Abstract
Accurate blood glucose forecasting enables proactive management of metabolic health, particularly when leveraging data from wearable sensors that capture data about physiological and behavioral health. However, existing models struggle with integrating multimodal time-series data with inconsistent sampling rates. This paper proposes a novel forecasting framework that incorporates a time-aware cross-attention mechanism with an LSTM architecture to predict blood glucose levels using continuous glucose monitoring (CGM) data alongside physiological and behavioral signals, such as heart rate (HR), electrodermal activity, accelerometry, and dietary intake. The proposed method dynamically encodes temporal features without the need for preprocessing and employs gated multi-head cross-attention layers to fuse sensor modalities effectively. We evaluate our approach on a newly constructed dataset involving 12 participants. Our method outperforms the baseline and state-of-the-art GlySim models across multiple prediction horizons ranging from 5 minutes to 90 minutes, achieving up to 17.8% improvement in Root Mean Squared Error (RMSE) values.
Shovito Barua Soumma
Graduate Research Associate
I am a Ph.D. candidate at Arizona State University. I work as a Graduate Research Associate at Embedded Machine Intelligence Lab (EMIL) under the supervision of Dr. Hassan Ghasemzadeh.
Asiful Arefeen
Graduate Research Assistant
I am a PhD student at Arizona State University (ASU). I am working under the supervision of Professor Hassan Ghasemzadeh at the Embedded Machine Intelligence Lab (EMIL). I am interested in explainable AI, using AI to generate interventions in digital health, machine learning, passive sensing and mobile health. I received a BS in Electrical & Electronic Engineering from Bangladesh University of Engineering & Technology (BUET) in 2019 and an MS in Biomedical Informatics from Arizona State University in 2023.
Hassan Ghasemzadeh
Director
Hassan Ghasemzadeh is an Associate Professor of Biomedical Informatics at Arizona State University (ASU) and a Computer Science Adjunct Faculty at Washington State University (WSU).