Lightweight Sequential Transformers for Blood Glucose Level Prediction in Type-1 Diabetes

-- Type 1 Diabetes (T1D) affects millions worldwide, requiring continuous monitoring to prevent severe hypo-and hyperglycemic events. While continuous glucose monitoring has improved blood glucose management, deploying predictive models on wearable devices remains challenging due to computational and memory constraints. T o address this, we propose a novel Lightweight Sequential Transformer model designed for blood glucose prediction in T1D. The model is optimized for deployment on resource-constrained edge devices and incorporates a balanced loss function to handle the inherent data imbalance in hypo-and hyperglycemic events. Experiments on two benchmark datasets, OhioT1DM and DiaTrend, demonstrate that the proposed model outperforms state-of-the-art methods in predicting glucose levels and detecting adverse events. This work fills the gap between high-performance modeling and practical deployment, providing a reliable and efficient T1D management solution. Type 1 Diabetes (T1D) [1] is a chronic autoimmune condition requiring lifelong blood glucose concentration (BGC) monitoring to prevent life-threatening complications such as hypoglycemia (BGC below 70 mg/dL [2]) and hyperglycemia (BGC above 180 mg/dL [3]).