Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913599228739584 |
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| author | Ma, Shuhao Zhang, Jie Shi, Chaoyang Di, Pei Robertson, Ian D. Zhang, Zhi-Qiang |
| author_facet | Ma, Shuhao Zhang, Jie Shi, Chaoyang Di, Pei Robertson, Ian D. Zhang, Zhi-Qiang |
| contents | Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to their fast execution speed, but label information is still required during training, which is not easy to acquire in practice. To tackle these issues, this paper presents a novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_04213 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals Ma, Shuhao Zhang, Jie Shi, Chaoyang Di, Pei Robertson, Ian D. Zhang, Zhi-Qiang Machine Learning Human-Computer Interaction Signal Processing Biological Physics Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to their fast execution speed, but label information is still required during training, which is not easy to acquire in practice. To tackle these issues, this paper presents a novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method. |
| title | Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals |
| topic | Machine Learning Human-Computer Interaction Signal Processing Biological Physics |
| url | https://arxiv.org/abs/2412.04213 |