Semprini, M, Cuppone, AV, Delis, I et al. (3 more authors) (2017) Biofeedback Signals for Robotic Rehabilitation: Assessment of Wrist Muscle Activation Patterns in Healthy Humans. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (7). pp. 883-892. ISSN 1534-4320
Abstract
Electrophysiological recordings from human muscles can serve as control signals for robotic rehabilitation devices. Given that many diseases affecting the human sensorimotor system are associated with abnormal patterns of muscle activation, such biofeedback can optimize human-robot interaction and ultimately enhance motor recovery. To understand how mechanical constraints and forces imposed by a robot affect muscle synergies, we mapped the muscle activity of seven major arm muscles in healthy individuals performing goal-directed discrete wrist movements constrained by a wrist robot. We tested six movement directions and four force conditions typically experienced during robotic rehabilitation. We analyzed electromyographic (EMG) signals using a space-by-time decomposition and we identified a set of spatial and temporal modules that compactly described the EMG activity and were robust across subjects. For each trial, coefficients expressing the strength of each combination of modules and representing the underlying muscle recruitment, allowed for a highly reliable decoding of all experimental conditions. The decomposition provides compact representations of the observable muscle activation constrained by a robotic device. Results indicate that a low-dimensional control scheme incorporating EMG biofeedback could be an effective add-on for robotic rehabilitative protocols seeking to improve impaired motor function in humans.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. This is an author produced version of a paper published in IEEE Transactions on Neural Systems and Rehabilitation Engineering . Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Biofeedback, electromyography, muscle synergies, robotic rehabilitation. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biomedical Sciences (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 21 Mar 2019 11:37 |
Last Modified: | 25 Jun 2023 21:45 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TNSRE.2016.2636122 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143950 |