ArticleSensors (Basel, Switzerland)2020
Real-Time Prediction of Joint Forces by Motion Capture and Machine Learning.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 62 citations in OpenAlex.
- Real-Time Prediction of Lower-Limb Joint Kinematics, Kinetics, and Ground Reaction Force Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2026Article
- From Joint Loading to Osteoarthritis: A Multiscale Review of Knee Mechanobiology and Digital Modelling.International journal of molecular sciences · 2026Review
- Comparison of deep and conventional machine learning methods in predicting joint moments in patients with cerebral palsy.Medical & biological engineering & computing · 2026Article
- Machine learning-based estimation of structure-specific load around the ankle and knee joint during running using IMU data.Frontiers in bioengineering and biotechnology · 2026Article
- Continuous Lower Limb Biomechanics Prediction via Prior-Informed Lightweight Marker-GMformer.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Key biomechanical features of jump-landing under cognitive dual-task conditions: an XGBoost-SHAP-based explainable machine learning analysis.Frontiers in bioengineering and biotechnology · 2026Article
- Generating synthetic images of human skeletal motion for pose and kinematics estimation tasks.Scientific data · 2025Article
- Entropy, Irreversibility, and Time-Series Deep Learning of Kinematic and Kinetic Data for Gait Classification in Children with Cerebral Palsy, Idiopathic Toe Walking, and Hereditary Spastic Paraplegia.Sensors (Basel, Switzerland) · 2025Article
- Prediction of Vertical Ground Reaction Forces Under Different Running Speeds: Integration of Wearable IMU with CNN-xLSTM.Sensors (Basel, Switzerland) · 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
- Prediction of joint moments from kinematics using machine learning in children with congenital talipes equino varus and typically developing peers.Journal of orthopaedics · 2024Article
- Application of Machine Learning Methods to Investigate Joint Load in Agility on the Football Field: Creating the Model, Part I.Sensors (Basel, Switzerland) · 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
- Towards a comprehensive biomechanical assessment of the elderly combiningFrontiers in bioengineering and biotechnology · 2024Article
- 3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units.Frontiers in bioengineering and biotechnology · 2024Article
- Robot-Aided Motion Analysis in Neurorehabilitation: Benefits and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks.Annals of biomedical engineering · 2023Article
- Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics.PLoS computational biology · 2023Article
- Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Conventional biomechanical modelling approaches involve the solution of large systems of equations that encode the complex mathematical representation of human motion and skeletal structure. To improve stability and computational speed, being a common bottleneck in current approaches, we apply machine learning to train surrogate models and to predict in near real-time, previously calculated medial and lateral knee contact forces (KCFs) of 54 young and elderly participants during treadmill walking in a speed range of 3 to 7 km/h. Predictions are obtained by fusing optical motion capture and musculoskeletal modeling-derived kinematic and force variables, into regression models using artificial neural networks (ANNs) and support vector regression (SVR). Training schemes included either data from all subjects (
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What OpenQuestion holds
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.