Predictive Glucose Monitoring for People with Diabetes Using Wearable Sensors

Abstract

Diabetes is a chronic non-communicable disease resulting from pancreatic inability to produce the hormone insulin, or a physiological cellular inability to use this hormone effectively. This leads to unregulated blood glucose levels, which can cause significant and often irreversible physiological damage. Current means of glucose level monitoring range from infrequent capillary blood glucose sampling to continuous interstitial fluid glucose monitoring. However, the accuracy of these methods is limited by numerous factors. A potential solution to this shortcoming involves the use of wearable sensors that record an individual’s physiological responses to a range of daily activities, which are subsequently fused and processed with machine learning (ML) algorithms to provide a prediction of an individual’s glucose level and can provide an artificial intelligence-driven glucose monitoring platform. In this paper, we conduct a comparison case study using quadratic discriminant analysis (QDA) and support vector machine (SVM) algorithms for the classification of glucose levels with data acquired from the wearable sensors of a type 1 diabetic individual. Preliminary results demonstrate predicted glucose levels with >70% accuracy, indicating potential for this approach to be used in the design of an ergonomic glucose prediction platform utilizing wearable sensors. Further work will involve the exploration of additional datasets from affordable wearables to enhance and improve the prediction power of the ML algorithms.