ArticlePeerJ. Computer science2021
Solving musculoskeletal biomechanics with machine learning.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Technology-Driven Ergonomics: A Narrative Review of Innovative Strategies for Preventing Musculoskeletal Disorders in Modern Work Environments.Health science reports · 2026Article
- Autonomous Slip-Prevention Grip Force Control and Its Potential in Shared Control of Robotic Prosthetic Hands.IEEE transactions on medical robotics and bionics · 2026Article
- Biomechanical Assessment of Syndesmotic and Deltoid Ligament Strain in Pronation-External Rotation Type Ankle Injuries by Musculoskeletal Computer Simulation.Foot & ankle orthopaedics · 2026Article
- Predicting joint loading in Asian overweight and obese females with flexible flatfoot: a regression analysis of anthropometric parameters and gait dynamics.Medical & biological engineering & computing · 2025Article
- Neural Networks-Based Approach to Solve Inverse Kinematics Problems for Medical Applications.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Pole balancing on the fingertip: model-motivated machine learning forecasting of falls.Frontiers in physiology · 2024Article
- Modern three-dimensional digital methods for studying locomotor biomechanics in tetrapods.The Journal of experimental biology · 2023Review
- Does joint impedance improve dynamic leg simulations with explicit and implicit solvers?bioRxiv : the preprint server for biology · 2023Article
- Article
- Does joint impedance improve dynamic leg simulations with explicit and implicit solvers?PloS one · 2023Article
- Evaluation of feature projection techniques in object grasp classification using electromyogram signals from different limb positions.PeerJ. Computer science · 2022Article
- Machine learning-based classification of nonspecific neck pain using movement trajectories of scapular upward rotation.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.