Accurate prediction of glucose concentration and identification of major contributing features from hardly distinguishable near-infrared spectroscopy

Diabetes occurs due to lack of insulin generation inside the human body. The continuous glucose monitoring using noninvasive measurement in smart healthcare can be effective for the diabetes people. The conventional method for glucose measurement requires pricking the blood samples, which may not be advisable for frequent measurement specially for old age people and children. This article provides brief review about various approaches of continuous glucose measurement with noninvasive manner. This article covers the state-of-the-art glucose measurement methods and its control mechanism. The study of various consumer products have also been discussed along with the open challenges.

Everything You Wanted to Know About Continuous Glucose Monitoring

Diabetes is a long-term condition in which a person’s body cannot break down blood sugar adequately due to a shortage of insulin. The most crucial element of health care is continuously monitoring blood glucose (BG) levels. The main concern of effective glucose monitoring equipment is based on the blood-pricking technique. However, this may not be suggested for frequent glucose measurement. The paper presents various glucose-measuring technologies. The research discusses various non-invasive glucose measurement techniques and their management using advanced medical technologies. The configuration of the precise measuring device is essential to meet the blood glucose monitoring requirements that are not invasive systems. Non-invasive glucose monitoring devices solve the issue of frequently pricking patients for blood samples for clinical tests. For the goal of continuous health monitoring, a Smart Healthcare framework would be built on the Internet-of-Medical-Things (IoMT) and a Healthcare Cyber-Physical System (H-CPS) to estimate blood glucose. The study also discusses a few consumer devices and cutting-edge methods for measuring glucose. The paper also outlines the several difficulties and open challenges with glucose prediction.

Non-invasive Glucose Measurement Technologies: Recent Advancements and Future Challenges

In this paper, we have presented the noninvasive monitoring of blood glucose concentration that was performed by using the smartphones camera, different lighting sources, and the near infra-spectroscopy used by transmission photoplethysmography (PPG). The system implementation was associated with getting the PPG signal at the near infra wavelengths of 850 nm and 1070 nm. The processing of the photoplethysmography (PPG) signal was carried out with the Linear Regression (LR), Support Vector Regression (SVR), Deep Neural Network (DNN), and Random Forest Regression (RFR) for estimating the glucose level in the blood. We have collected three sets of data and analyzed them using the Python programming language to find out the relation between blood glucose concentration and photoplethysmography (PPG). The predicted glucose level was 4.7 mg/dl with respect to 5 mg/dl.

Noninvasive Blood Glucose Measurement Using Live Video by Smartphone

Diabetes mellitus is a major health problem which needs regular glucose monitoring for management of the disease. Invasive blood glucose measuring systems with acceptable accuracy are currently used for measurements. Several non- invasive blood glucose measurement techniques are reported in research literature, but most of them are not commercially available due to low accuracy or dependency on individual physiological parameters. This paper presents a hybrid technique to measure blood glucose level non-invasively using a combination of multi-wavelength Near Infrared (NIR) spectroscopy and bio-impedance spectroscopy. Physiological parameters of individuals and other environmental factors that affect non-invasive glucose measurements have been identified and compensated to make the device work on any person without calibrations. The measured parameters, along with the glucose level of the subject obtained from a commercial blood glucose meter is used to train a Random Forest regression algorithm. A training set of 315 data samples were used for the development of the system. The accuracy of predictions was tested using a testing set of 80 data samples. The trained system can predict the blood glucose levels non-invasively with 90.7% accuracy.

Removing subject dependencies on Non-Invasive Blood Glucose Measurement using Hybrid Techniques

Diabetes is already the world's most costly epidemic. Current glucose monitoring are invasive, which requires painful procedures and not reliable for long term usage. Impedance of human skin provides details about physiological structure and chemical composition of the skin and underneath tissues. Glucose concentration will affect the dielectric properties of cellular membranes, and therefore, changing the impedance of the blood. This paper describes a non-invasive measurement continuous glucose monitoring system based on bioelectrical impedance. In this project, an alternative current constant current source with a frequency of 50kHz and peak-to-peak of 1mA is developed and injected to the finger tip through the developed tetra-polar electrode. The voltage drop across the finger will be measured and also used to determine the variation on glucose concentration. The developed prototype device will help to manage the painful procedures, cost-effective and reduce the infection rate as compared to the conventional invasive technique. Preliminary results show that 80% of measurement accuracy of the developed system measured non-invasively blood-glucose by means of bioelectrical impedance.

A Bio-Impedance Approach

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A Bio-Impedance Approach