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A Random Forest Machine Learning Framework to Reduce Running Injuries in Young Triathletes.
Javier Martínez-Gramage1, Juan Pardo Albiach2, Iván Nacher Moltó1
1Department of Physiotherapy, Universidad Cardenal Herrera-CEU, CEU Universities, 46115 Valencia, Spain.
Sensors (Basel, Switzerland)
|November 13, 2020
Summary
Triathletes experienced fewer lower limb injuries after gait retraining, which improved running biomechanics. Key changes included reduced pelvic drop and enhanced gluteus medius activation, crucial for injury prevention.
Area of Science:
- Sports Medicine
- Biomechanics
- Exercise Physiology
Background:
- Running in triathlons causes a high incidence of lower limb injuries.
- Running kinematics are demonstrably linked to specific injury types.
Purpose of the Study:
- To investigate the effect of a gait retraining program on injury incidence in triathletes.
- To identify biomechanical patterns associated with reduced injury risk during running.
Main Methods:
- A seven-month gait retraining program was implemented for 19 triathletes.
- Biomechanical analysis using surface sensor dynamic electromyography and kinematic analysis was performed.
- Artificial intelligence (random forest) was used to correlate biomechanical patterns with injury incidence.
Main Results:
- Post-retraining, triathletes reported fewer injuries.
- Observed improvements included decreased pelvic drop and increased gluteus medius activation.
- Enhanced trunk extension, knee flexion, and reduced ankle dorsiflexion upon ground contact were noted.
Conclusions:
- High injury rates in triathletes correlate with increased pelvic drop and reduced gluteus medius activation.
- Contralateral pelvic drop is a significant factor in triathlete lower limb injuries.
