ArticleFrontiers in bioengineering and biotechnology2020
A Machine Learning and Wearable Sensor Based Approach to Estimate External Knee Flexion and Adduction Moments During Various Locomotion Tasks.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 4 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 4 syntheses or guidelines pooled it, 116 citations in OpenAlex.
- Just-in-Time Adaptive Interventions for Behavior Change in Physiological Health Outcomes and the Use Case for Knee Osteoarthritis: Systematic Review.Journal of medical Internet research · 2024Pooled 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
- Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review.Frontiers in neurorobotics · 2022Pooled it
- A Single-IMU Wearable System with 1D U-Net for Knee Adduction Moment Waveform Reconstruction During Gait.Sensors (Basel, Switzerland) · 2026Article
- Sagittal-Plane Knee Flexion Moment Estimation Using a Lightweight Deep Learning Framework Based on Sequential Surface EMG Feature Frames.Sensors (Basel, Switzerland) · 2026Article
- Review
- Is EMG Information Necessary for Deep Learning Estimation of Joint and Muscle Level States?IEEE transactions on bio-medical engineering · 2026Article
- Intelligent prediction of dynamic characteristics during exercise in patients with stroke.Journal of neuroengineering and rehabilitation · 2025Article
- Estimation of the External Knee Adduction Moment Using Inertial Measurement Unit Sensors on the Shank and Lower Back: A Pilot Study.Journal of functional morphology and kinesiology · 2025Article
- Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network.Arthroplasty today · 2025Article
- Functional Monitoring of Patients With Knee Osteoarthritis Based on Multidimensional Wearable Plantar Pressure Features: Cross-Sectional Study.JMIR aging · 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
- A Deep Learning Framework for End-to-End Control of Powered Prostheses.IEEE robotics and automation letters · 2024Article
- SensAA-Design and Verification of a Cloud-Based Wearable Biomechanical Data Acquisition System.Sensors (Basel, Switzerland) · 2024Article
- Predicting overstriding with wearable IMUs during treadmill and overground running.Scientific reports · 2024Article
- Leveraging Machine Learning for Personalized Wearable Biomedical Devices: A Review.Journal of personalized medicine · 2024Review
- 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
- Estimating 3D kinematics and kinetics from virtual inertial sensor data through musculoskeletal movement simulations.Frontiers in bioengineering and biotechnology · 2024Article
- A wearable gait lab powered by sensor-driven digital twins for quantitative biomechanical analysis post-stroke.Wearable technologies · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Joint moment measurements represent an objective biomechanical parameter of knee joint load in knee osteoarthritis (KOA). Wearable sensors in combination with machine learning techniques may provide solutions to develop assistive devices in KOA patients to improve disease treatment and to minimize risk of non-functional overreaching (e.g., pain). The purpose of this study was to develop an artificial neural network (ANN) that estimates external knee flexion moments (KFM) and external knee adduction moments (KAM) during various locomotion tasks, based on data obtained by two wearable sensors. Thirteen participants were instrumented with two inertial measurement units (IMUs) located on the right thigh and shank. Participants performed six different locomotion tasks consisting of linear motions and motions with a change of direction, while IMU signals as well as full body kinematics and ground reaction forces were synchronously recorded. KFM and KAM were determined using a full body biomechanical model. An ANN was trained to estimate the KFM and KAM time series using the IMU signals as input. Evaluation of the ANN was done using a leave-one-subject-out cross-validation. Concordance of the ANN-estimated KFM and reference data was categorized for five tasks (walking straight, 90° walking turn, moderate running, 90° running turn and 45° cutting maneuver) as strong (
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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.