A novel deep learning method for predicting athletes’ health using wearable sensors and recurrent neural networks

Abstract

Good health is extremely important for athletes who engage in strenuous physical activities, such as football. They must develop a healthy body before participating in vigorous activities and competitions. Although researchers have presented a wide range of analytical approaches emphasizing athlete health, only a small percentage of completed studies have used neural networks. In this study, we propose a novel technique for predicting football players’ health using wearable technology and recurrent neural networks. The proposed system monitors the players’ health in real-time, making it one of the first applications of wearable sensors for athletes’ conditioning and health. Health prediction results are provided after the time-step data is entered into a recurrent neural network, and subsequent deep features are obtained from that data. Several trials are conducted in this investigation, and the outcomes are determined by the information acquired about the players’ health. The simulation results illustrate the practicality and dependability of the proposed approach. The algorithms developed in this study can serve as the foundation for data-driven monitoring and training.