ArticleSensors (Basel, Switzerland)2020
A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes.
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 11 papers.
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Who cites it
11 citing papers in PubMed, 20 citations in OpenAlex.
- 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
- Attention-Enhanced Convolutional BiLSTM Model for Predicting Recovery Outcomes in Sports Injuries.Indian journal of orthopaedics · 2026Article
- Machine learning applications in sport: a scoping review.Frontiers in psychology · 2026Review
- The running gait analysis technology: A comprehensive systematic literature review.Journal of orthopaedics · 2025Review
- Comparative analysis of spatiotemporal gait parameters in patients with distal femoral megaprosthesis and healthy subjects using an inertial measurement unit (IMU).Wearable technologies · 2025Article
- Evaluating functional outcomes and quality of life in musculoskeletal tumor patients with distal femoral megaprostheses: a case-control study.World journal of surgical oncology · 2024Article
- Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models.Sensors (Basel, Switzerland) · 2024Article
- A Machine Learning Approach to Finding the Fastest Race Course for Professional Athletes Competing in IronmanInternational journal of environmental research and public health · 2023Article
- The Use of Wearable Sensors for Preventing, Assessing, and Informing Recovery from Sport-Related Musculoskeletal Injuries: A Systematic Scoping Review.Sensors (Basel, Switzerland) · 2022Article
- Machine-learned-based prediction of lower extremity overuse injuries using pressure plates.Frontiers in bioengineering and biotechnology · 2022Article
- The Relationship between VOSensors (Basel, Switzerland) · 2021Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe running segment of a triathlon produces 70% of the lower limb injuries. Previous research has shown a clear association between kinematic patterns and specific injuries during running.
methodsAfter completing a seven-month gait retraining program, a questionnaire was used to assess 19 triathletes for the incidence of injuries. They were also biomechanically analyzed at the beginning and end of the program while running at a speed of 90% of their maximum aerobic speed (MAS) using surface sensor dynamic electromyography and kinematic analysis. We used classification tree (random forest) techniques from the field of artificial intelligence to identify linear and non-linear relationships between different biomechanical patterns and injuries to identify which styles best prevent injuries.
resultsFewer injuries occurred after completing the program, with athletes showing less pelvic fall and greater activation in gluteus medius during the first phase of the float phase, with increased trunk extension, knee flexion, and decreased ankle dorsiflexion during the initial contact with the ground.
conclusionsThe triathletes who had suffered the most injuries ran with increased pelvic drop and less activation in gluteus medius during the first phase of the float phase. Contralateral pelvic drop seems to be an important variable in the incidence of injuries in young triathletes.
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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.