GlucoSense: early diabetes risk assessment via fusion of human activity recognition and glucose prediction

Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic and progressive metabolic disease characterized by chronically high blood glucose levels. It is a disease with an often asymptomatic onset, which means that a high percentage of affected individuals remain unidentified until quite far in the disease process, thereby underlining the importance of a good, reliable screening instrument for proactive detection. In this paper, GlucoSense is introduced as a single framework for early NIDDM risk stratification by combining Human Activity Recognition (HAR) with continuous blood glucose forecasting using two Graph Attention Network version 2 (GATv2)-based architectures. The first is a HAR module trained with smartphone triaxial accelerometer data that covers 23 activity classes, while the second is a glucose prediction module trained with the OhioT1DM dataset to predict glucose levels for a 30 min horizon. Unlike sequential models, GlucoSense encodes complex dependencies between movement patterns, cardiac activity, nutrition, and glycemic behaviour by modeling relationships in a graph structure. The study was longitudinal, spanning 90 days, with six participants. Three important clinical parameters, mean glucose level, time above hyperglycemic level, and glycemic coefficient of variance were calculated. The system was able to correctly identify metabolically healthy individuals and those with prediabetic patterns. The framework was cross-validated with HbA1c laboratory tests to verify the clinical consistency of the framework. Both modules were benchmarked against sequential, convolutional, attention-based, and graph-based baselines under an identical protocol with five independent runs. The glucose module lowered the root mean squared error from 31.82 mg/dL for an LSTM baseline to 25.60 mg/dL, and the activity module reached 98.50% accuracy, with every difference confirmed by paired significance testing and reported with 95% confidence intervals. Understanding glucose dynamics based on smartphone sensor data and a limited number of physiological markers, GlucoSense offers a non-invasive, scalable and cost-effective alternative to traditional clinical glucose screening. This paper demonstrates the feasibility of smartphone-based longitudinal diabetes risk assessment and motivates future large-scale population studies.