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Assessing stroke-induced abnormal muscle coactivation in the upper limb using the surface EMG co-contraction index: A systematic review
Citation
Wang, Y., Zhong, L., Jin, M., Liao, D., Privitera, A. J., Wong, A. Y. L., Fong, G. C. H., Bao, S.-C., & Sun, R. (2025). Assessing stroke-induced abnormal muscle coactivation in the upper limb using the surface EMG co-contraction index: A systematic review. Journal of Electromyography and Kinesiology, 81, Article 102985. https://doi.org/10.1016/j.jelekin.2025.102985
Author
Wang, Yong
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Zhong, Lingling
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Jin, Minxia
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Liao, Dantong
•
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Wong, Arnold Y. L.
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Fong, Gabriel C. H.
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Bao, Shi-Chun
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Sun, Rui
Abstract
Electromyography (EMG) is increasingly used in stroke assessment research, with studies showing that EMG co-contraction (EMG-CC) of upper limb muscles can differentiate stroke patients from healthy individuals and correlates with clinical scales assessing motor function. This suggests that EMG-CC has potential for both assessing motor impairments and monitoring recovery in stroke patients. However, systematic reviews on EMG-CC’s effectiveness in stroke assessment are lacking. To address this, the present study aims to synthesize recent evidence on EMG-CC’s use in evaluating stroke-induced muscle abnormality. Eighteen studies including a total of 308 stroke patients and 155 healthy controls were included. Fifteen out of Eighteen included studies used the EMG-CC to successfully differentiate abnormal muscle co-contraction performance of the affected upper limb, even in comparison to the unaffected side in static tasks (isometric maximal voluntary contractions) and dynamic tasks (movement-oriented or goal-oriented). The EMG-CC shows promise as a convenient and effective tool for evaluating the extent of abnormal muscle coactivation in the upper limbs of post-stroke patients with spasticity as well as assessing the effectiveness of rehabilitation interventions. Further research is needed to validate these findings and establish standardized protocols for EMG-CC’s use in stroke assessment.
Date Issued
2025
Publisher
Elsevier
Journal
Journal of Electromyography and Kinesiology
Grant ID
2021YFF0501600
62101546
RCBS20210609104358078
Funding Agency
National Key R&D Program of China
National Natural Science Foundation of China
Shenzhen Science and Technology Program