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Study on Auxiliary Rehabilitation System of Hand Function Based on Machine Learning with Visual Sensors.

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This study developed an AI-powered system for stroke patient hand function recovery. The system uses deep learning and augmented reality for accurate assessment and engaging rehabilitation exercises at home.

Keywords:
deep learninggamified designgesture recognitionstroke rehabilitation

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Area of Science:

  • Rehabilitation Engineering
  • Artificial Intelligence in Healthcare
  • Neurorehabilitation

Background:

  • Stroke survivors often face challenges in hand function recovery during mid-to-late Brunnstrom stages.
  • Active patient participation is crucial for effective rehabilitation but can be difficult to maintain.
  • Existing assessment methods may lack precision or engaging elements for home-based therapy.

Purpose of the Study:

  • To assess hand function recovery in stroke patients using advanced technology.
  • To develop an automated system for rehabilitation exercises encouraging active participation.
  • To create a feasible and economical solution for home and community-based stroke rehabilitation.

Main Methods:

  • Utilized a deep residual network (ResNet) with Focal Loss for gesture recognition (91.0% Macro F1 score).
  • Employed Leap Motion 2 for precise hand tracking and established skeletal joint point mapping.
  • Developed an augmented reality (AR) system via Unity with C# for real-time motion range quantification.

Main Results:

  • Achieved high accuracy in gesture recognition (90.9% validation accuracy).
  • Demonstrated technical feasibility and accuracy of the automated assessment and rehabilitation system.
  • Collected a large static assessment gesture dataset (502,401 frames) based on the FMA scale.

Conclusions:

  • The developed AI and AR system is technically feasible and accurate for stroke patient hand rehabilitation.
  • The system has the potential to enhance motivation, interactivity, and self-efficacy in stroke survivors.
  • This integrated framework provides a foundation for future clinical applications in neurorehabilitation.