A Radial Basis Network-based Early Warning Algorithm for Physical Injuries in Marathon Athletes

Abstract

For marathon runners, a single injury may affect their lifelong athletic career, so their injury management is very important. The current injury management for marathon runners has a certain lag, and the current injury warning is mainly based on manual teams, which is costly and poorly automated. To solve these problems, the study proposes a marathon athlete physical injury warning algorithm based on inertia weight adjustment optimized radial basis network. Particle swarm optimization technology has also been incorporated into early warning algorithms. Finally, an athlete injury and disease early warning model is constructed based on the algorithm. The results of performance tests show that the algorithm has a minimum fitness function value of 0.13, which is significantly lower than the current algorithm used for comparison. In the test with real data, the MAPE of the proposed algorithm was as low as 7.598% and the agreement of the hazard score results with the expert human assessment reached 100%. The results of the study indicate the practicality of the algorithm to assist work teams and perform early warning of physical injuries in athletes. However, the high number of iterations required is a limitation awaiting resolution.