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Improved method of step length estimation based on inverted pendulum model.

Qi Zhao1, Boxue Zhang1, Jingjing Wang1

  • 1School of Electronic and Information Engineering, Beihang University, Beijing, China.

International Journal of Distributed Sensor Networks
|June 19, 2018
PubMed
Summary
This summary is machine-generated.

This study presents a novel method for estimating step length using a waist-worn wearable device. The algorithm accurately measures walking step length, improving performance with increased walking speed.

Keywords:
Step lengthempirical mode decompositioninertia measurement unit sensorsinverted pendulum modelwearable computer

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Signal Processing

Background:

  • Accurate step length estimation is crucial for gait analysis, sports training, and pedestrian localization.
  • Traditional methods often rely on sensors placed on extremities, which can be cumbersome.
  • Wearable sensors on the trunk offer a less intrusive alternative for motion data acquisition.

Purpose of the Study:

  • To develop and validate an algorithm for estimating step length using data from a waist-worn wearable computer (eButton).
  • To leverage motion sensors on the trunk for step length estimation, avoiding the need for extremity-based sensors.

Main Methods:

  • Utilized a waist-worn wearable computer (eButton) equipped with motion sensors.
  • Applied the Direction Cosine Matrix (DCM) to transform vertical acceleration data from device coordinates to topocentric coordinates.
  • Employed Empirical Mode Decomposition (EMD) to mitigate integration-induced skew effects.

Main Results:

  • The proposed algorithm demonstrated effective step length estimation.
  • The Direction Cosine Matrix algorithm's accuracy improved by 1.69% to 3.56% as walking speed increased.
  • Experimental results confirmed the algorithm's robust performance across different walking speeds.

Conclusions:

  • The eButton-based system provides a viable solution for step length estimation.
  • The combination of DCM and EMD processing techniques enhances accuracy, particularly at higher walking speeds.
  • This approach offers a promising non-intrusive method for gait analysis and related applications.