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Human Walking Direction Detection Using Wireless Signals, Machine and Deep Learning Algorithms.

Hanan Awad Hassan Ali1,2, Shinnazar Seytnazarov1

  • 1Faculty of Computer Science and Engineering, Innopolis University, 420500 Innopolis, Russia.

Sensors (Basel, Switzerland)
|December 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel device-free method for recognizing human activities and indoor positioning using wireless signals. The approach accurately identifies walking direction across diverse environments and individuals, achieving high accuracy rates.

Keywords:
Wi-Fi signalschannel state information (CSI)human activity recognitionwalking direction detection

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Signal Processing

Background:

  • Device-free activity recognition and indoor positioning using wireless signals are increasingly important.
  • Existing methods for detecting human walking direction struggle with environmental and individual variations.

Purpose of the Study:

  • To develop a robust and adaptable approach for identifying human walking direction using wireless signal characteristics.
  • To overcome the limitations of current methods in handling environmental changes and diverse user populations.

Main Methods:

  • Utilized Channel State Information (CSI) from wireless signals.
  • Applied a Hampel filter for outlier removal and Discrete Wavelet Transform (DWT) for noise reduction and feature extraction.
  • Employed machine learning and deep learning algorithms for walking direction identification.

Main Results:

  • Achieved high accuracy rates: 92.9% (classroom), 95.1% (meeting room), and 89% (both rooms) for untrained data.
  • Demonstrated effectiveness across different genders, heights, and environments.
  • Validated the approach's adaptability and robustness.

Conclusions:

  • The proposed method effectively detects human walking direction in diverse indoor settings.
  • Machine and deep learning integration enables low-cost, device-free human activity detection.
  • This approach offers a promising solution for advanced indoor sensing applications.