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Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise.

Andrés Aguirre1, Maria J Pinto1, Carlos A Cifuentes1

  • 1Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá 111166, Colombia.

Sensors (Basel, Switzerland)
|August 10, 2021
PubMed
Summary

This study developed a computational model to estimate fatigue during sit-to-stand exercises in physical rehabilitation. The model accurately identifies low, moderate, and high fatigue levels using kinematic and heart rate data.

Keywords:
Kinectfatigue estimationmachine learningphysical exercisephysical rehabilitationsit-to-stand

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Area of Science:

  • Rehabilitation Medicine
  • Biomedical Engineering
  • Sports Science

Background:

  • Physical exercise (PE) is crucial for rehabilitation, with high-intensity exercises (HIEs) showing superior health benefits.
  • Monitoring patient fatigue during HIEs is vital to prevent complications, yet objective and practical estimation methods are lacking.
  • The sit-to-stand (STS) exercise is widely used in physical rehabilitation.

Purpose of the Study:

  • To propose a computational model for objective fatigue estimation during the sit-to-stand exercise.
  • To develop and evaluate this model using data from healthy volunteers performing STS exercises.

Main Methods:

  • A dataset was collected from 60 healthy volunteers performing STS exercises.
  • A computational model was developed using 32 kinematic features and heart rate data from ambulatory sensors (Kinect and Zephyr).
  • A random forest model with 60 sub-classifiers was employed to classify three fatigue conditions: low, moderate, and high.

Main Results:

  • The proposed model achieved an accuracy of 82.5% in classifying fatigue levels.
  • Upper body movement was identified as the most significant feature for fatigue estimation.
  • Lower body movements and heart rate also provided valuable information for fatigue assessment.

Conclusions:

  • The developed computational model offers a promising, objective tool for monitoring fatigue during physical rehabilitation, particularly for HIEs like STS.
  • The findings highlight the importance of kinematic analysis, especially upper body movement, in fatigue assessment.
  • This tool can aid clinicians in optimizing rehabilitation programs and preventing overexertion.