Carbohydrates are molecules of enormous biological importance that have empirical formulas such as C_n (H₂O)_n or C_n(H₂O)_n-1. These formulas suggest they are “hydrates of carbon” and that is why early chemists gave them the general name carbohydrates. We commonly call carbohydrates sugars, and they are also known as saccharides. They are organic compounds shown by the formula. Since these compounds are synthesized through photosynthesis in plants, they are mostly components specific to plant foods. However, they can also be found as the building blocks of many vital tissues in animals. Glucose is the most important carbohydrate group called simple sugar (monosaccharide) and is abundant in foods. Another area where determining glucose values is very important is the health sector. Measurements to determine blood glucose levels are important in the treatment of patients with diabetes and hyperglycemia [1]. Diabetes Mellitus is a very common hereditary disease today; It causes blindness, heart attack, kidney failure, amputation and, in later stages, death. The rapidly increasing number of patients makes it necessary to control and constantly monitor diabetes. Monitoring diabetes is also extremely important in terms of reducing the progression of the disease and healthcare costs [2]. Noninvasive techniques for blood glucose monitoring have gained significant research interest due to high sensitivity and better patient compliance, unlike invasive ones. Typical non-invasive biosensors based on different approaches include iontophoretic extraction of glucose from the skin, surface plasmon resonance, Raman spectroscopy, visible or near-infrared (NIR) spectroscopy, polarimetry, photo-acoustic probes and fluorescence methods. These methods may be an alternative option for continuous glucose measurement [3]. Optical detection of glucose allows for more frequent and better monitoring for people with diabetes. The development and creation of low-cost, non-invasive glucose measuring equipment would generate interest both among people who suffer from diabetes and among people who do not yet suffer but are in risk groups. One advantage of being non-invasive is that there are people who avoid taking the test using conventional invasive equipment, because they don’t like the prospect of getting their finger pricked (its uncomfortable and could lead to infections) and others who are very sensitive to the sight of blood, making self-testing by a conventional invasive equipment unlikely for a good part of the population. It would allow the acquisition by everyone of an equipment that would be used regularly at home and at work, allowing continuous monitoring and control and thus preventing a sudden emergency in the population already suffering from this disease (if blood glucose remains too high or is steadily rising, the person will speak with their doctor as they may need to adjust their treatment) or even the increase of the population with this problem.

The environmental aspect cannot be forgotten either, as the fact that we stop using (and throw away) meter blood strips as well as having to periodically change the needles of the conventional equipment’s is another point in favor of a change to the use of non-invasive equipment.

The key to a successful noninvasive optical measurement of glucose is the collection of an optical spectrum with a very high signal-to-noise ratio in a spectral region containing significant glucose. Optical sensors exploit different interaction properties of light with glucose molecules in a concentration-dependent manner. Infrared (IR) provides an optical window through which it passes subcutaneously, independent of the epidermis and skin pigmentation [3].

The aim of this study is to demonstrate the detectability of glucose with infrared light and its subsequent improvement so that people can measure blood sugar non-invasively in the future. In this promising experimental study, concentration-dependent determination of glucose was performed, and the results were recorded.

This section explains the elements that make up the architecture of this project: microcontroller (Arduino Uno), optical sensors, heartbeat sensor and wireless communication via the Bluetooth network. The firmware flowchart and its explanation are also presented. As well as the experimental tests and all the results obtained, which prove the functionality of the work as defined.

During this work, tests were carried out on the optical sensors (emitter Near IR led and receiver Near IR photodiode), reading the voltage at the receiver Near IR photodiode, so that the values could then be acquired by the microcontroller ADC resident on the Arduino Uno board. As mentioned above, this ADC has a resolution of 10 bits. To implement a process that reads glucose levels, measurements had to be taken and compared with commercial meters, namely the OneTouch Select Plus. The OneTouch Select Plus is a conventional equipment where we apply blood to a test strip. It will associate the custom interval limit before or after the meal with our result, depending on whether we have added a corresponding meal marker. Between the several choices of tests that we must detect diabetes (e.g., Glucose tolerance test (GTT), Random blood sugar test, Fast blood sugar test), the Fast Blood sugar test, also known by Fasting Plasma Glucose test (FPG) its typically the best option by their ability to provide a stable baseline (they are not influenced by recent meals or physical activity). This stability allows for accurate assessment of glucose metabolism. Fasting means after not having anything to eat or drink (except water) for at least 8 hours before the test. This test is usually done first thing in the morning, before breakfast. Diabetes is diagnosed at fasting blood glucose of greater than or equal to 126 mg/dl [13]. To have an idea of what type of results we should expect (interval limits):

For a different type of population groups, such as children or pregnant women, other levels than these should be considered.

In our experiment and to achieve a range of values that is robust and wide enough to be able to establish the connection between the infrared optical receiver voltage supplied to the Arduino and the glucose values, we used the values of 10 different persons aged 18 to 24 and whose physical condition was considered healthy. These 10 people measured their glucose values on the commercial conventional equipment (One Touch Select Plus meter) and then on our experiment. The values were recorded before and after meals and their average is presented in the Table 1.

The implementation in programming terms required linear regression to obtain the expression in (1), which is the result of an empirical process.

Linear regression is an equation used to estimate the expected value of a y variable. To obtain the equation, it is necessary to have real measured values and what the equation gives us, graphically, is a linear line that approximates the measured values. For our project, linear regression was important to obtain a glucose value in mg/dL according to the value in mV obtained by the sensor.

According to Lambert’s law, the intensity of light decreases exponentially as the thickness of the absorbing medium increases arithmetically.

When infrared radiation passes through the finger and is received by the infrared receiver (Figure 10), it is attenuated by the blood glucose molecules present in the blood and, depending on the amount of glucose, the value of IR light received by the infrared diode (photodetector) can be higher if there is a low level of glucose (this means that more of the NIR light is absorbed or scattered by glucose molecules in the blood) or lower in the if the opposite is the case (Figure 11). An integrated image of our glucose meter can be seen in Figure 12.

Based on the results in Table 1, a linear regression was implemented in Geogebra, as shown in Figure 13. The expression in (1) was then implemented in the C/C++ programming language for the microcontroller in the Arduino Uno. This expression can be seen in Figure 13 in the bottom left-hand corner. Table 2 shows the average values measured by the Arduino and the glucose level values measured by the One Touch Select Plus commercial meter.

For the heartbeat sensor, the results shown in Table 3 were obtained.

Figure 14 and Figure 15 shows our experiment during the testing phase and the APP developed with experimental results.

In this academic and experimental work, we were able to achieve an average error of 2.86 per cent when compared to commercial glucose meters, which we are very pleased about, as we were able to develop a system that is low cost (around 8 euro) in terms of hardware and low consumption, as in use it consumes around 50 mA. The possibility of the user and/or doctor being able to visualize the data collected via an APP is an added value, as is the addition of the heartbeat. An attempt was made to encapsulate the work to make it easier to use during tests.

In terms of improvements, clearly the physical aspect, as it was not very ergonomic. The next steps will be to do more extensive tests looking at different types of population (and diabetics). Integrating screenings carried out by the Ministry of Health or by private clinics with the aim of alerting and preventing this type of disease, would also be an objective as we would then have access to a sample of the wider population and could compare it with the values obtained by various commercial equipment used in these screenings.