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<p>Effective rehabilitation for patients with upper-limb paralysis requires adaptive and systematic training strategies that go beyond simple muscle strengthening to promote precise motor recovery and neural reorganization. Existing smart glove-based systems can measure finger and wrist movements but remain limited in facilitating coordinated upper-limb motion and maintaining user engagement. To overcome these limitations, this study developed an adaptive rehabilitation system integrating a smart glove and a collaborative robot within a closed-loop control framework. The system enables real-time interaction between human motion sensing and torque-based robotic feedback to deliver personalized assistance through adaptive impedance modulation based on user performance. The proposed system was evaluated through simulation-based and system-level experiments using quantitative performance metrics, including movement error, task success rate, robot intervention rate, response time, and movement variability. Results showed substantial improvements in overall rehabilitation performance: The movement error decreased from 18.5 ± 3.2% to 4.2 ± 1.7%, corresponding to an average positional deviation reduction from about 8&#xa0;mm to approximately 5&#xa0;mm. The robot intervention rate declined from 92.7 ± 3.5% to 18.2 ± 4.3%, and the task success rate increased from 65.2 ± 4.8% to 91.6 ± 2.8%. Response time and movement variability were also significantly reduced, indicating faster feedback adaptation and improved motion stability. These findings confirm that the adaptive feedback mechanism enhances motion precision, promotes user autonomy, and supports consistent upper-limb coordination during training. This study demonstrates that integrating multi-sensor data and torque-based feedback control provides a robust foundation for personalized and efficient rehabilitation. The proposed adaptive glove–robot framework offers practical potential for clinical applications and contributes to the advancement of intelligent, patient-centered rehabilitation systems.</p>

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Design and Performance Evaluation of a Smart Glove Integrated with a Collaborative Robot for Upper-Limb Rehabilitation

  • 期刊论文

  • 发表日期：

    2026年7月23日

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  • Youngkuk Kwon

摘要

Effective rehabilitation for patients with upper-limb paralysis requires adaptive and systematic training strategies that go beyond simple muscle strengthening to promote precise motor recovery and neural reorganization. Existing smart glove-based systems can measure finger and wrist movements but remain limited in facilitating coordinated upper-limb motion and maintaining user engagement. To overcome these limitations, this study developed an adaptive rehabilitation system integrating a smart glove and a collaborative robot within a closed-loop control framework. The system enables real-time interaction between human motion sensing and torque-based robotic feedback to deliver personalized assistance through adaptive impedance modulation based on user performance. The proposed system was evaluated through simulation-based and system-level experiments using quantitative performance metrics, including movement error, task success rate, robot intervention rate, response time, and movement variability. Results showed substantial improvements in overall rehabilitation performance: The movement error decreased from 18.5 ± 3.2% to 4.2 ± 1.7%, corresponding to an average positional deviation reduction from about 8 mm to approximately 5 mm. The robot intervention rate declined from 92.7 ± 3.5% to 18.2 ± 4.3%, and the task success rate increased from 65.2 ± 4.8% to 91.6 ± 2.8%. Response time and movement variability were also significantly reduced, indicating faster feedback adaptation and improved motion stability. These findings confirm that the adaptive feedback mechanism enhances motion precision, promotes user autonomy, and supports consistent upper-limb coordination during training. This study demonstrates that integrating multi-sensor data and torque-based feedback control provides a robust foundation for personalized and efficient rehabilitation. The proposed adaptive glove–robot framework offers practical potential for clinical applications and contributes to the advancement of intelligent, patient-centered rehabilitation systems.