Multi-scale feature fusion model for real-time Blood glucose monitoring and hyperglycemia prediction based on wearable devices

Abstract

Accurate monitoring of blood glucose levels and the prediction of hyperglycemia are critical for the management of diabetes and the enhancement of medical efficiency. The primary challenge lies in uncovering the correlations among physiological information, nutritional intake, and other features, and addressing the issue of scale disparity among these features, in addition to considering the impact of individual variances on the model's accuracy. This paper introduces a universal, wearable device-assisted, multi-scale feature fusion model for real-time blood glucose monitoring and hyperglycemia prediction. It aims to more effectively capture the local correlations between diverse features and their inherent temporal relationships, overcoming the challenges of physiological data redundancy at large time scales and the incompleteness of nutritional intake data at smaller time scales. Furthermore, we have devised a personalized tuner strategy to enhance the model's accuracy and stability by continuously collecting personal data from users of the wearable devices to fine-tune the generic model, thereby accommodating individual differences and providing patients with more precise health management services. The model's performance, assessed using public datasets, indicates that the real-time monitoring error in terms of Mean Squared Error (MSE) is 0.22mmol/L, with a prediction accuracy for hyperglycemia occurrences of 96.75%. The implementation of the personalized tuner strategy yielded an average improvement rate of 1.96% on individual user datasets. This study on blood glucose monitoring and hyperglycemia prediction, facilitated by wearable devices, assists users in better managing their blood sugar levels and holds significant clinical application prospects.