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Sollerman Hand Function Sub-Test "Write with a Pen": A Computer-Vision-Based Approach in Rehabilitation Assessment.

Orestis N Zestas1, Nikolaos D Tselikas1

  • 1Communication Networks and Applications Laboratory (CNALab), Department of Informatics and Telecommunications, University of Peloponnese, 221 00 Tripoli, Greece.

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Summary

This study introduces a computer-vision tool to assess hand function after stroke using the Sollerman Hand Function Test (SHT). The accessible technology digitizes assessments, improving rehabilitation monitoring and treatment effectiveness evaluation.

Keywords:
computer visionfine motor skillsrehabilitation assessmentshape detectionsollerman hand function testupper-limb rehabilitation

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Area of Science:

  • Rehabilitation Medicine
  • Computer Vision
  • Biomedical Engineering

Background:

  • Impaired hand function is a common stroke consequence, necessitating objective motor recovery assessments.
  • The Sollerman Hand Function Test (SHT) evaluates daily activity capacity but requires in-person therapist administration.
  • Current limitations include time constraints and reliance on clinical settings for SHT administration.

Purpose of the Study:

  • To develop a computer-vision-based system for the "Write with a pen" sub-test of the SHT.
  • To create an accessible and cost-effective tool for assessing hand function recovery.
  • To provide an accurate hand spasticity evaluator integrated into the system.

Main Methods:

  • Utilized a single RGB camera for data acquisition, eliminating the need for specialized hardware.
  • Implemented the original SHT guidelines and scoring methods within the computer-vision application.
  • Developed algorithms to analyze hand movements and evaluate spasticity.

Main Results:

  • The proposed system accurately replicates the SHT's "Write with a pen" sub-test.
  • The application demonstrates feasibility on lower-end hardware, enhancing accessibility.
  • Preliminary findings from real-world usage show promising results for rehabilitation monitoring.

Conclusions:

  • A computer-vision approach offers a viable, accessible alternative for SHT assessment.
  • This technology can improve the efficiency and consistency of hand function evaluation post-stroke.
  • Further research and real-world application will refine the system for broader clinical adoption.