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Device-Free Wireless Sensing for Gesture Recognition Based on Complementary CSI Amplitude and Phase.

Zhijia Cai1,2, Zehao Li2, Zikai Chen2

  • 1School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
Summary

This study presents a WiFi-based system for device-free human gesture recognition (HGR) using channel state information (CSI). The system achieves high accuracy by jointly analyzing CSI amplitude and phase, demonstrating its effectiveness in practical scenarios.

Keywords:
WiFi-based wireless sensingchannel state informationhuman gesture recognition

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Area of Science:

  • Wireless communication and sensing
  • Human-computer interaction
  • Signal processing

Background:

  • Wireless sensing offers contactless and non-line-of-sight (NLOS) capabilities for human behavior recognition.
  • Channel State Information (CSI) dynamics capture intricate details of human movements.
  • Existing methods may face challenges with noise, multi-path effects, and feature selection.

Purpose of the Study:

  • To develop an effective device-free human gesture recognition (HGR) system using WiFi.
  • To leverage complementary CSI amplitude and phase for enhanced recognition accuracy.
  • To create a robust system through advanced data processing and feature selection.

Main Methods:

  • A linear transform-based method preprocesses CSI to mitigate phase offset, noise, and multi-path interference.
  • Six time and frequency domain features are extracted from both CSI amplitude and phase.
  • A feature selection algorithm combining filtering and Principal Component Analysis (PCA) refines the feature subspace.
  • A Support Vector Machine (SVM)-based stacking algorithm is employed for gesture classification.

Main Results:

  • The proposed HGR system demonstrates high performance in practical experimental settings.
  • The system achieves an average accuracy of 98.3% for human gesture recognition.
  • The F1-score for the gesture recognition task exceeds 97%.

Conclusions:

  • The joint exploitation of CSI amplitude and phase significantly improves device-free HGR.
  • The proposed data processing and feature selection methods enhance system robustness and accuracy.
  • This WiFi-based sensing approach offers a promising solution for contactless human behavior monitoring.