Personalized diabetes monitoring platform leveraging IoMT and AI for non-invasive estimation

Abstract

Non-invasive blood glucose estimation is an extensively researched area since current gold-standard invasive glucose monitoring methods present numerous inconveniences and challenges in terms of comfort and cost. We present the design, development, and validation of an Internet of Medical Things (IoMT) based wearable device for non-invasive and real-time measurement of blood glucose. This paper presents a diabetic health monitoring platform architecture that consists of (a) a user-worn photoplethysmography (PPG) device, (b) a smart analytics cloud that deploys models for blood glucose estimation, and (c) an end-to-end mobile/web application for monitoring diabetes patients. Blood glucose computation is achieved using a novel light-weight 1-dimensional input-reinforced deep neural network architecture, which we call as GlucoNet. This captures both long and short, temporal and spatial features from the PPG signal. The training and validation of the model were conducted on a dataset of 283 participants which demonstrated a mean absolute percentage error (MAPE) of 17.8% ($\pm$ 12.8%) wherein 100% of predictions fall in the clinically acceptable zones A and B of the Clarke-error grid. The lightweight model is also deployed on edge devices for real-time and offline blood glucose measurement. We report a clinical outcome deployment study and insights from 20,000＋ glucose measurements obtained from another 600 patients. To our knowledge, this is the largest reported work employing a non-calibrated, non-invasive, demography, and time-of-food agnostic IoMT glucose monitoring system that does not require any feature engineering and is capable of running on edge devices.