Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm

As a fundamental sports discipline, track and field not In recent years, real-time monitoring and data analysis only forms the core of major events like the Olympics have become increasingly critical in enhancing athletic and World Championships but also plays a crucial role in performance. Studies have shown that by monitoring physiological promoting public health Jacobsson, Ekberg, Timpka, Haggren indicators (such as heart rate, body temperature, and Råsberg, Sjöberg, Mirkovic and Nilsson (2020); Timpka, blood oxygen saturation) and performance metrics (such as Dahlström, Fagher, Adami, Andersson, Jacobsson, Svedin speed, acceleration, and force) in real-time, it is possible to and Bermon (2022). The wide variety of track and field events, identify problems during training promptly and make targeted including sprints, middle and long-distance running, jumps, adjustments. For example, analyzing heart rate changes under and throws, demand high levels of physical fitness, technical different training intensities can assess endurance levels and skills, and mental strength from athletes Guo (2022); Zhang recovery status, while monitoring gait and acceleration during et al. (2023a). To excel in such competitive environments, running can optimize technical movements and improve athletes require not only innate talent and dedication but efficiency Rana and Mittal (2020a). Many studies have begun also scientific and systematic training methods Zhang et al. exploring the potential of using sensor technology and data (2023b); Yuan et al. (2024).