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Real-Time Step Length Estimation in Indoor and Outdoor 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 new EWMA algorithm improves real-time accuracy and significantly reduces processing time for step length estimation.

Keywords:
RSSIexponential weighted moving averagepath lossstep length estimation

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Area of Science:

  • Human locomotion analysis
  • Wireless sensing technologies
  • Wearable sensor systems

Background:

  • Accurate human step length estimation is crucial for gait analysis and health monitoring.
  • Existing methods often require complex setups or lack real-time processing capabilities.
  • Received Signal Strength Indicator (RSSI) offers a promising, non-intrusive approach for step length measurement.

Purpose of the Study:

  • To develop a real-time human step length estimation method using wireless channel properties.
  • To introduce a novel Exponential Weighted Moving Average (EWMA) algorithm for recursive threshold updating.
  • To improve the efficiency and accuracy of step length estimation in both indoor and outdoor environments.

Main Methods:

  • Utilizing on-ankle path loss derived from RSSI measurements via developed wearable hardware.
  • Implementing an EWMA algorithm to recursively update upper and lower RSSI thresholds for step length calculation.
  • Processing data within a sliding time window for on-the-fly, real-time estimations.

Main Results:

  • The EWMA algorithm achieved comparable accuracy to previous methods, with errors as low as 3.02% (indoor) and 0.30% (outdoor).
  • Processing time for step length estimation was significantly reduced by 53.96% (indoor) and 60% (outdoor).
  • Optimal EWMA parameters were identified for various experimental scenarios.

Conclusions:

  • The proposed EWMA algorithm enables efficient and accurate real-time human step length estimation.
  • This wireless sensing approach offers a practical solution for gait analysis in diverse environments.
  • The method demonstrates a significant improvement in computational efficiency for wearable sensing applications.