Abstract
Recent advancements in non-invasive blood glucose detection have seen progress in both photoplethysmogram and multiple near-infrared methods. While the former shows better predictability of baseline glucose levels, it lacks sensitivity to daily fluctuations. Near-infrared methods respond well to short-term changes but face challenges due to individual and environmental factors. To address this, we developed a novel fingertip blood glucose detection system combining both methods. Using multiple light sensors and a lightweight deep learning model, our system achieved promising results in oral glucose tolerance tests. A total of 10 participants were involved in the study, each providing approximately 700 data segments of about 10 seconds each. With a root mean squared error of 0.242 mmol/L and 100% accuracy in zone A of the Parkes error grid, our approach demonstrates the potential of multiple near-infrared sensors for non-invasive glucose detection.
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