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Automated Gait Analysis Based on a Marker-Free Pose Estimation Model.

Chang Soon Tony Hii1, Kok Beng Gan1, Nasharuddin Zainal1

  • 1Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia.

Sensors (Basel, Switzerland)
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Summary

This study presents an automated method for temporal gait analysis using MediaPipe Pose, offering a reliable and accessible tool for monitoring gait and evaluating rehabilitation effectiveness. The approach shows good to excellent agreement with gold standards for most gait parameters.

Keywords:
automated gait analysismarkerlesspose estimationtemporal gait parameters

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Area of Science:

  • Biomechanics
  • Human Movement Analysis
  • Rehabilitation Technology

Background:

  • Traditional gait analysis methods (visual observation, instrumented evaluation) have limitations including inconsistency, high cost, invasiveness, and specialized equipment requirements.
  • Markerless gait analysis using 2D pose estimation offers a potential alternative but often demands substantial computational resources and human input.
  • There is a need for accessible, automated, and reliable methods for temporal gait analysis in diverse settings.

Purpose of the Study:

  • To propose and validate an automated method for temporal gait analysis utilizing the low-computational-resource MediaPipe Pose model.
  • To assess the reliability and accuracy of the proposed method by comparing its outputs with a gold-standard Vicon motion capture system.
  • To establish the utility of this approach for community-based gait monitoring and intervention evaluation.

Main Methods:

  • An automated temporal gait analysis method was developed using the MediaPipe Pose estimation model.
  • The reliability of the proposed method was evaluated by comparing its results against data from the Vicon motion capture system.
  • Statistical analysis included calculating the Intraclass Correlation Coefficient (ICC) and mean absolute error for temporal gait parameters.

Main Results:

  • The MediaPipe Pose-based method demonstrated good to excellent agreement (ICC > 0.75 to > 0.90) for most temporal gait parameters.
  • Moderate agreement (ICC > 0.50) was observed for double support time (right) and swing time (right).
  • The method produced temporal gait parameters with low mean absolute error, indicating high accuracy.

Conclusions:

  • The automated MediaPipe Pose approach provides a reliable and accurate method for temporal gait analysis.
  • This technique offers a practical, low-resource solution for gait assessment, suitable for community-based applications.
  • The findings support its use in monitoring gait changes and evaluating the effectiveness of rehabilitation and training programs.