Quantifying Muscle Co-Activation for Impaired Finger Independence in Stroke Survivors

Yuwen Ruan, Henry Shin, Xiaogang Hu

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: Hand impairment frequently occurs in individuals following a stroke. There is evidence of abnormal muscle co-activation that contributes to impaired control of finger independence. This study quantitatively analyzed hand muscle co-activation patterns of chronic stroke survivors. Systematically quantifying the degree of muscle co-activation patterns in stroke survivors can help us to better understand the mechanisms behind compromised finger independence and enables a more accurate assessment of hand impairment. Methods: We analyzed muscle co-activation patterns both macroscopically and microscopically using high-density surface electromyographic (HD-sEMG) signals and decomposed motor unit signals from extrinsic and intrinsic flexor/extensor muscles. The muscle co-activation patterns between both sides of stroke survivors and neurologically intact controls were compared. Results: We observed increased levels of co-activation in the affected sides of stroke survivors compared with their contralateral sides and the control groups, with a higher degree in the extrinsic muscles than the intrinsic muscles. The asymmetry in muscle co-activation between hands correlated with impaired finger force independence and clinical assessment scales. In the micro-level analysis of motor unit action potentials (MUAPs) distributions, we observed a notable increase in action potential spread of MUAPs in the individual affected extrinsic muscles, but the altered MUAP distribution did not correlate with clinical assessment scales. Conclusion: We systematically quantified abnormal muscle co-activation patterns in impaired finger independence after stroke. Significance: With further development, the outcomes provide a comprehensive understanding of hand dexterity deficits in stroke survivors, which may provide guidance for targeted rehabilitation strategies and offer a potential for automated impairment evaluations.

Original languageEnglish (US)
Pages (from-to)3293-3301
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume71
Issue number11
DOIs
StatePublished - 2024

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

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