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Related Experiment Video

Updated: Jun 6, 2026

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand
06:44

Development of a Novel Task-oriented Rehabilitation Program using a Bimanual Exoskeleton Robotic Hand

Published on: May 20, 2020

An intention driven hand functions task training robotic system.

K Y Tong1, S K Ho, P K Pang

  • 1Department of Health Technology and informatics, The Hong Kong Polytechnic University, China, SAR. k.y.tong@polyu.edu.hk

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel robotic hand system for stroke rehabilitation that uses electromyography (EMG) signals to detect user intention. The system enables stroke survivors to control their impaired hand for daily living tasks.

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Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy

Published on: August 8, 2011

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Robotics
  • Neurorehabilitation

Background:

  • Stroke rehabilitation often requires intensive therapy to regain hand function.
  • Existing assistive devices may lack intuitive control or adaptability.
  • Restoring hand function is crucial for independence in daily living activities.

Purpose of the Study:

  • To develop and evaluate a novel robotic hand system for stroke rehabilitation.
  • To enable stroke survivors to control hand movements via electromyography (EMG) signal detection.
  • To facilitate task-specific training for improving hand function in hemiplegic patients.

Main Methods:

  • A robotic hand system with 5 individually actuated finger assemblies was designed.
  • Each finger assembly provides 2 degrees of freedom (DOFs) and adjustable length.
  • Electromyography (EMG) signals from the hemiplegic side detect hand opening/closing intentions.
  • Linear actuators power the finger movements, achieving specific ranges of motion (ROM) at MCP and PIP joints.

Main Results:

  • The system successfully detects user intention through EMG signals.
  • The robotic hand demonstrates controlled opening and closing movements.
  • The system's adjustable finger assemblies allow for personalized fitting.
  • Achieved ROM: 55 degrees at MCP joints and 65 degrees at PIP joints per finger.

Conclusions:

  • The developed robotic hand system offers a promising approach for stroke rehabilitation.
  • EMG-based intention detection provides intuitive control for hand function training.
  • This technology can aid stroke survivors in performing daily living tasks and improving motor recovery.