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Step Length Estimation Using the RSSI Method in Walking and Jogging Scenarios.

Zanru Yang1, Le Chung Tran1, Farzad Safaei1

  • 1School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong, NSW 2522, Australia.

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Summary

This study estimates human step length using wireless channel properties and received signal strength indicator (RSSI). A novel filtering technique based on on-ankle path loss histograms achieves highly accurate step length estimation.

Keywords:
RSSIdata histogramdistance estimationgait speedon-ankle path lossstep length estimationstrike length estimationtwo-term Gaussian distribution

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Area of Science:

  • Wearable technology
  • Biomedical engineering
  • Signal processing

Background:

  • Accurate human step length estimation is crucial for gait analysis and health monitoring.
  • Existing methods often rely on inertial sensors or complex infrastructure.
  • Wireless channel properties offer a novel, unobtrusive approach.

Purpose of the Study:

  • To develop and validate a method for estimating human step length using wireless channel properties.
  • To introduce a new filtering technique for improving the accuracy of step length estimation.
  • To assess the performance of the proposed method across various walking and jogging scenarios.

Main Methods:

  • Utilized wearable transceivers to measure received signal strength indicator (RSSI) between ankles.
  • Converted RSSI to on-ankle path loss and analyzed its histogram, identifying two distinct humps.
  • Developed a two-term Gaussian fitting curve model and a novel filtering technique to set path loss thresholds for step length estimation.

Main Results:

  • The histogram of on-ankle path loss exhibited two humps, with the second related to maximum path loss and step length.
  • The proposed filtering technique effectively removed path loss outliers, establishing accurate upper and lower thresholds.
  • Achieved high accuracy in step length estimation with errors as low as 4.40 mm (indoor jogging).

Conclusions:

  • Wireless channel properties, specifically on-ankle path loss, can be reliably used for human step length estimation.
  • The novel filtering technique significantly enhances the accuracy of step length estimation.
  • The developed method demonstrates potential for practical applications in gait analysis and wearable health monitoring.