Muscle force prediction method considering the role of antagonistic muscle based on a coupled spring model
The prediction of muscle forces is very important in order to evaluate the physical loads during human motion. Although a number of musculoskeletal models, which can predict muscle forces during motion, have been proposed, these models use the optimization method, in which the sum of the predicted m...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
The Japan Society of Mechanical Engineers
2019
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/ec3f9f287dbd49cbb0d80fc4e215de5e |
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| Sumario: | The prediction of muscle forces is very important in order to evaluate the physical loads during human motion. Although a number of musculoskeletal models, which can predict muscle forces during motion, have been proposed, these models use the optimization method, in which the sum of the predicted muscle forces is minimized. However, unfortunately, the optimization method does not usually consider the role of antagonistic muscles, which act in a direction opposite to the prime motion or for restriction of rotational joint motion. Therefore, the present study proposes a new method to predict muscle forces considering the role of the antagonistic muscle during human motion. The present study proposes a new model, in which the agonist muscle is assumed to be connected to the antagonistic muscle by a coupled spring. Joint torque is defined as the summation of both the torques derived from the agonist muscles and the torques derived from the antagonistic muscles. Each muscle force can be predicted in order to maintain balance among the torques generated by the agonist muscles and the antagonistic muscles, respectively. Experiments were conducted in order to validate the proposed method of predicting muscle forces. Surface electromyograms (sEMGs) were measured for comparison with the predicted muscle forces. The experimental results showed that the predicted muscle forces agreed well with the sEMGs of muscles. |
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