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Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
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Smartphone-Based Markerless Motion Capture for Accessible Rehabilitation: A Computer Vision Study.

Bruno Cunha1,2, José Maçães3, Ivone Amorim2

  • 1CINTESIS@RISE, CINTESIS.UPT, Department of Science and Technology, Portucalense University, Rua Dr. António Bernardino de Almeida 541, 4200-072 Porto, Portugal.

Sensors (Basel, Switzerland)
|September 13, 2025
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Summary

This study introduces a smartphone app using computer vision for physical rehabilitation exercises. It offers accessible, independent feedback to improve patient recovery and adherence outside of clinical settings.

Keywords:
accessibilityartificial intelligencecomputer visionmachine learningrehabilitation

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Science
  • Computer Vision

Background:

  • Physical rehabilitation is vital for recovery but often limited by accessibility and adherence issues due to reliance on in-person professional feedback.
  • Home-based exercises lack consistent guidance, leading to decreased motivation and effectiveness.
  • Existing computer vision systems for exercise assessment have limitations in accuracy and accessibility.

Purpose of the Study:

  • To propose and evaluate a novel smartphone-based system for independent physical rehabilitation exercise feedback.
  • To enhance patient adherence and recovery outcomes through accessible, AI-driven support.
  • To reduce the reliance on in-person supervision and specialized equipment in rehabilitation.

Main Methods:

  • Development of an intelligent system utilizing computer vision for motion tracking and analysis of rehabilitation exercises via smartphone videos.
  • Human pose detection and movement quality assessment framework.
  • Evaluation of the system against the Qualysis Motion Capture System using expert-labeled data.

Main Results:

  • The proposed system demonstrates feasibility as a proof-of-concept, with a pilot study involving 15 participants.
  • The framework focuses on accurate human pose detection and movement quality assessment.
  • The system aims to minimize human error and improve the accessibility of rehabilitation.

Conclusions:

  • Smartphone-based computer vision offers a viable solution for providing independent exercise feedback in physical rehabilitation.
  • This technology has the potential to significantly improve rehabilitation accessibility, reduce recovery times, and enhance patient outcomes.
  • Further validation with larger datasets is warranted to scale this innovative approach.