Subject-Specific EMG Modeling with Multiple Muscles: A Preliminary Study

Modeling of surface electromyographic (EMG) signal has been proven valuable for signal interpretation and algorithm validation. However, most EMG models are currently limited to single muscle, either with numerical or analytical approaches. Here, we present a preliminary study of a subject-specific...

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Bibliographic Details
Published in:	Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Vol. 2020; pp. 740 - 743
Main Authors:	Ma, Shihan, Chen, Chen, Han, Dong, Sheng, Xinjun, Farina, Dario, Zhu, Xiangyang
Format:	Conference Proceeding Journal Article
Language:	English
Published:	IEEE 01.07.2020
ISSN:	2694-0604, 1558-4615, 2694-0604
Online Access:	Get full text
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Summary:	Modeling of surface electromyographic (EMG) signal has been proven valuable for signal interpretation and algorithm validation. However, most EMG models are currently limited to single muscle, either with numerical or analytical approaches. Here, we present a preliminary study of a subject-specific EMG model with multiple muscles. Magnetic resonance (MR) technique is used to acquire accurate cross section of the upper limb and contours of five muscle heads (biceps brachii, brachialis, lateral head, medial head, and long head of triceps brachii). The MR image is adjusted to an idealized cylindrical volume conductor model by image registration. High-density surface EMG signals are generated for two movements - elbow flexion and elbow extension. The simulated and experimental potentials were compared using activation maps. Similar activation zones were observed for each movement. These preliminary results indicate the feasibility of the multi-muscle model to generate EMG signals for complex movements, thus providing reliable data for algorithm validation.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2694-0604 1558-4615 2694-0604
DOI:	10.1109/EMBC44109.2020.9175286