ArticleSensors (Basel, Switzerland)2020
Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players.
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 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Predictive accuracy of machine learning and markerless gait analysis for return-to-sport following lower extremity injury: a systematic review and meta-analysis.Frontiers in sports and active living · 2026Pooled it
- Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.BMC medical informatics and decision making · 2026Article
- Advances in Gait Alterations and Rehabilitation After Anterior Cruciate Ligament Reconstruction: Biomechanics and Emerging Technologies.Orthopaedic surgery · 2026Review
- Utilization of machine learning to identify lower extremity biomechanical predictors of rupture in a validated cadaveric model of ACL injury.Scientific reports · 2026Article
- Machine and deep learning models for ligament injury recognition: a systematic review and meta-analysis of imaging and novel diagnostic techniques.EFORT open reviews · 2026Article
- Prediction of on-field rugby scrummaging contact forces from videos using artificial neural networks.PloS one · 2026Article
- Enhancing game outcome prediction in the Chinese basketball league through a machine learning framework based on performance data.Scientific reports · 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
- Sensor-based technologies for motion analysis in sports injuries: a scoping review.BMC sports science, medicine & rehabilitation · 2025Article
- Predicting Severe Knee Arthritis Based on Two Inertial Measurement Unit Sensors as a Dynamic Coordinate System Using Classical Machine Learning.Journal of medical signals and sensors · 2025Article
- Article
- Treatment effect analysis of the Frailty Care Bundle (FCB) in a cohort of patients in acute care settings.Aging clinical and experimental research · 2024Article
- Non-contact lower limb injuries in Rugby Union: A two-year pattern recognition analysis of injury risk factors.PloS one · 2024Article
- Leveraging explainable machine learning to identify gait biomechanical parameters associated with anterior cruciate ligament injury.Scientific reports · 2022Article
- Analysing gait patterns in degenerative lumbar spine diseases: a literature review.Journal of spine surgery (Hong Kong) · 2022Review
- Unsupervised IMU-based evaluation of at-home exercise programmes: a feasibility study.BMC sports science, medicine & rehabilitation · 2022Article
- Comparison of Machine Learning Techniques for Mortality Prediction in a Prospective Cohort of Older Adults.International journal of environmental research and public health · 2021Article
- Common Biochemical and Magnetic Resonance Imaging Biomarkers of Early Knee Osteoarthritis and of Exercise/Training in Athletes: A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- Artificial Intelligence in the Management of Anterior Cruciate Ligament Injuries.Orthopaedic journal of sports medicine · 2021Review
- A Machine-Learning Approach to Measure the Anterior Cruciate Ligament Injury Risk in Female Basketball Players.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5-10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred.
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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.