Noninvasive Blood Glucose Monitoring System Based on Deep Learning and Multiwavelength Near-Infrared Technology

Frequent blood glucose monitoring is crucial for patients with diabetes. However, current blood glucose measurement methods are primarily invasive and, thus, cause discomfort and infection risks. Accordingly, this study developed a noninvasive real-time glucose monitoring system based on a deep learning (DL) model (comprising convolutional neural network and long short-term memory network models) and multiwavelength near-infrared (NIR) light technology. The system is equipped with a portable finger gripper with NIR light-emitting diodes emitting at three distinct wavelengths (810, 860, and 940 nm) to illuminate the finger. A triad spectroscopy sensor is then used to capture finger photoplethysmography (PPG) signals within only 6 s. The captured signals are then transmitted to a server-side DL model for glucose prediction. This DL model analyzes the three-wavelength PPG data to predict a user’s blood glucose value. The predicted glucose value is subsequently displayed on a dedicated smartphone app. In contrast to previously proposed systems relying on machine learning for feature extraction, the proposed system uses a DL model to automatically extract glucose-related features from the three-band PPG signals, ultimately leading to blood glucose predictions with a root-mean-square error of 6.62. Furthermore, the proposed system prioritizes user comfort, portability, and stability, thereby offering a convenient and accessible blood glucose monitoring experience.