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Article

A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes

1
Department of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, Spain
2
Embedded Systems and Artificial Intelligence Group, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, Spain
3
Triathlon Technification Program, Federación Triatlón Comunidad Valencian, 46940 Manises, Spain
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6388; https://doi.org/10.3390/s20216388
Received: 6 October 2020 / Revised: 27 October 2020 / Accepted: 6 November 2020 / Published: 9 November 2020
(This article belongs to the Special Issue Wearable Sensors & Gait)
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. View Full-Text
Keywords: running; kinematics; gait retraining running; kinematics; gait retraining
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MDPI and ACS Style

Martínez-Gramage, J.; Albiach, J.P.; Moltó, I.N.; Amer-Cuenca, J.J.; Huesa Moreno, V.; Segura-Ortí, E. A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes. Sensors 2020, 20, 6388. https://doi.org/10.3390/s20216388

AMA Style

Martínez-Gramage J, Albiach JP, Moltó IN, Amer-Cuenca JJ, Huesa Moreno V, Segura-Ortí E. A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes. Sensors. 2020; 20(21):6388. https://doi.org/10.3390/s20216388

Chicago/Turabian Style

Martínez-Gramage, Javier, Juan P. Albiach, Iván N. Moltó, Juan J. Amer-Cuenca, Vanessa Huesa Moreno, and Eva Segura-Ortí. 2020. "A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes" Sensors 20, no. 21: 6388. https://doi.org/10.3390/s20216388

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