ArticleAnnals of biomedical engineering2019
Deep Learning for Musculoskeletal Force Prediction.
Article in Annals of biomedical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed.
- From Joint Loading to Osteoarthritis: A Multiscale Review of Knee Mechanobiology and Digital Modelling.International journal of molecular sciences · 2026Review
- ML-based electromyography signal analysis for assessing rehabilitation exercise execution quality.Scientific reports · 2026Article
- Article
- From Simplified Markers to Muscle Function: A Deep Learning Approach for Personalized Cervical Biomechanics Assessment Powered by Massive Musculoskeletal Simulation.Sensors (Basel, Switzerland) · 2026Article
- Article
- Neural networks estimate muscle force in dynamic conditions better than Hill-type muscle models.The Journal of experimental biology · 2025Article
- Performance of deep-learning models incorporating knee alignment information for predicting ground reaction force during walking.Biomedical engineering online · 2025Article
- A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton Assistance.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Predicting Knee Joint Contact Force Peaks During Gait Using a Video Camera or Wearable Sensors.Annals of biomedical engineering · 2024Article
- Explainable AI Elucidates Musculoskeletal Biomechanics: A Case Study Using Wrist Surgeries.Annals of biomedical engineering · 2024Article
- Finding the Goldilocks Zone of Mechanical Loading: A Comprehensive Review of Mechanical Loading in the Prevention and Treatment of Knee Osteoarthritis.Bioengineering (Basel, Switzerland) · 2024Review
- Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks.Annals of biomedical engineering · 2023Article
- Method for Classifying Schizophrenia Patients Based on Machine Learning.Journal of clinical medicine · 2023Article
- Musculotendon Parameters in Lower Limb Models: Simplifications, Uncertainties, and Muscle Force Estimation Sensitivity.Annals of biomedical engineering · 2023Review
- Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data.Bioengineering (Basel, Switzerland) · 2023Article
- Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks.Frontiers in bioengineering and biotechnology · 2023Article
- A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs.Frontiers in physiology · 2023Article
- Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning.Frontiers in bioengineering and biotechnology · 2023Article
- Detection of COVID-19 from CT and Chest X-ray Images Using Deep Learning Models.Annals of biomedical engineering · 2022Review
- [Evaluation of brain injury caused by stick type blunt instruments based on convolutional neural network and finite element method].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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.