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

Journal: Sensors (Basel, Switzerland)
PMID: 33182357

Abstract

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.

Authors

  • Javier Martínez-Gramage
    Department of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, Spain.
  • Juan Pardo Albiach
    Embedded Systems and Artificial Intelligence Group, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, Spain.
  • Iván Nacher Moltó
    Department of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, Spain.
  • Juan José Amer-Cuenca
    Department of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, Spain.
  • Vanessa Huesa Moreno
    Triathlon Technification Program, Federación Triatlón Comunidad Valencian, 46940 Manises, Spain.
  • Eva Segura-Ortí
    Department of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, Spain.

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