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Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review.

Sara Sardari1, Sara Sharifzadeh2, Alireza Daneshkhah3

  • 1Centre for Computational Science & Mathematical Modelling, Coventry University, Coventry, UK; School of Information Technology, Faculty of Science Engineering and Built Environment, Deakin University, Geelong, Vic, Australia.

Computers in Biology and Medicine
|April 5, 2023
PubMed
Summary

Home-based physical rehabilitation is enhanced by AI-powered systems that analyze skeleton data from vision sensors. These tools help patients monitor exercise performance and improve outcomes in physical therapy.

Keywords:
Activity evaluationActivity recognitionComputer visionDeep learningPhysical rehabilitationSkeleton data

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Area of Science:

  • Rehabilitation Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Home-based rehabilitation programs are crucial for individuals with physical disabilities to regain strength and balance.
  • Patients often lack expert supervision, hindering accurate self-assessment of exercise performance.
  • Advancements in vision-based sensors and AI offer potential for automated monitoring solutions.

Approach:

  • This paper reviews literature on skeleton data acquisition for physiotherapy exercise monitoring.
  • It examines Artificial Intelligence (AI) methodologies for analyzing skeleton data, including feature learning.
  • The review covers evaluation metrics and feedback generation for rehabilitation monitoring.

Key Points:

  • Vision-based sensors capture accurate skeleton data, enabling detailed activity monitoring.
  • Computer Vision (CV) and Deep Learning (DL) advancements facilitate automatic patient monitoring.
  • AI-based analysis of skeleton data is key for feature learning, evaluation, and feedback in rehabilitation.

Conclusions:

  • The study provides a comprehensive review of AI-driven approaches for physiotherapy exercise monitoring.
  • It highlights challenges in skeleton data acquisition, analysis, and feedback generation.
  • Future research directions are proposed to advance AI applications in home-based rehabilitation.