We present GluBox, a multimodal forecasting system that leverages continuous glucose monitoring (CGM) as a core wearable sensing modality, together with behavioral and clinical data that influence blood glucose patterns, to predict long-term blood glucose patterns in individuals with type 1 diabetes while prioritizing clinically consequential errors.
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