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Related Experiment Video

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Setup for the Quantitative Assessment of Motion and Muscle Activity During a Virtual Modified Box and Block Test
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Published on: January 12, 2024

ML-based electromyography signal analysis for assessing rehabilitation exercise execution quality.

Finn Siegel1, Andreas Hein2, Matthias Maszuhn3

  • 1Department of Health Services Research, Assistance Systems and Medical Device Technology, Carl von Ossietzky Universität Oldenburg, Ammerländer Heerstr, 114- 118, 26129, Oldenburg, Germany. finn.siegel@uol.de.

Scientific Reports
|July 2, 2026
PubMed
Summary

This study introduces an electromyography (EMG) sensor system and machine learning to provide real-time feedback on orthopedic rehabilitation exercise form. The system accurately identifies optimal versus non-optimal exercise execution, aiding patient recovery.

Keywords:
ClassificationElectromyographyMachine learningNovelty detectionRehabilitation

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Science
  • Machine Learning in Healthcare

Background:

  • Optimal biomechanical form is critical for successful lower limb trauma recovery through orthopedic rehabilitation exercises.
  • Many patients struggle to perform rehabilitation exercises with correct form, potentially hindering recovery.
  • Existing methods for assessing exercise execution quality lack real-time, objective feedback.

Purpose of the Study:

  • To develop and evaluate an electromyography (EMG)-based sensor system integrated with machine learning for real-time assessment of orthopedic rehabilitation exercise quality.
  • To determine the accuracy of machine learning algorithms in classifying optimal and non-optimal exercise executions.
  • To explore the potential for personalized feedback to improve patient outcomes in rehabilitation.

Main Methods:

  • High-density EMG data (32 electrodes) were collected from vastus lateralis and medialis muscles during four lower limb exercises (squat, hip abduction, leg raises, rocking) performed in optimal and non-optimal variations.
  • A dataset of 3,040 exercise executions and 194,560 EMG recordings was generated from 19 participants.
  • Support Vector Machine (SVM) and One Class SVM algorithms were employed to analyze EMG patterns and classify execution quality.

Main Results:

  • A Support Vector Machine algorithm achieved an average accuracy of 83.3% (±8.8%) in classifying four distinct execution quality classes per exercise.
  • A One Class SVM, trained on optimal executions, could identify unknown exercises as optimal or non-optimal with 76.6% (±5.9%) accuracy.
  • The findings demonstrate the feasibility of using EMG and machine learning for objective assessment of rehabilitation exercise performance.

Conclusions:

  • The proposed EMG-based sensor system with machine learning shows significant potential for evaluating exercise execution quality in real-time during rehabilitation.
  • This technology can provide objective, personalized feedback to patients, potentially improving adherence and therapeutic outcomes.
  • Future development could lead to advanced systems for remote monitoring and tailored rehabilitation programs, enhancing recovery after lower limb trauma.