Key Findings
GlucoSense identifies early risk of non-insulin-dependent diabetes mellitus (NIDDM) by fusing human activity recognition with continuous blood glucose forecasting. In a 90-day trial with six volunteers, the system successfully distinguished between metabolic normality and prediabetes criteria. Two participants were flagged as high-risk, showing mean glucose levels of 113 mg/dL and 119 mg/dL, with variability rates of 37.5 percent and 39.4 percent, respectively.
Technical Details
The framework employs two Graph Attention Network version 2 (GATv2) modules. Unlike sequential LSTM models, GATv2 treats data as a graph where nodes represent feature streams (heart rate, acceleration, carbohydrate content) and edges represent statistical correlations. This allows the model to capture complex interdependencies between nutrition, movement, and glycemic behavior.
- GATv2-GraphSensor: Recognizes 23 diabetes-relevant activities using triaxial smartphone accelerometer data. To minimize battery drain for long-term monitoring, the team sampled at 1 Hz, achieving 98.50 percent classification accuracy.
- Glucose Forecasting: Trained on the OhioT1DM dataset, this module predicts blood glucose 30 minutes ahead. It achieved a root mean squared error of 25.60 mg/dL, with 94.30 percent of predictions falling within the clinically safe Zone A of the Clarke Error Grid.
Background and Context
Type 2 diabetes affected over 500 million adults in 2021, with projections suggesting a rise to 12.2 percent of the adult population by 2045. Because the condition often develops without early symptoms, only about 45 percent of people with the disease have a confirmed diagnosis. GlucoSense addresses this by using transfer learning; while trained on type 1 diabetes data, it applies general metabolic relationships—such as muscle glucose uptake during exercise—to assess type 2 risk.
Significance and Outlook
The system provides a non-invasive method for early risk stratification by deriving three clinical indicators: mean glucose concentration, time above hyperglycemic thresholds (140 and 180 mg/dL), and the glycemic coefficient of variation. By removing the need for clinic visits or glucose sensors, the framework nudges users toward clinical consultation before symptoms appear.
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