Wearable Multimodal Data Fusion Methods for Intelligent Assessment in Physical Education

Abstract

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.