Summary of Physics-informed Deep Learning For Muscle Force Prediction with Unlabeled Semg Signals, by Shuhao Ma et al.
Physics-informed Deep Learning for Muscle Force Prediction with Unlabeled sEMG Signals
by Shuhao Ma, Jie Zhang, Chaoyang Shi, Pei Di, Ian D.Robertson, Zhi-Qiang Zhang
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC); Signal Processing (eess.SP); Biological Physics (physics.bio-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel approach presents a physics-informed deep learning method that predicts muscle forces without requiring label information during training. By embedding the Hill muscle model-based forward dynamics into a deep neural network, this method not only estimates muscle forces but also identifies personalized muscle-tendon parameters. The proposed method outperforms baseline methods in terms of root mean square error (RMSE) and coefficient of determination on wrist joint data from six healthy subjects. This physics-informed approach has the potential to revolutionize the field of biomechanical analysis, enabling faster and more accurate predictions of human movements and physical functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper presents a new way to predict how muscles work without needing special labels during training. It combines ideas from physics and deep learning to get better results. The method is tested on data from six healthy people’s wrists and shows promise in predicting muscle forces and identifying personalized muscle-tendon parameters. This could help us understand and improve human movement and physical function. |
Keywords
» Artificial intelligence » Deep learning » Embedding » Neural network
