Multi-modal affective computing framework for real-time monitoring of children's learning engagement

Children's attention monitoring is crucial in education for improving learning outcomes. However, existing methods often rely on single-modality inputs, making it difficult to address the complexity of children's emotional expressions and attention fluctuations. This paper presents CSW-FocusNet, a multi-modal affective computing framework that integrates visual, auditory, motion, and physiological modalities, combined with temporal modeling and personalized feedback mechanisms for real-time monitoring of children's attention. CSW-FocusNet consists of three key modules: the CENet module for multi-modal feature fusion and dynamic relation capture, the STFEformer module for temporal feature modeling and attention prediction, and the SWO module for personalized feedback strategies. Extensive experiments on the different datasets demonstrate that CSW-FocusNet outperforms 9 baseline models across all metrics. On the DEAP dataset, its Macro-F1 is 0.85, MCS is 0.89, and RMSE is 1.32, significantly exceeding other methods. Future work will focus on refining feedback strategies for improved adaptability and real-time performance.