Design and implementation of an intelligent sports management system (ISMS) using wireless sensor networks

In recent years, growth in technology has significantly impacted various industries, including sports, health, e-commerce, and agriculture. Among these industries, the sports sector is experiencing significant transformation, which needs support in accurately monitoring athlete predicting and performance injuries arising due to traditional methods' limitations. Keeping the above in mind, in this article, we present the Intelligent Sports Management System (ISMS) with the integration of wireless sensor networks (WSNs) and neural networks (NNs), which enhance athlete monitoring and injury prediction. Our proposed ISMS consists of several layers: user interface, business logic layer, data management layer, integration layer, analytics and AI layer, IoT layer, and security layer. To facilitate interactions for athletes, coaches, and administrators, our planned ISMS integrates a user-friendly interface accessible through web and mobile applications. Besides, scheduling and event management are managed by the business logic layer. Similarly, the data management layer can process and store comprehensive data from various sources. To ensure smooth data exchange, the integration layer connects the ISMS with third-party services, and the analytics and AI layer leverages machine learning to provide actionable insights on performance and outcomes. In addition, the IoT layer collects real-time data from sensors and wearable devices, which is essential for performance analysis and injury prevention. Finally, the security layer ensures data integrity and confidentiality with robust encryption and access controls. To evaluate the system performance in different scenarios, we performed many experiments, which show that the proposed ISMS model shows the system efficacy in improving accuracy (0.94), specificity (0.97), recall (0.91), precision (0.93), F1 score (0.95), mean absolute error (MAE) (0.6), mean square error (MSE) (0.8), and root mean square error (RMSE) (0.9), compared to traditional methods. From these results, it is clear that our suggested approach improves athlete performance monitoring, injury prevention plans, and training schedules by presenting a complete and novel solution for recent sports management.