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Evaluation of a Portable EMG-Controlled Hand Exoskeleton With Independent Digit Actuation

delete2026-07-20
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OA
AI
M
Mohammad Ghassemi
C
Christopher M. Vogel
A
Alexander H. Sprague
D
Derek G. Kamper
DOI:10.1109/tnsre.2026.3710674delete
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Abstract

Abstract

En 中文
Background: Despite therapeutic efforts, the majority of individuals post-stroke will experience chronic motor deficits, particularly in the hand. Assistive devices such as hand exoskeletons have the potential to improve hand function, but these instruments must provide sufficient flexibility to support a variety of tasks. This study tests the robustness and utility of a novel device, the Bidirectionally Actuated Cable (BAC)-Glove, which provides independent actuation of each digit. The BAC-Glove supports a variety of control modes, including direct user control through electromyography (EMG). Methods: Device capabilities in terms of digit movement and fingertip force production were assessed in 5 neurotypical participants. Kinematics were captured with motion tracking and kinetics were captured with a load cell. Another 10 neurotypical participants completed four training sessions with the BAC-Glove to examine the viability of EMG control. Evaluations were performed at the end of the first and last sessions to examine user performance with the device. Results: The device produced coordinated flexion and extension motions of all three finger joints and peak fingertip forces of 15.1 N in flexion and 9.6 N in extension. Participants improved EMG control of the BAC-Glove over the training sessions, as evidenced by significant improvement on measures of object manipulation with the device, such as the Box and Block test (p < 0.05). Conclusions: BAC-Glove is a robust and potentially useful device for assisting manipulation of objects to perform activities of daily living. The proposed EMG control scheme showed sufficient viability to warrant future examination in clinical populations.
Keywords:
Rehabilitation robotics
exoskeletons
assistive technology
robot control

Journal

IEEE Transactions on Neural Systems and Rehabilitation Engineering cover
IEEE Transactions on Neural Systems and Rehabilitation Engineering
IF:
5.2
Papers:
448
Citations:
1.6W

Organization

U
University of North Carolina
Scholars:
4.2K
Papers: 1.9K
Citations: 337
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