A Novel Muscle Synergy Extraction Method Used for Motor Function Evaluation of Stroke Patients: A Pilot Study

Area of Science:

• Biomedical Engineering
• Neuroscience
• Rehabilitation Science

Background:

• Nonnegative matrix factorization (NMF) is standard for muscle synergy extraction but suffers from reliability issues due to component sparseness.
• Limitations in NMF hinder the accurate assessment of muscle coordination and motor control.
• Evaluating motor function in stroke survivors requires reliable methods for analyzing muscle synergies.

Purpose of the Study:

• To introduce and validate a novel muscle synergy extraction method, multivariate curve resolution-alternating least squares (MCR-ALS).
• To overcome the limitations of NMF in extracting non-sparse muscle synergies.
• To assess the potential of MCR-ALS for evaluating motor function in stroke survivors.

Main Methods:

• Muscle synergy extraction using MCR-ALS, compared against NMF and self-modeling mixture analysis (SMMA).
• Simulated datasets analyzed to determine the impact of sparseness and noise on synergy extraction.
• Electromyography (EMG) data from healthy and stroke subjects analyzed for upper limb movements.

Main Results:

• MCR-ALS demonstrated superior accuracy in estimating true synergies compared to SMMA and NMF on simulated data.
• MCR-ALS exhibited significantly higher repeatability and intra-subject consistency than NMF in human subject data.
• Stroke survivors showed reduced intra-subject consistency and potentially more complex synergies than healthy individuals.

Conclusions:

• MCR-ALS offers a more reliable and robust method for muscle synergy extraction compared to NMF.
• The enhanced reliability of MCR-ALS is crucial for accurate motor function evaluation, particularly in clinical populations like stroke survivors.
• MCR-ALS shows promise as a valuable tool for assessing motor recovery and function in stroke rehabilitation.