Revolutionizing Athletic Training with Machine Learning: Injury Prediction Using Predictive Analytics and Customized Workouts Through Personalization Algorithms

Abstract

This research offers the solution to the existing problem by implementing the idea of a unique athletic training based on predictive analytics and personalization algorithms. In particular, we suggest the use of the MLP-LSTM model to forecast athletic injuries and prescribe individualized workout schedules relevant to the specific athlete. Multilayer perceptron and long short-term memory network are used together for designing MLP-LSTM architecture that has the capacity to grasp temporal patterns present in athlete information and predict injuries. It will employ training data, athletes’ profile, and data on their past and present injuries and using statistical and mathematical models to read into it and look for signs that may be precursors to possible injuries. Accordingly, individual training schedules are suggested with the intent to minimize the chances for sustaining an injury while at the same time maximize performance. The creation of this system appears to be fundamentally proactive with regards to athletic training because it puts it in the hands of the coaches and athletes involved to allow prevention of injuries when they are most likely to occur. Based on the experimental results, it can be concluded that MLP-LSTM outperforms most of the traditional machine learning classifiers for the injury prediction. Moreover, the workout routines created by the system have proved to enhance the athletes’ performance and prevent injuries. This research can be useful in changing the existing mode of training of athletes in order to improve on their health and performance in sports.

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