A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes
Background: The 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. Methods: After completing a seven-month gait retraining program, a questionnaire was used to assess...
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doaj-a723f30e1f7c43c09bca8711d1786d3a2020-11-25T04:01:06ZengMDPI AGSensors1424-82202020-11-01206388638810.3390/s20216388A Random Forest Machine Learning Framework to Reduce Running Injuries in Young TriathletesJavier Martínez-Gramage0Juan Pardo Albiach1Iván Nacher Moltó2Juan José Amer-Cuenca3Vanessa Huesa Moreno4Eva Segura-Ortí5Department of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, SpainEmbedded Systems and Artificial Intelligence Group, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, SpainDepartment of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, SpainDepartment of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, SpainTriathlon Technification Program, Federación Triatlón Comunidad Valencian, 46940 Manises, SpainDepartment of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, SpainBackground: The 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. Methods: After 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. Results: Fewer 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. Conclusions: The 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.https://www.mdpi.com/1424-8220/20/21/6388runningkinematicsgait retraining |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Javier Martínez-Gramage Juan Pardo Albiach Iván Nacher Moltó Juan José Amer-Cuenca Vanessa Huesa Moreno Eva Segura-Ortí |
spellingShingle |
Javier Martínez-Gramage Juan Pardo Albiach Iván Nacher Moltó Juan José Amer-Cuenca Vanessa Huesa Moreno Eva Segura-Ortí A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes Sensors running kinematics gait retraining |
author_facet |
Javier Martínez-Gramage Juan Pardo Albiach Iván Nacher Moltó Juan José Amer-Cuenca Vanessa Huesa Moreno Eva Segura-Ortí |
author_sort |
Javier Martínez-Gramage |
title |
A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes |
title_short |
A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes |
title_full |
A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes |
title_fullStr |
A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes |
title_full_unstemmed |
A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes |
title_sort |
random forest machine learning framework to reduce running injuries in young triathletes |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-11-01 |
description |
Background: The 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. Methods: After 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. Results: Fewer 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. Conclusions: The 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. |
topic |
running kinematics gait retraining |
url |
https://www.mdpi.com/1424-8220/20/21/6388 |
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