Real-time health warning model for college students’ sports supported by wearable devices

In college sports, health monitoring and real-time early warning are crucial for the prevention of sports injuries and sudden illnesses, but traditional monitoring means are limited by real-time and multi-dimensional analysis capabilities. To this end, this study designs a real-time health warning model based on wearable devices, which realizes dynamic parsing of physiological timing signals and multimodal data synergistic sensing to accurately identify sports risks by integrating a flexible sensor network with a hybrid long short-term memory-graph convolutional network (LSTM-GNN) algorithm. The model adopts a lightweight edge computing architecture to reduce response latency and combines with migration learning to enhance cross-scene adaptability. Experiments show that its warning accuracy reaches 95.2%, which can be adapted to real-time monitoring and personalized sports guidance in sports classrooms, providing innovative theoretical and technological support for the safety and security of college students’ sports health and intelligent application of wearable devices.