## 4 Discussion  
The study's objective was to classify the motor competence of schoolchildren performed in a school using smart bands according to age range and sex, using machine-learning techniques optimized with hyperparameters acceptably for the classification indicated through the passing results in the tests carried out. The results of the study have shown that according to the motor competence tests carried out, step cadence values obtained range from 96 to 29 steps in both sexes and the number of steps ranges from 1437 to 455, with speed from 4.75 to 1.16 and the stride from \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multirow{2}{*}{**Subcharacteristics**} & \multirow{2}{*}{**Metrics**} & \multicolumn{2}{c}{**Percentage of**} & \multirow{2}{*}{**Quality Level**} \\  & & & & **Compliance** & \\ \hline Functionality (FUN) & FUN. 1 Fit for purpose & 20 & 84.00\% & Satisfied \\  & FUN. 2 Accuracy & 6 & 86.67\% & Satisfied \\  & FUN. 3 Interoperability & 4 & 86.67\% & Satisfied \\  & FUN. 4 Security & 3 & 53.33\% & Does not satisfy \\  & Subtotal & 33 & 81.88\% & Satisfied \\ Usability (USA) & USA.1 Ease of Compression & 5 & 90.00\% & Satisfied \\  & USA. 2 Learning Capacity & 8 & 92.00\% & Satisfied \\  & USA. 3 Graphical Interface & 4 & 95.00\% & Satisfied \\  & USA. 4 Operability & 3 & 52.00\% & Does not satisfy \\  & Subtotal & 20 & 82.73\% & Satisfied \\ Reliability (RIA) & RIA. 1 Maturity & 5 & 60.00\% & Does not satisfy \\  & RIA. 2 Fault tolerance & 4 & 90.00\% & Satisfied \\  & RIA. 3 Recovery & 2 & 50.00\% & Does not satisfy \\  & Subtotal & 11 & 69.09\% & Does not satisfy \\ \hline  & Total & 64 & 77.09\% & Satisfied \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Categories and characteristics according to the prototype of Software Quality Systemic Model (SQSM). Figure 11: Calculation interfaces and use of AI to generate the value of your motor competence within the percentiles. 88 to 71. Furthermore, it was verified that the measurements obtained decreased rapidly with age, being more pronounced in females than in males. In this context, the cut-off points suggested in other recent research [37] were taken as a basis, where the study proposed percentiles of the number of steps in a day. The percentiles indicate <p25 below average, p25 to p75 average, and >p75 above average. In essence, percentiles, regardless of the method used, can be applied to establish improvement goals, especially for schoolchildren below the 25 th percentile [38]. For example, the schoolchildren in this study classified below the p25 th percentile in the tests performed showed higher BMI values. Precisely, these results coincide with those found in [4], when it states that the measurements obtained decrease rapidly with age. This information could help promote strategies for developing physical activity in schoolchildren in educational centers to improve their performance. However, a limitation found in the study was the need for a broader set of tests to be performed. Furthermore, the results of the study have shown that using machine-learning techniques such as those evaluated by [PERSON] et al. [14; 39], then optimized with hyperparameters like work carried out by [PERSON] et al. [38] and [PERSON] et al. [40], they achieve reasonable classifications for schoolchildren according to their motor competence. It can also be seen that the naive Bayes algorithm obtains the lowest results [41; 42]. It was determined that the most suitable supervised machine-learning technique would be the gradient-boosted model, which has obtained the best accuracy of 0.95 with an f-score of 0.93, recall of 0.92, and precision of 0.94, which was used in works such as [43; 44]. This has been corroborated by the ROC-AUC curves, where the \"Low\" classes for males and \"High\" for females show a substantial elevation towards the upper left corner of the graph and have a high area under the curve of 0.98; this indicates that the model has a high sensitivity to detect motor competence classification.