Performance Optimization Mechanism of Adolescent Physical Training Based on Reinforcement Learning and Markov Model

Abstract

Upon the teenagers' failure to obtain the plenty of physical exercises at the growth and development stage, the related central nervous system is prone to degeneration and the physical fitness starts to decline gradually. In fact, through monitoring the exercise process real-timely and quantifying the exercise data, the adolescent physical training can be effectively conducted. For such process, it involves two issues, i.e., the real-time data monitoring and data quantification evaluation. Therefore, this paper proposes a novel method based on Reinforcement Learning (RL) and Markov model to monitor and evaluate the physical training effect. Meanwhile, the RL is used to optimize the adaptive bit rate of surveillance video and help the real-time data monitoring; the Markov model is employed to evaluate the health condition on the physical training. Finally, we develop a real-time monitoring system on exercise data and compare with the state-of-the-art mechanisms based on this system platform. The experimental results indicate that the proposed performance optimization mechanism can be more efficient to conduct the physical training. Particularly the average evaluation deviation rate based on Markov model is controlled within 0.16%.