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Evaluation of functional tests performance using a camera-based and machine learning approach.

Jindřich Adolf1,2, Yoram Segal3, Matyáš Turna4

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Summary

This study shows camera-based systems and machine learning can assess functional tests like the Single Leg Squat Test. This technology offers a simple, affordable method for evaluating exercise performance quality.

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Area of Science:

  • Biomechanics
  • Computer Science
  • Sports Medicine

Background:

  • Functional tests are crucial for assessing physical performance and injury risk.
  • Current assessment methods can be subjective and require specialized equipment.
  • Objective, automated assessment tools are needed to improve accuracy and accessibility.

Purpose of the Study:

  • To evaluate the efficacy of a camera-based system with machine learning for assessing functional tests.
  • To determine if OpenPose and standard cameras can quantify the quality of the Single Leg Squat Test and Step Down Test.
  • To compare machine learning predictions against expert physiotherapist assessments.

Main Methods:

  • Collected motion data from 46 healthy subjects performing functional tests using OpenPose and a standard camera.
  • Extracted movement parameters including joint angles and segment distances.
  • Utilized machine learning algorithms (AdaBoost classifier) to predict expert assessments based on extracted data.
  • Classified performance using 15 binary parameters aligned with physiotherapist evaluations.

Main Results:

  • The AdaBoost classifier achieved an accuracy of 0.7, with a specificity of 0.8 and sensitivity of 0.68.
  • Machine learning models successfully predicted physiotherapists' expert assessments of functional test quality.
  • Quantitative analysis of movement ranges provided objective performance metrics.

Conclusions:

  • A camera-based system combined with machine learning presents a viable, cost-effective tool for functional test assessment.
  • This approach can provide objective and reliable evaluations of exercise performance quality.
  • Future applications may include remote patient monitoring and personalized rehabilitation programs.