{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-for-musculoskeletal-force","title":"Deep Learning for Musculoskeletal Force Prediction","arxiv_id":null,"date":"2018-12-13","proceeding":"Annals of Biomedical Engineering 2018 12","authors":["Lance Rane","Ziyun Ding","Alison H. McGregor","Anthony M. J. Bull"],"abstract":"Musculoskeletal models permit the determination of internal forces acting during dynamic movement, which is clinically useful, but traditional methods may suffer from slowness and a need for extensive input data. Recently, there has been interest in the use of supervised learning to build approximate models for computationally demanding processes, with benefits in speed and flexibility. Here, we use a deep neural network to learn the mapping from movement space to muscle space. Trained on a set of kinematic, kinetic and electromyographic measurements from 156 subjects during gait, the network’s predictions of internal force magnitudes show good concordance with those derived by musculoskeletal modelling. In a separate set of experiments, training on data from the most widely known benchmarks of modelling performance, the international Grand Challenge competitions, generates predictions that better those of the winning submissions in four of the six competitions. Computational speedup facilitates incorporation into a lab-based system permitting real-time estimation of forces, and interrogation of the trained neural networks provides novel insights into population-level relationships between kinematic and kinetic factors.","url_abs":"https://doi.org/10.1007/s10439-018-02190-0","url_pdf":"https://link.springer.com/content/pdf/10.1007%2Fs10439-018-02190-0.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"emg-signal-prediction","task_name":"EMG Signal Prediction"},{"task_slug":"electromyography-emg","task_name":"Electromyography (EMG)"},{"task_slug":"medial-knee-jrf-prediction","task_name":"Medial knee JRF Prediction"},{"task_slug":"muscle-force-prediction","task_name":"Muscle Force Prediction"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/emg-signal-prediction-on-grand-challenge","task":"EMG Signal Prediction","dataset":"Grand Challenge Competition - in Vivo Knee Loads","model":"ImpCollege","rank_in_archive_order":1,"of":1,"metrics":{" RMSE (SE, Gluteus Medius)":"0.22 ± 0.09"},"uses_additional_data":false},{"leaderboard":"/sota/medial-knee-jrf-prediction-on-grand-challenge","task":"Medial knee JRF Prediction","dataset":"Grand Challenge Competition - in Vivo Knee Loads","model":"IMP - Force plate and kinematic data","rank_in_archive_order":1,"of":3,"metrics":{" RMSE (Subject-exposed)":"186 ± 207","RMSE (Subject-naïve)":"216 ± 136"},"uses_additional_data":false},{"leaderboard":"/sota/medial-knee-jrf-prediction-on-grand-challenge","task":"Medial knee JRF Prediction","dataset":"Grand Challenge Competition - in Vivo Knee Loads","model":"IMP - Kinematic data only","rank_in_archive_order":2,"of":3,"metrics":{" RMSE (Subject-exposed)":"212 ± 213","RMSE (Subject-naïve)":"247 ± 119"},"uses_additional_data":false},{"leaderboard":"/sota/medial-knee-jrf-prediction-on-grand-challenge","task":"Medial knee JRF Prediction","dataset":"Grand Challenge Competition - in Vivo Knee Loads","model":"IMP - Force plate data only","rank_in_archive_order":3,"of":3,"metrics":{" RMSE (Subject-exposed)":"268 ± 206","RMSE (Subject-naïve)":"291 ± 132"},"uses_additional_data":false},{"leaderboard":"/sota/muscle-force-prediction-on-grand-challenge","task":"Muscle Force Prediction","dataset":"Grand Challenge Competition - in Vivo Knee Loads","model":"ImpCollege","rank_in_archive_order":1,"of":1,"metrics":{" RMSE (SE, Gluteus Maximus)":"91 ± 72"," RMSE (SE, Gluteus Medius)":"196 ± 186"," RMSE (SE, Hamstrings)":"140 ± 175"," RMSE (SE, Quadriceps)":"194 ± 140"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}