Publication

Abstract : Athletes who push their bodies to the limit need to be in tip-top shape to compete. Before engaging in strenuous activity or competition, they should focus on building a healthy body. The ubiquitous availability of smartphones, recent advancements in computational, and artificial intelligence (AI) technologies, and the rising trends in multimedia and edge computation have all contributed to the emergence of new models and paradigms for wearable devices. Researchers have provided a diverse array of analytical methodologies centering on athlete health; however, neural networks have been applied in just a fraction of the completed investigations. Using recurrent neural networks and wearable technology, we offer a new method for forecasting the health of football players. One of the earliest uses of wearable sensors for athletes’ training and health, the suggested system keeps tabs on the players’ well-being in real time. After feeding the time-step data into a recurrent neural network (RNN) and extracting deep features from it, a set of health prediction results is returned. This study involves a number of experiments, the results of which are dependent on the players’ health data. The proposed method is shown to be practical and reliable through simulation results. The study's algorithms can form the basis of data-driven monitoring and instruction. The chapter finishes with a discussion of potential research approaches and future directions for the smart wearables sector.