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Wi-Fi sensing gesture control algorithm based on semi-supervised generative adversarial network.

Chao Wang1,2,3, Yinfan Ding1,2,3, Meng Zhou1,2,3

  • 1Joint Laboratory for International Cooperation of the Special Optical Fiber and Advanced Communication, Shanghai University, Shanghai, China.

Peerj. Computer Science
|December 9, 2024
PubMed
Summary
This summary is machine-generated.

A novel Wi-Fi sensing system uses channel state information (CSI) for non-contact gesture control in smart homes. An enhanced generative adversarial network (GAN) achieves 95.67% accuracy, outperforming traditional methods with limited data.

Keywords:
Gesture recognitionPattern recognitionWi-Fi networkWireless sensing

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Area of Science:

  • Human-Computer Interaction
  • Wireless Communication
  • Machine Learning

Background:

  • Existing smart home gesture control often requires specialized hardware or line-of-sight, limiting usability.
  • There is a growing need for non-contact, versatile gesture recognition systems within household environments.

Purpose of the Study:

  • To develop and evaluate a Wi-Fi-sensing gesture control system for smart homes.
  • To improve gesture recognition accuracy and efficiency using advanced machine learning techniques.
  • To enable robust gesture recognition even with limited labeled data and across different environments.

Main Methods:

  • Utilized the Fresnel region sensing model for theoretical investigation.
  • Collected and preprocessed Wi-Fi channel state information (CSI) for gesture data.
  • Employed dynamic feature extraction, Gramian Angular Summation Field (GASF) transform, and an enhanced generative adversarial network (GAN) with a classifier.
  • Implemented a semi-supervised learning algorithm for cross-scene gesture recognition.

Main Results:

  • Achieved 98.20% accuracy in gesture interception using an improved dynamic double threshold algorithm.
  • The semi-supervised GAN algorithm demonstrated an average accuracy of 95.67%, significantly outperforming LDA, LightGBM, and SVM.
  • The system maintained over 94% accuracy across various scenarios, showing high performance with limited labeled data.

Conclusions:

  • The developed Wi-Fi-sensing system offers a highly accurate and efficient solution for non-contact gesture control in smart homes.
  • The enhanced GAN and semi-supervised learning approach provide superior performance compared to traditional methods, especially in data-scarce situations.
  • This technology paves the way for more intuitive and seamless human-device interaction in smart environments.