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This paper introduces Patch-TACA, a multimodal transformer that forecasts long-term blood glucose in healthy individuals by fusing CGM data with physiological and behavioral signals via time-aware cross-attention and self-supervised pretraining. On twelve participants, it achieved 14.26 ± 3.48 mg/dL RMSE at a 90-minute horizon and 93.9% hyperglycemia prediction accuracy, outperforming GlySim and Gluformer baselines. The results show that multimodal sensor fusion enables accurate long-horizon glucose forecasting for proactive metabolic health monitoring before disease onset.

Proposed a novel framework for generating CFs using finetuned large language models (LLMs), with a focus on structured sensor-derived datasets in health and physiological monitoring

This pilot study examined whether pre-stress music listening alters physiological and psychological responses. Thirty participants either listened to 10 minutes of self-selected relaxing music or quietly read before a modified Trier Social Stress Test.