Non-invasive, intelligent, and continuous monitoring of human blood glucose with UWB dual-antenna and cascade CNN

Finger-prick blood collection process has become unrealistic for a long-term and frequent blood glucose detection. Hence, an appropriate non-invasive detection system is highly desirable to effectively address this concern. A non-invasive and intelligent dual-sensing system is forwarded in this paper. The feasibility of the proposed system has been verified using glucose solution, animal serum, and human trials. In the in vivo experiments, the detection signal exhibited a high correlation (r = 0.96) with blood glucose levels. An improved cascade convolution neural network is suggested to accurately predict the BGL. For the estimation results of BGL, the root mean squared error of 7.3217 mg dl(-1) and a mean absolute relative difference of 4.7209% are achieved. The estimated results also fell by 100% in the clinically acceptable zones of the Clarke error grid analysis, indicating that the proposed system could potentially be used for clinical measurements.