# An IoT-Based Non-Invasive Glucose Level Monitoring System Using Raspberry Pi
## 4 Discussion  
The presented results (Clark grid error = 90.32% in zone A, MAE = 10.37) suggests that our approach is competitive with those found in the literature: [PERSON] et al. [48] reported 87.7% of points in zone A, [PERSON] et al. [49] 81.2% for zone A, and [PERSON] et al. [50] reported a 96% of the points are in zones A and B, but only 57% are in zone A [50]. Despite the fact that non-invasive glucose meters have been presented previously, most of their applications are implemented locally, and the measurements are carried out in vitro or using a robust setup. Even with the promising results that IoT technology could bring, there are no new works that suggest the methodology for its implementation in a cloud device or devices of daily use. Our proposal was implemented and instrumented as an IoT system, in which all our measurements were carried out in vivo. The obtained data was validated by making comparisons with standard blood tests by means of the MAE and Clarke grid error. The use of Raspberry Pi Zero has advantages: it has a powerful microprocessor running a special Linux distribution, with digital I/O as Arduino has, but with more communications modules. The use of optical methodologies for quantifying glucose levels is currently one of the leading techniques conducting the major research in the field of non-invasive glucose estimation. In this regard, this work aimed to estimate the glucose level from histograms of pictures taken with the RPi Zero board by means of an intelligent algorithm based on an ANN. To this end, sensors for the final setup were embedded in a glove and are in charge of data acquisition, but also perform a pre-processing step by computing the histograms of acquired pictures, thus reducing the amount of data to be sent but leaving the whole neural network computing process to the server. As a result, a high scalability of the system is achieved, allowing other things to be attached, e.g., oximeter, ECG, temperature or blood pressure sensors could be added. All the above was validated by the system functionality, but also, in terms of accuracy as stated in the results section. The glucose estimation readings recorded by the present approach could be \begin{table}
\begin{tabular}{c c c} \hline  & **Train/Test Subset** & **Validation Subset** \\ \hline MAE & 11.81 & 10.37 \\ Clarke grid error zone (\%) & (83.84-16.16-0-0-0) & (90.32-9.68-0-0-0) \\ (A-B-C-D-E) & & \\ \hline \end{tabular} \({}^{1}\) MAE: mean absolute error.  
\end{table} 
Table 2: MAE \({}^{1}\) and Clarke grid error region on train/test and validation subsets. Figure 8: Clarke grid error results over (**a**) 10-fold cross-validation on the train/test sets, and (**b**) the external validation set. particularly useful to provide further user health information, permitting the medical specialist to perform a better diagnostic. Despite the variation of skin colour of participants not being studied in this work, it would be of great interest to take it into consideration for further research in which we propose to incorporate this new variable along with the histogram to feed the ANN. One limitation of the present proposal is that the instrumentation can be considered bulky for use during daily activities. However, the size of the system can be improved by using smaller batteries and designing PCB (Printed Circuit Board) circuits for integrating the whole system. Furthermore, the incorporation of more sensors connected to the RPi Zero could turn the current system in a health care platform, for this reason, this work can serve as a starting point towards a full smart health care service. Conceptualization, A.A.-P., I.P.G.-G. and G.A.A.-S.; Data curation, V.F.-G. and I.P.G.-G.; Formal analysis, J.C.-N. and R.E.C.-V.; Investigation, V.F.-G., J.C.-N. and R.E.C.-V.; Methodology, J.C.-N. and G.A.A.-S.; Software, A.A.-P. and V.F.-G., V.A.A.-P., I.P.G.-G. and G.A.A.-S.; Writing--original draft, A.A.-P. and G.A.A.-S.; Writing--review and editing, V.F.-G., I.P.G.-G., [PERSON]-N. and R.E.C.-V.