Non-invasive Diabetes Level Monitoring System Using Artificial Intelligence and UWB

Abstract

Diabetes is a silent-killer disease throughout the world. It is not curable, therefore, regular blood glucose concentration levels (BGCL) monitoring is necessary to be healthy in a long run. The traditional way of BGCL measurement is invasive by pricking and collecting blood sample from human arm (or finger-tip), then measuring the level either using a glucometer or sending to laboratory. This blood collecting process produces significant discomfort to the patients, especially to the children with type-A diabetes, resulting increased undetected-cases and health-complications. To overcome this drawbacks, a non-invasive ultra-wideband (UWB) BGCL measurement system is proposed here with enhanced software module. The hardware can be controlled through the graphical user interface (GUI) of software and can execute signal processing, feature extraction, and feature classification using artificial intelligence (AI). As AI, cascade forward neural network (CFNN) and naive bayes (NB) algorithms are investigated, then CFNN with four independent features (skewness, kurtosis, variance, mean-absolute-deviation) are found to be best-suited for BGCL estimation. A transmit (Tx) antenna was placed at one side of left-earlobe to Tx UWB signals, and a receive (Rx) antenna at opposite side to Rx transmitted signals with BGCL marker. These signals are saved and used for AI training, validation and testing. The system with CFNN shows approximately 86.62% accuracy for BGCL measurement, which is 5.62% improved compared to other methods by showing its superiority. This enhanced system is affordable, effective and easy-to-use for all users (home and hospital), to reduce undetected diabetes cases and related mortality rate in near future.