Rehabilitation training of hamstring injury in athletes training hamstrings based on BP neural network algorithm
Abstract
Athletes are prone to injury during daily training and competition. In order to achieve better results, they are subjected to heavy training every day, who challenge the limits of their bodies. Excessive exertion, inattention, and irregular movements may all lead to muscle strains in athletes. The hamstrings, consisting of the biceps, semitendinosus, and semimembranosus, are susceptible to injury. Traditional research on hamstring injury rehabilitation training focuses on the prevention of muscle strains and the restoration of muscle elasticity. However, traditional training methods are often unable to make targeted adjustments to each athlete’s specific situation. The actual application effect is not good. In order to improve the effectiveness of rehabilitation training for hamstring injury, this paper has introduced the BP neural network algorithm model. Based on the BP (Back Propagation) algorithm model, this paper has conducted an in-depth analysis of the causes of muscle strain in athletes. The results showed that the average accuracy of the algorithm was 97.83%, which had a high accuracy for the analysis of the cause. Muscle strain rehabilitation training methods were further analyzed. Research showed that the BP neural network algorithm could optimize up to 31%, and the effectiveness was above 96%. In the comparison of these two methods, it can be clearly seen that the algorithm in this paper is more scientific and efficient, which is conducive to better and faster recovery of the injured hamstrings of athletes.
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