## 5 Results
### Testing and Validation  
Through the natural competition established between the Math and ML agents, with their respective strategies, it was possible to establish the best result between them and practice a continuous flow of active reinforcement learning using historical data from seven volunteers for up to 21 days. The best result is always closer to the ideal glucose range: 80-120 mg/dL in fasting and up to 160 mg/dL in the postprandial period. Daytime and night-time windows were personalized for each individual (Table 6), with the addition of information related to their usual sleeping hours (night-time) and active hours (daytime). Such personalization is needed to decide whether Tesseratus must be fed by a new external stimulus. In addition, our prediction horizon for the night-time is eight hours at most, and individuals with night-time windows greater than that must have their prediction horizon updated. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Days** & **Source** & **Insuin** \\ \hline R-BRA01 & 14 & FreeStyle Libre\({}^{\text{\textregistered}}\) & aspart + degladec \\ \hline R-BRA02 & 21 & Minimed 640G\({}^{\text{\textregistered}}\) & lispro \\ \hline R-BRA03 & 14 & FreeStyle Libre\({}^{\text{\textregistered}}\) & aspart + degladec \\ \hline R-BRA04 & 21 & Minimed 640G\({}^{\text{\textregistered}}\) & lispro \\ \hline R-BRA05 & 14 & FreeStyle Libre\({}^{\text{\textregistered}}\) & aspart + glargine \\ \hline R-BRA06 & 21 & Paradigm VEO 754\({}^{\text{\textregistered}}\) & lispro \\ \hline R-BRA07 & 14 & FreeStyle Libre\({}^{\text{\textregistered}}\) & lispro + degladec \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Satellite characteristics used to collect data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Hypertenusive** & **HbAlc (\%)** & **eGDR** \\ \hline R-BRA01 & 0 & 5 & 11.7 \\ \hline R-BRA02 & 1 & 6 & 4.51 \\ \hline R-BRA03 & 0 & 6.3 & 9.91 \\ \hline R-BRA04 & 1 & 5.6 & 3.2 \\ \hline R-BRA05 & 0 & 8 & 8.8 \\ \hline R-BRA06 & 0 & 6 & 10 \\ \hline R-BRA07 & 0 & 7.5 & 9.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Information used to calculate estimated glucose disposal rate (eGDR). We used the Parkes error grid (PEG) [56] to evaluate errors in the measurement of predicted glucose oscillation provided by Tesseratus. PEG was adopted because it is compliant with ISO15197 [57] and separates T1D individuals from individuals with type 2 DM. Moreover, PEG had its technical issues revised by [PERSON] et al. 2013 [54], who established exact borders for the performance zones for glucose measurements and supported the accuracy definition for glucose monitors. The zones are categorized as A, B, C, D and E. The associate meaning for a measurement of being in a zone is: zone A--clinically accurate measurements, no effect on clinical action; zone B--altered clinical action, little or no effect on clinical outcome; zone C--altered clinical action, likely to affect clinical outcome; zone D--altered clinical action, could have significant clinical risk; zone E--altered clinical action, could have dangerous consequences. PEG was used considering the predicted **versus** measured values, and the success metric was simple: the more prediction points that fall within zones A and B, the better the model. Table 7 details the PEG for the daytime window considering each prediction horizon (PH) (from 15 min to 4 h). It can be depicted that 95.1% of measurements, on average, fall in zones A and B. The same result is presented in Figure 7, showing a total of 3804 predictions. Zones A and B are the closest ones to the diagonal line of the plot.