IoT-Enabled Prediction Model for Health Monitoring of College Students in Sports Using Big Data Analytics and Convolutional Neural Network

In recent years, the development of wearable devices and health applications has influenced the technical development of SHM in sports-related activities. These technologies can be invoked to improve the health management of college students who practice certain physical activities. This paper proposed and developed a novel IoT framework for sports health monitoring using prediction models based on big data analytics and convolutional neural networks (CNN). The proposed framework combines IoT technology with state-of-the-art deep learning techniques to analyze extensive data collected from wearable devices, optimizing sports performance and mitigating injury risks. The study outlines a complete methodology, including data collection from multiple sources, preprocessing for CNN models, and constructing and comparing CNN-based predictive models. Experimental results reveal the effectiveness of the proposed technique in predicting injuries and optimizing performance results. Ethical considerations, such as data privacy, model interpretability, and fairness, are also discussed to ensure responsible implementation. The findings highlight the potential of CNN and big data analytics in enhancing sports health management, offering personalized recommendations, and promoting overall well-being among college students. The experiment results outperformed the performance of the different evaluation metrics such as accuracy, sensitivity, specificity, F1 score, and MCC, with the proposed model achieving 0.9342%, 0.8500%, 0.9415%, 0.8803%, and 0.8232%, respectively. The error losses achieved less than those of the other methods, such as MSE, MASE, MAE, and RMSE, which achieved 0.0654%, 0.0758%, 0.2356%, and 0.2537%, respectively. Future research should focus on refining the models, expanding the dataset, and addressing ethical concerns to improve the framework’s applicability and effectiveness further.