An IoT-based Smart Healthcare integrated solution for Basketball using Q-Learning Algorithm

Abstract

Internet of Things (IoT) technology has been adopted football Practice industry, where athletes train and upgrade their health status. Internet-connected machinery has the potential to gather huge amounts of data in real-time personal characteristics of an individual athlete; his or her, motion, health, and other parameters and conditions of the surrounding environment. This information, which is not obvious in the traditional training techniques can be very valuable in the individualization of training processes. In basketball, where skillful maneuvers, accuracy, speed as well as planned movements are important IoT technology can be of great importance. The paper outlines a method in which basketball players are furnished with IoT gadgets that may monitor activities such as pulse rate, oxygen level, and movements. It is essential to note that these devices participate in data transmission to a central system where a Q-learning algorithm takes place. The algorithm’s decision-making principles are the reward functions that are prescribed to suit the most preferable behaviors: performance parameters (e.g., shooting accuracy, speed, etc.) and physiology parameters (e.g., heart rate variability, recovery rates, etc.). It is paramount that such training alterations are not only performance-oriented but also health-centered, hence maintaining a two-pronged focus on overall player growth. The outcomes demonstrate the contrast between regular mode training sessions and IoT/Q-learning enhanced training sessions and figure out the enhancement of 15% via shooting precision within six weeks. It establishes a link between adapting training sessions to the health of the players involved and the execution of the skills incorporating enhanced agility of participants by 20 percent. The ideas for the adaptive system entail immediate feedback and modification procedures, which may afford enhanced training results.