Article ; Online: Solving musculoskeletal biomechanics with machine learning.
PeerJ. Computer science
2021 Volume 7, Page(s) e663
Abstract: Deep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be ... ...
| Abstract | Deep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be approximated by relatively simple functions, which opens the opportunity for machine learning (ML) applications. In this study, we challenged general ML algorithms with the problem of approximating the posture-dependent moment arm and muscle length relationships of the human arm and hand muscles. We used two types of algorithms, light gradient boosting machine (LGB) and fully connected artificial neural network (ANN) solving the wrapping kinematics of 33 muscles spanning up to six degrees of freedom (DOF) each for the arm and hand model with 18 DOFs. The input-output training and testing datasets, where joint angles were the input and the muscle length and moment arms were the output, were generated by our previous phenomenological model based on the autogenerated polynomial structures. Both models achieved a similar level of errors: ANN model errors were 0.08 ± 0.05% for muscle lengths and 0.53 ± 0.29% for moment arms, and LGB model made similar errors-0.18 ± 0.06% and 0.13 ± 0.07%, respectively. LGB model reached the training goal with only 10 |
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| Language | English |
| Publishing date | 2021-08-26 |
| Publishing country | United States |
| Document type | Journal Article |
| ISSN | 2376-5992 |
| ISSN (online) | 2376-5992 |
| DOI | 10.7717/peerj-cs.663 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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