# An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
## 1 Introduction
Some of the commercially available include the Gluco Track, a registered trade mark from Integrity Applications Ltd. Israel, which uses a combination of ultrasonic and thermal technology to measure glucose from the earlobe, but requires individual calibration [25]; and the FreeStyle Libre developed by Abbott Diabetes Care Inc. USA, to determine glucose levels in adults, but still have a high false positive rate for hypoglycaemia [26]. Non-invasive commercial monitors are still in development, such as the SugarBEAT by Nemaura Medical Inc. UK that performs the glucose measurement using a disposable skin-patch connected to its own transmitter. MediWise Ltd. United Kingdom releases the GlucoWise, which works by transmitting low power radio waves through the earlobe [26]. In addition, the smart contact lens developed by Google uses the tear fluid for measuring the glucose concentration [27]. The technologies used by non-invasive glucose monitoring systems addressed in previous works are mainly transdermal, thermal and optical. The transdermal glucose estimation is performed by sonophoresis, reverse iontophoresis or ultrasound [25; 28]. The thermal approaches aim to estimate glucose concentrations by measuring the physiological response to thermal emissions, due to glucose having absorptive effects on the heat radiation that are directly related to its concentration [28; 29]. In optical methods, a light beam is directed to human tissue and the energy absorption, reflection or scattering is used to estimate the glucose concentration [30]. Commonly, optical methods are preferred due to its simplicity and that small laser diodes can be used to construct portable and inexpensive devices. Besides, other technologies such as transdermal sensors need a thorough calibration process and are more susceptible to non-controlled environment [25; 31; 32]. However, there is a range of factors that affect the glucose estimation, such as sweating, skin roughness, ambient temperature or pressure. For this reason, the search for newly and more accurate non-invasive technologies, is still underway [28; 31]. Scientific efforts have been directed to develop non-invasive glucose meters and systems, from which optical technologies have gained attention [30], near infrared (NIR) [33; 34], Raman [35; 36], and Fourier transform infrared (FTIR) [30] spectroscopy are among the most investigated. On a regular basis, the systems referring to the state-of-the-art share similar components: (i) an energy source by a light beam in certain wavelength, commonly interacting with (ii) a medium or region of interest in the body, (iii) a module with signal acquisition sensors, (iv) a microcontroller or signal processor, and (v) an element where the result will be displayed. However, the choice of both the characteristic of components (i)-(iv) and the physical principle on which the system is built, leads to very different results. Depending on the sensor used for this purpose, optical techniques analyse the medium (e.g., fingertip or ear lobe) based on the reflection, refraction, dispersion or absorption of the light beam provided by the energy source. Previous works have mainly considered the use of energy sources with wavelengths in the near infrared range. In this paper, an IoT-based monitoring framework for non-invasive blood glucose monitoring is described. Data for the non-invasive monitoring is acquired taking a picture of the user's fingertip using the camera of a Raspberry Pi Zero (RPi), and then data is processed by a neural network implemented using the Tensorflow libraries in a Flask microservice. The estimated glucose values can be harvested by an end device such as a smartphone for monitoring purposes, or by any other end device allowing the opportunity for different potential applications.