## 4 Discussion  
For all the experiments, the difference between the classification and the recognition accuracies is due to the way that these performances are computed. Let us remember that, for classification, the time in which a gesture is executed is not taken into account--unlike for recognition, where the time matters in the computation of the result. The results presented in Table 3 show that the highest classification and recognition results are obtained in experiment 1, where we simulate an ideal case in which a user always wears the bracelet in exactly the same orientation both for training and testing. It is worth pointing out that the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Classification Accuracy (\%)** & **Recognition Accuracy (\%)** \\ \hline Female (15 users) & \(97.33\pm 1.72\) & \(95.89\pm 2.80\) \\ \hline Male (45 users) & \(95.40\pm 10.41\) & \(93.90\pm 12.52\) \\ \hline Left-handed (2 users) & \(97.69\pm 3.26\) & \(96.40\pm 1.70\) \\ \hline Right-handed (58 users) & \(95.82\pm 9.21\) & \(94.33\pm 11.11\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of the experiment 4 (4 sync.) classified by the mutually non-exclusive classes sex and handedness. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Classification Accuracy (\%)** & **Recognition Accuracy (\%)** & **Time (ms)** \\ \hline
1 & \(96.92\pm 2.14\) & \(95.32\pm 3.92\) & \(59.07\pm 13.64\) \\ \hline
2 & \(17.38\pm 8.06\) & \(8.03\pm 6.96\) & \(55.96\pm 11.44\) \\ \hline
3 (1 sync.) & \(81.54\pm 31.50\) & \(78.73\pm 34.06\) & \(64.61\pm 11.01\) \\ \hline
3 (2 sync.) & \(88.24\pm 24.42\) & \(86.11\pm 26.42\) & \(57.78\pm 8.78\) \\ \hline
3 (3 sync.) & \(88.62\pm 23.74\) & \(86.12\pm 26.13\) & \(51.77\pm 10.23\) \\ \hline
3 (4 sync.) & \(93.78\pm 14.90\) & \(91.93\pm 16.44\) & \(53.82\pm 10.76\) \\ \hline
4 (1 sync.) & \(83.01\pm 28.37\) & \(80.01\pm 31.87\) & \(67.46\pm 11.03\) \\ \hline
4 (2 sync.) & \(87.60\pm 24.40\) & \(85.52\pm 26.51\) & \(62.42\pm 7.53\) \\ \hline
4 (3 sync.) & \(94.65\pm 12.99\) & \(93.05\pm 14.59\) & \(52.43\pm 10.62\) \\ \hline
4 (4 sync.) & \(95.88\pm 9.07\) & \(94.40\pm 10.93\) & \(50.04\pm 5.58\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of each experiment of this work. results of experiment 1 are the targets for the results of the remaining three experiments. When, in experiment 2, we simulate the rotation of the sensor for testing, and when the recognition model does not include a correction of orientation, the classification and recognition accuracies hugely deteriorate, making the recognition model almost useless since a random model will achieve a classification accuracy of 17% for six classes. For the case of experiment 3, where there is rotation of the sensor, for testing and training, the EMGs are acquired in the recommended orientation for the My armband, and the correction of orientation is included for the training and testing EMGs. For experiment 3, the classification and recognition accuracies improve significantly with respect to the accuracies of experiment 2. The highest accuracies of experiment 3 are obtained with four synchronization EMGs. For the case of experiment 4, the training and testing EMGs are acquired in different orientations from the recommended orientation for the Myo armband, and the orientation of the sensor for training is different from the orientation of testing, and includes the correction of orientation for training and testing; we also observe an improvement of the accuracies with respect to the ones of experiment 2. Once again, among the accuracies of experiment 4, the highest results are obtained with four synchronization EMGs. The high standard deviations of the best accuracies of experiments 3 and 4 (both achieved with four synchronization EMGs) indicate that the recognition model with the correction of orientation proposed in this work has a performance that varies significantly from one user to another.