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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| Vydáno v: | Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Ročník 2020; s. 740 - 743 |
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| Médium: | Konferenční příspěvek Journal Article |
| Jazyk: | angličtina |
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IEEE
01.07.2020
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| ISSN: | 2694-0604, 1558-4615, 2694-0604 |
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| Abstract | 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. |
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| AbstractList | 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.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. 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. |
| Author | Zhu, Xiangyang Chen, Chen Han, Dong Sheng, Xinjun Ma, Shihan Farina, Dario |
| Author_xml | – sequence: 1 givenname: Shihan surname: Ma fullname: Ma, Shihan organization: Shanghai Jiao Tong University,State Key Laboratory of Mechanical System and Vibration,Shanghai,China – sequence: 2 givenname: Chen surname: Chen fullname: Chen, Chen organization: Shanghai Jiao Tong University,State Key Laboratory of Mechanical System and Vibration,Shanghai,China – sequence: 3 givenname: Dong surname: Han fullname: Han, Dong organization: Fudan University,Department of Hand Surgery,Shanghai,China – sequence: 4 givenname: Xinjun surname: Sheng fullname: Sheng, Xinjun organization: Shanghai Jiao Tong University,State Key Laboratory of Mechanical System and Vibration,Shanghai,China – sequence: 5 givenname: Dario surname: Farina fullname: Farina, Dario organization: Imperial College London,Department of Bioengineering,London,UK – sequence: 6 givenname: Xiangyang surname: Zhu fullname: Zhu, Xiangyang organization: Shanghai Jiao Tong University,State Key Laboratory of Mechanical System and Vibration,Shanghai,China |
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| Title | Subject-Specific EMG Modeling with Multiple Muscles: A Preliminary Study |
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