Wearable Multimodal Data Fusion Methods for Intelligent Assessment in Physical Education

With the growing demand for intelligent and precise physical education (PE) evaluation, traditional single‐source data or manual scoring approaches have shown clear limitations in terms of objectivity, efficiency, and scalability. This paper proposes a novel Multimodal Attention‐based Transformer‐Enhanced Deep Fusion Model (MAT‐DFM) for intelligent assessment in PE, leveraging wearable sensor data, video recordings, and audio signals to construct a robust, real‐time evaluation framework. Through temporal synchronization, deep neural feature extraction, and attention‐driven fusion, the model captures both physical performance and contextual behavioral cues. Extensive experiments on a custom multimodal PE dataset demonstrate that MAT‐DFM achieves superior accuracy (91.3%) and lower MAE (3.42) compared to multiple state‐of‐the‐art baselines, validating the effectiveness of transformer‐based multimodal fusion. Furthermore, the model supports real‐time feedback and fine‐grained skill analysis, providing a comprehensive and scalable solution for smart PE instruction. This work presents an innovative fusion strategy that advances the development of wearable multimodal assessment systems in education.