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Towards Automating Personal Exercise Assessment and Guidance with Affordable Mobile Technology.

Maria Sideridou1, Evangelia Kouidi2, Vassilia Hatzitaki2

  • 1Lab of Computing, Medical Informatics, and Biomedical-Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.

Sensors (Basel, Switzerland)
|April 13, 2024
PubMed
Summary

This study introduces a home-based monitored physical activity system using pose estimation for real-time exercise guidance. It helps beginners and remote patients improve exercise form and prevent injuries.

Keywords:
BlazePosecomputer visionelderlykinematicsmachine learningpose estimation modelsreal-time biomechanical feedback-(BMF)signal processingvirtual coach

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Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Sports Science

Background:

  • Physical activity (PA) is crucial for health, but beginners face challenges like discouragement and injury.
  • Limited access to supervised exercise, especially during pandemics, highlights the need for remote, personalized systems.
  • Existing systems often lack real-time, individualized feedback for home-based exercise.

Purpose of the Study:

  • To develop and evaluate a monitored physical exercise system for real-time guidance and recommendations.
  • To assist users, particularly beginners and the elderly, in performing exercises correctly at home.
  • To create an interactive tool supporting remote rehabilitation programs.

Main Methods:

  • Utilized BlazePose for posture estimation via computer or smartphone cameras to recognize body movement.
  • Employed machine learning classifiers and signal processing for exercise identification, achieving high accuracy.
  • Conducted kinematic analysis and statistical studies on range of motion (ROM) to detect execution deviations.

Main Results:

  • Machine learning classifiers achieved test-set accuracy between 94.76% and 100% for exercise recognition.
  • Kinematic analysis identified deviations from expected exercise execution, enabling targeted guidance.
  • Data collected from 57 volunteers provided a comprehensive understanding of exercise performance.

Conclusions:

  • The developed system offers real-time, personalized exercise guidance for home environments.
  • Leveraging BlazePose and machine learning, the system can effectively monitor exercise performance.
  • This interactive tool has the potential to significantly support remote rehabilitation and physical activity programs.