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The influence of muscle shapes on HDsEMG decomposition yield and accuracy
Citation Link: https://doi.org/10.15480/882.16638
Publikationstyp
Journal Article
Date Issued
2025-12-18
Sprache
English
TORE-DOI
Volume
86
Article Number
103103
Citation
Journal of Electromyography and Kinesiology 86: 103103 (2026)
Publisher DOI
Scopus ID
Publisher
Elsevier
The application of high-density surface electromyography in conjunction with decomposition algorithms has enabled decoding of motor unit (MU) firings. In developing such algorithms, model-based simulation is an established means to assess yield and accuracy. In this work, we therefore investigate how the simulated muscle shape impacts the decomposition performance. To this end, an arm model with an anatomical biceps brachii shape was developed, and its plausibility in representing MU action potential waveforms was verified with decomposition of experimental, submaximal ramp contractions. From this anatomical muscle shape model, two simplified shapes were derived. In a simulation study, comparable fiber pathways and MU properties were used in anatomical and simplified muscle shape models, and a blind source separation-based decomposition was performed. The results were evaluated with respect to the number of identified MUs and their rate of agreement (RoA). Across all models, MUs with the highest energy contribution were identified. For the two simplified shapes, statistically significantly more MUs (p <0.05) were decomposed due to changed electrode-fiber distances, while the RoA remained consistently high above 94.63±12.16% indicating reliable MU property extraction with all three models. The results emphasize the benefit of anatomically accurate muscle shape models since small simplifications can affect decomposition yield.
Subjects
Biceps brachii
High-density surface electromyography
Muscle modeling
Numerical computer simulation
Surface electromyography decomposition
DDC Class
616: Diseases
Funding(s)
Individualisierte Medizintechnik für bildgestützte, robotische Interventionen
More Funding Information
This work was funded by Land Schleswig-Holstein, Germany, Project: “Individualisierte Medizintechnik für bildgestützte, robotische Interventionen (IMTE2)”, Project No.: LPW21-L/2.2/262, 125 24 009.
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