MMG-net: Multi modal approach to estimate blood glucose using multi-stream and cross modality attention.

• The significance of incorporating multiple modalities, namely PPG, electrodermal activity (EDA), and skin temperature (ST), in contrast to relying on a single modality for blood glucose estimation. • Exploration of the utilization of food logs as a valuable data source to enhance the accuracy of blood glucose estimation. • Introduction of a multi-stream neural network architecture augmented with cross-modality attention mechanisms, a novel approach that substantially improves the precision of blood glucose level estimation. In the context of effective disease management for hyperglycemia patients, regular monitoring of blood glucose levels is imperative. However, traditional glucose monitoring methods suffer from invasiveness, discomfort, and potential infection. This paper introduces an innovative approach that utilizes non-invasive data sources derived from wearable devices, namely Photoplethysmography (PPG), Electrodermal Activity (EDA), and skin temperature (ST), in combination with user-provided food logs. The proposed model, MMG-Net, uses the three waveforms along with food features extracted from food logs to estimate blood glucose levels. MMG-Net delivers exceptional performance metrics, achieving a Mean Absolute Error of 13.51 mg/dL, a Mean Absolute Percentage Error of 12.57 %, and a Root Mean Square Error of 17.26 mg/dL. Notably, MMG-Net outperforms existing solutions in the estimation of blood glucose levels, solidifying its status as an innovative approach. The model's clinical precision is substantiated through Clarke Error Grid analysis, with a remarkable 99.43 % of predictions falling within clinically acceptable ranges. This paper presents a substantial advancement in non-invasive blood glucose monitoring, offering a promising avenue for enhanced disease management among hyperglycemic patients with only wearable devices. [ABSTRACT FROM AUTHOR]