Classification of Smartphone Interaction Using Multimodal Physiological Signals with a Brain-Body Spatio-Temporal Transformer

Distinct smartphone interaction behaviors, like short-form video scrolling and mobile gaming, elicit qualitatively different cognitive and physiological responses. However, such distinctions is often overlooked by approaches that treat smart-phone use as a monolithic behavior. This paper presents Brain-Body Spatio-Temporal Transformer (BB-STT), a unified deep learning framework for classifying interaction-specific physiological signatures from multimodal signals, including EEG, EDA, PPG, and eye-tracking. BB-STT achieves 83.51% accuracy in distinguishing smartphone from non-smartphone activity and 74.13% accuracy in three-class classification of short-form video, gaming, and baseline viewing. The model demonstrates strong generalization with leave-one-subject-out (LOSO) performance that is also comparable to 5-fold cross-validation accuracy. Cross-modal attention emerges as the key component, improving three-class accuracy by 16.74 points through dynamic integration of multimodal signals. Interpretability analysis indicates a hierarchical organization of physiological responses. Eye-tracking features, particularly gaze depth, enable coarse separation between smartphone and non-smartphone activity. In contrast, finer discrimination between passive video viewing and active gaming on smartphones relies on the joint contribution of bilateral pupil dilation and central EEG features. Together, these results demonstrate the potential of multimodal physiological signals for objective, real-time assessment of digital engagement in naturalistic settings.