ArticleSensors (Basel, Switzerland)2021
A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it, 91 citations in OpenAlex.
- 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
- IMU- and Vision-Based Measurement Techniques for Joint Kinematics: A Narrative Review.Sensors (Basel, Switzerland) · 2026Review
- Biomechanics-informed inertial tracking achieves the accuracy of marker-based kinematics.Nature communications · 2026Article
- The Use of a Smartphone to Assess the Two-Minute Step Test: Validity of Machine Learning Compared to Analytical Data Processing.Sensors (Basel, Switzerland) · 2026Article
- Machine Learning-Based Estimation of Knee Joint Mechanics from Kinematic and Neuromuscular Inputs: A Proof-of-Concept Using the CAMS-Knee Datasets.Bioengineering (Basel, Switzerland) · 2026Article
- Transfer Learning With Simulated and Recorded Data Improves Predictions of Lateral Pinch Thumb-Tip Forces.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Is EMG Information Necessary for Deep Learning Estimation of Joint and Muscle Level States?IEEE transactions on bio-medical engineering · 2026Article
- Bridging the Methodological Gap Between Inertial Sensors and Optical Motion Capture: Deep Learning as the Path to Accurate Joint Kinematic Modelling Using Inertial Sensors.Sensors (Basel, Switzerland) · 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
- Gait Speed and Task Specificity in Predicting Lower-Limb Kinematics: A Deep Learning Approach Using Inertial Sensors.Mayo Clinic proceedings. Digital health · 2025Article
- Comparing sparse inertial sensor setups for sagittal-plane walking and running reconstructions.Frontiers in bioengineering and biotechnology · 2025Article
- Article
- Assessing Locomotive Syndrome Through Instrumented Five-Time Sit-to-Stand Test and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Article
- A Machine Learning Approach for Predicting Pedaling Force Profile in Cycling.Sensors (Basel, Switzerland) · 2024Article
- Improving Biological Joint Moment Estimation During Real-World Tasks With EMG and Instrumented Insoles.IEEE transactions on bio-medical engineering · 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
- Walking and scuba diving assisted amphibious exoskeleton robots: the designing of power assist control and myoelectricity based wearers' fatigue evaluation.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of artificial intelligence techniques to wearable sensor data may facilitate accurate analysis outside of controlled laboratory settings-the holy grail for gait clinicians and sports scientists looking to bridge the lab to field divide. Using these techniques, parameters that are difficult to directly measure in-the-wild, may be predicted using surrogate lower resolution inputs. One example is the prediction of joint kinematics and kinetics based on inputs from inertial measurement unit (IMU) sensors. Despite increased research, there is a paucity of information examining the most suitable artificial neural network (ANN) for predicting gait kinematics and kinetics from IMUs. This paper compares the performance of three commonly employed ANNs used to predict gait kinematics and kinetics: multilayer perceptron (MLP); long short-term memory (LSTM); and convolutional neural networks (CNN). Overall high correlations between ground truth and predicted kinematic and kinetic data were found across all investigated ANNs. However, the optimal ANN should be based on the prediction task and the intended use-case application. For the prediction of joint angles, CNNs appear favourable, however these ANNs do not show an advantage over an MLP network for the prediction of joint moments. If real-time joint angle and joint moment prediction is desirable an LSTM network should be utilised.
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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.