Kalman Filter-based Microwave Blood Glucose Monitoring using A Priori Physiological Model

Diabetes mellitus (DM) is a highly prevalent endocrine metabolic disorder, and traditional finger-prick blood glucose monitoring methods pose infection risks. Microwave medical sensing and imaging (MMSI) leverages the dielectric properties of human tissues to facilitate non-invasive, continuous glucose monitoring, demonstrating significant potential for clinical application. This paper proposes a Kalman filter-based algorithm using a physiological glucose concentration model to optimize blood glucose estimation in microwave sensing systems utilizing microgel sensors. The algorithm integrates the physiological glucose model as the prediction step of the Kalman filter and employs a first-order Taylor series expansion to linearize the nonlinear variations in glucose concentration. This approach ensures that the filtering process incorporates both the current actual state and the model's linear approximation. The results indicate that, compared to traditional linear Kalman filtering, the improved algorithm enhances filtering effectiveness by 73.8%, significantly boosting system performance and enabling continuous glucose monitoring.