ArticleMedical & biological engineering & computing2020
Prediction of lower limb joint angles and moments during gait using artificial neural networks.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Minimal Sensor Configuration for Human Activity Recognition in Patients With Hip Osteoarthritis: Proof-of-Concept Study.JMIR biomedical engineering · 2026Article
- Attention-Based Multimodal Framework for Athlete-Performance Analysis and Rehabilitation Monitoring Using Vision and Wearable Sensors.Bioengineering (Basel, Switzerland) · 2026Article
- Long short-term attention memory (LSTAM): a global-feature-integrated model for joint moment prediction in human rehabilitation.Scientific reports · 2026Article
- EMG-Based Simulation for Optimization of Human-in-the-Loop Control in Simple Robotic Walking Assistance.Journal of sensor and actuator networks · 2025Article
- Performance of deep-learning models incorporating knee alignment information for predicting ground reaction force during walking.Biomedical engineering online · 2025Article
- Validity of a Single Inertial Measurement Unit to Measure Hip Range of Motion During Gait in Patients Undergoing Total Hip Arthroplasty.Sensors (Basel, Switzerland) · 2025Article
- Meta-Transfer-Learning-Based Multimodal Human Pose Estimation for Lower Limbs.Sensors (Basel, Switzerland) · 2025Article
- Article
- Data augmentation of time-series data in human movement biomechanics: A scoping review.PloS one · 2025Article
- Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions.Frontiers in bioengineering and biotechnology · 2025Article
- Predicting noncontact injuries of professional football players using machine learning.PloS one · 2025Article
- Estimation of Lower Limb Joint Angles Using sEMG Signals and RGB-D Camera.Bioengineering (Basel, Switzerland) · 2024Article
- Multibody Model with Foot-Deformation Approach for Estimating Ground Reaction Forces and Moments and Joint Torques during Level Walking through Optical Motion Capture without Optimization Techniques.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
- Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model.Sensors (Basel, Switzerland) · 2023Article
- BioMAT: An Open-Source Biomechanics Multi-Activity Transformer for Joint Kinematic Predictions Using Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data.Bioengineering (Basel, Switzerland) · 2023Article
- A comparison of machine learning models' accuracy in predicting lower-limb joints' kinematics, kinetics, and muscle forces from wearable sensors.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, gait analysis outside the laboratory attracts more and more attention in clinical applications as well as in life sciences. Wearable sensors such as inertial sensors show high potential in these applications. Unfortunately, they can only measure kinematic motions patterns indirectly and the outcome is currently jeopardized by measurement discrepancies compared with the gold standard of optical motion tracking. The aim of this study was to overcome the limitation of measurement discrepancies and the missing information on kinetic motion parameters using a machine learning application based on artificial neural networks. For this purpose, inertial sensor data-linear acceleration and angular rate-was simulated from a database of optical motion tracking data and used as input for a feedforward and long short-term memory neural network to predict the joint angles and moments of the lower limbs during gait. Both networks achieved mean correlation coefficients higher than 0.80 in the minor motion planes, and correlation coefficients higher than 0.98 in the sagittal plane. These results encourage further applications of artificial intelligence to support gait analysis. Graphical Abstract The graphical abstract displays the processing of the data: IMU data is used as input to a feedforward and a long short-term memory neural network to predict the joint kinematics and kinetics of the lower limbs during gait.
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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.