Portable Non-invasive Blood Glucose Real-time Monitoring System

Diabetes has a great impact on the lives and work of people around the world,and there is no clinical cure.The current method is to frequently monitor patients’blood sugar combined with drugs to manage patients’ blood sugar levels.Existing clinical blood glucose testing is measured by blood collection by large instruments.There are also instruments for measuring blood glucose by minimally invasive blood collection.Although the results of such invasive or minimally invasive methods are more accurate,blood collection will bring trauma to users,Easy to be infected,so non-invasive blood glucose detection technology is of great significance.This paper has designed a non-invasive real-time blood glucose monitoring system,and designed a prototype that can accurately measure human blood glucose levels.The specific work is as follows:(1)According to the current research results of noninvasive blood glucose at home and abroad,a method for measuring blood glucose by combining energy conservation method and spectroscopic method is proposed.This method increases the characteristics of blood glucose parameters,makes the calculation of blood glucose more precise and strict,and makes the results more accurate.(2)This paper designs a non-invasive blood glucose real-time monitoring system,the entire system can be divided into hardware and software parts.The hardware part adopts a modular design idea,and designs a data acquisition instrument integrating multiple sensors.It uses STM32F103RCT6 as a processor to collect real-time energy conservation and spectrometry data for blood glucose measurement.The collected two kinds of data are sent to the host computer alternately through the serial port.(3)In the software part,the photoelectric volume pulse wave is filtered to remove the baseline,and features are extracted at the same time.Combined with minimally invasive blood glucose value for modeling,multiple linear regression,k-nearest neighbor algorithm regression and support vector regression are used to train three machine learning algorithm models to obtain blood glucose non-invasive measurement functions and establish a non-invasive blood glucose detection method.(4)Compare these three different machine learning algorithm design specific experiments,and compare their accuracy,correlation coefficient,mean square error and root mean square error.The experiment proves that the measurement accuracy based on the support vector regression algorithm is the best,the correlation coefficient is as high as 0.865,meanwhile the mean square error and root mean square error are lower than those of multiple linear regression and k-nearest neighbor algorithm.Then the support vector regression model based on Gaussian radial basis kernel function is selected as the optimal model,and the algorithm is used to detect human blood glucose in real time.