Deep Q Learning-Enabled Training and Health Monitoring of Basketball Players Using IoT Integrated Multidisciplinary Techniques

The advancement of AI is opening gateways for sports analytics and sports healthcare. This paper investigates the use of Reinforcement learning alongside IoT devices to establish optimum policy in coaching. The optimum policy will cover three aspects of the agent, (1) the attacking position (2) the defensive position (3) the health of the agent both in defensive and attacking mode. This paper also investigates the training strategies of basketball to enhance player movement and health life. A DQN approach along with an IoT health sensor is used in simulation settings. The sensors in the simulation were attached to an agent to record the data of the agent related to health. The simulation analyzes the movement of players according to the conditions, the trajectory of the ball, and the health condition of the players. Based on this condition the player creates defensive and attacking strategies by shifting positions. The received data is passed through a neural network architecture to maximize the performance of the player and increase the play life and performance of the player. Different parameters of Deep Q-learning such as reward shaping Learning rate and loss function of the model. This multidisciplinary approach focuses on automated decision-making processes and flexible methodologies tailored to dynamic game situations, to connect concepts from healthcare analytics to sports training. The study proposes new methods for assessing player performance, anticipating game outcomes, and developing effective training regimens based on ideas from IoT-enabled healthcare, such as real-time monitoring and predictive analytics. Our model simulation integrated with deep learning demonstrates substantial improvement in playing court. The model predicts 95% accuracy predicting accurate moves both in attacking and defensive positions. The risk of injury is reduced by up to 60% and the overall performance and efficiency of the player was 98% in gameplay.