A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model
Motion predictions for limbs can be performed using commonly called Hill-based muscle models. For this type of models, a surface electromyogram (sEMG) of the muscle serves as an input signal for the activation of the muscle model. However, the Hill model needs additional information about the mechan...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1179224/full |
| _version_ | 1797785688308973568 |
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| author | Malte Mechtenberg Axel Schneider |
| author_facet | Malte Mechtenberg Axel Schneider |
| author_sort | Malte Mechtenberg |
| collection | DOAJ |
| description | Motion predictions for limbs can be performed using commonly called Hill-based muscle models. For this type of models, a surface electromyogram (sEMG) of the muscle serves as an input signal for the activation of the muscle model. However, the Hill model needs additional information about the mechanical system state of the muscle (current length, velocity, etc.) for a reliable prediction of the muscle force generation and, hence, the prediction of the joint motion. One feature that contains potential information about the state of the muscle is the position of the center of the innervation zone. This feature can be further extracted from the sEMG. To find the center, a wavelet-based algorithm is proposed that localizes motor unit potentials in the individual channels of a single-column sEMG array and then identifies innervation point candidates. In the final step, these innervation point candidates are clustered in a density-based manner. The center of the largest cluster is the estimated center of the innervation zone. The algorithm has been tested in a simulation. For this purpose, an sEMG simulator was developed and implemented that can compute large motor units (1,000's of muscle fibers) quickly (within seconds on a standard PC). |
| first_indexed | 2024-03-13T00:57:33Z |
| format | Article |
| id | doaj.art-e1539b195daa4925a3e21f6b99302a98 |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-03-13T00:57:33Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-e1539b195daa4925a3e21f6b99302a982023-07-06T16:55:03ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182023-07-011710.3389/fnbot.2023.11792241179224A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG modelMalte MechtenbergAxel SchneiderMotion predictions for limbs can be performed using commonly called Hill-based muscle models. For this type of models, a surface electromyogram (sEMG) of the muscle serves as an input signal for the activation of the muscle model. However, the Hill model needs additional information about the mechanical system state of the muscle (current length, velocity, etc.) for a reliable prediction of the muscle force generation and, hence, the prediction of the joint motion. One feature that contains potential information about the state of the muscle is the position of the center of the innervation zone. This feature can be further extracted from the sEMG. To find the center, a wavelet-based algorithm is proposed that localizes motor unit potentials in the individual channels of a single-column sEMG array and then identifies innervation point candidates. In the final step, these innervation point candidates are clustered in a density-based manner. The center of the largest cluster is the estimated center of the innervation zone. The algorithm has been tested in a simulation. For this purpose, an sEMG simulator was developed and implemented that can compute large motor units (1,000's of muscle fibers) quickly (within seconds on a standard PC).https://www.frontiersin.org/articles/10.3389/fnbot.2023.1179224/fullinnervation pointmotor endplatesEMG simulationconcentrated current sourcemotor unit (MU)conduction velocity (CV) |
| spellingShingle | Malte Mechtenberg Axel Schneider A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model Frontiers in Neurorobotics innervation point motor endplate sEMG simulation concentrated current source motor unit (MU) conduction velocity (CV) |
| title | A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model |
| title_full | A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model |
| title_fullStr | A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model |
| title_full_unstemmed | A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model |
| title_short | A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model |
| title_sort | method for the estimation of a motor unit innervation zone center position evaluated with a computational semg model |
| topic | innervation point motor endplate sEMG simulation concentrated current source motor unit (MU) conduction velocity (CV) |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1179224/full |
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