TSPADS: A Transformer-Based System for Proactive Student Physical Health Monitoring

With the continuous growth in the scale of student physical health (SPH) monitoring data, annually sampled time-series records provide a valuable foundation for risk early warning. However, traditional models often fail to capture multi-year developmental trajectories of individuals, resulting in delayed intervention for students at potential health risk. This study aims to develop a Transformer-based Student Physical Anomaly Detection System (TSPADS), which is a dedicated intelligent software system to enable effective and timely anomaly detection in SPH data. The proposed TSPADS is built on the Transformer architecture and incorporates a novel masked anomaly-attention mechanism to learn implicit long-span dependencies in SPH data. A density-based clustering algorithm is then applied to distinguish anomalies and automatically generate hierarchical warning signals. Comprehensive experiments were conducted on a public multimodal movement and health dataset. The results demonstrate that TSPADS achieves high effectiveness and efficiency in both anomaly detection and classification tasks. The system shows strong potential to assist educational administrators and physical education teachers in providing timely, personalized health guidance, thereby addressing a critical gap in existing student health monitoring approaches.