ArticleScientific reports2023
A comparison of machine learning models' accuracy in predicting lower-limb joints' kinematics, kinetics, and muscle forces from wearable sensors.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 73 citations in OpenAlex.
- An Anatomy-Informed Cross-Attention Framework for sEMG-Driven Knee and Ankle Moment Prediction During Sit-to-Walk Transitions.Bioengineering (Basel, Switzerland) · 2026Article
- Accurate estimation of hip range of motion using MediaPipe and inertial sensors with machine learning models.BMC musculoskeletal disorders · 2026Article
- [Progress and application of machine learning in sports injury research].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- Machine learning prediction of ACL loading during the wide lunge: a multifactorial coupling analysis based on kinematic and electromyographic signals.Frontiers in bioengineering and biotechnology · 2026Article
- Musculoskeletal Inverse Kinematics Tool for Inertial Motion Capture Data Based on the Adaptive Unscented Kalman Smoother: An Implementation for OpenSim.Annals of biomedical engineering · 2025Article
- A Preliminary Data-Driven Approach for Classifying Knee Instability During Subject-Specific Exercise-Based Game with Squat Motions.Sensors (Basel, Switzerland) · 2025Article
- Interpretable prediction of knee joint loading during tennis serves based on GNN-GRU model and layer-wise relevance propagation.Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine · 2025Article
- Computational Analysis of Neuromuscular Adaptations to Strength and Plyometric Training: An Integrated Modeling Study.Sports (Basel, Switzerland) · 2025Article
- Integrating deep learning in stride-to-stride muscle activity estimation of young and old adults with wearable inertial measurement units.Scientific reports · 2025Article
- Pain Assessment in Osteoarthritis: Present Practices and Future Prospects Including the Use of Biomarkers and Wearable Technologies, and AI-Driven Personalized Medicine.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Review
- Challenges in Combining EMG, Joint Moments, and GRF from Marker-Less Video-Based Motion Capture Systems.Bioengineering (Basel, Switzerland) · 2025Review
- Learning based lower limb joint kinematic estimation using open source IMU data.Scientific reports · 2025Article
- Balance and fall-risk assessment in older adults using wearable plantar pressure and semi-supervised learning.Frontiers in bioengineering and biotechnology · 2025Article
- Using Vital Signs for the Early Prediction of Necrotizing Enterocolitis in Preterm Neonates with Machine Learning.Children (Basel, Switzerland) · 2024Article
- A Machine Learning Approach for Predicting Pedaling Force Profile in Cycling.Sensors (Basel, Switzerland) · 2024Article
- A Surface Electromyography (sEMG) System Applied for Grip Force Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Achieving Precision Healthcare through Nanomedicine and Enhanced Model Systems.ACS materials Au · 2024Review
- On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods.Scientific reports · 2024Article
- IMU-Based Energy Expenditure Estimation for Various Walking Conditions Using a Hybrid CNN-LSTM Model.Sensors (Basel, Switzerland) · 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
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
A combination of wearable sensors' data and Machine Learning (ML) techniques has been used in many studies to predict specific joint angles and moments. The aim of this study was to compare the performance of four different non-linear regression ML models to estimate lower-limb joints' kinematics, kinetics, and muscle forces using Inertial Measurement Units (IMUs) and electromyographys' (EMGs) data. Seventeen healthy volunteers (9F, 28 ± 5 years) were asked to walk over-ground for a minimum of 16 trials. For each trial, marker trajectories and three force-plates data were recorded to calculate pelvis, hip, knee, and ankle kinematics and kinetics, and muscle forces (the targets), as well as 7 IMUs and 16 EMGs. The features from sensors' data were extracted using the Tsfresh python package and fed into 4 ML models; Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine, and Multivariate Adaptive Regression Spline for targets' prediction. The RF and CNN models outperformed the other ML models by providing lower prediction errors in all intended targets with a lower computational cost. This study suggested that a combination of wearable sensors' data with an RF or a CNN model is a promising tool to overcome the limitations of traditional optical motion capture for 3D gait analysis.
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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.