Physics-informed Deep Learning for Musculoskeletal Modelling:Predicting Muscle Forces and Joint Kinematics from Surface EMG
| Autor: | Jie Zhang, Yihui Zhao, Fergus Shone, Zhenhong Li, Alejandro F. Frangi, Sheng Quan Xie, Zhi-Qiang Zhang |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Signal Processing (eess.SP)
Kinematics Musculoskeletal system Electromyography General Neuroscience Muscles Rehabilitation Biomedical Engineering deep neural network Deep learning muscle forces and joint kinematics prediction Musculoskeletal modelling Predictive models Internal Medicine FOS: Electrical engineering electronic engineering information engineering Convolutional neural networks Electrical Engineering and Systems Science - Signal Processing physics-based domain knowledge |
| Zdroj: | Zhang, J, Zhao, Y, Shone, F, Li, Z, Frangi, A F, Xie, S Q & Zhang, Z Q 2022, ' Physics-informed Deep Learning for Musculoskeletal Modelling : Predicting Muscle Forces and Joint Kinematics from Surface EMG ', IEEE Transactions on Neural Systems and Rehabilitation Engineering, pp. 1 . https://doi.org/10.1109/TNSRE.2022.3226860 |
| DOI: | 10.1109/TNSRE.2022.3226860 |
| Popis: | Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moments) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, data-driven methods have emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. Simultaneously, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework. |
| Databáze: | OpenAIRE |
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