: Apr 29, 2024
• Publicly Available Date: Mar 21, 2025
• Journal: Measurement
• Print ISSN: 0263-2241
• Electronic ISSN: 1873-412X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 231
• Article Number: 114525
• DOI: https://doi.org/10.1016/j.measurement.2024.114525
• Keywords: Gesture recognition; Wi-Fi; Intelligent environment; Wireless sensing; Signal-based method
• Public URL: https://nottingham-repository.worktribe.com/output/32754617
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S026322412400410X?via%3Dihub