LAUSR

As the increasing number of diabetic around the world and the deficiencies of invasive blood glucose detection, such as prickly pain, risk of infection, and no continuous monitoring, a proper noninvasive blood glucose monitoring method is highly desired to deal better with it. In this article, an ultrawide band microwave based technique combined with a cascaded general regression neural network (C-GRNN) is proposed for blood glucose level estimation to achieve noninvasive and intelligent monitoring. Different glucose solutions are tested by using the reported monitoring system in which C-GRNN is used for intelligent detection and frequency choice. The proposed C-GRNN can reduce the prediction interval from 12.3858 to 2.1375 mg/dL compared with the conventional GRNN, and the root-mean-square error (RMSE) reaches 2.20 mg/dL. These results show good estimation performance. We also conduct a continuous-time blood glucose level monitoring on volunteer using the proposed noninvasive method. The final results show well accuracy in which the RMSE and mean absolute percentage error are 13.3208 mg/dL and 9.74%, respectively.