## 3 Results  
The accuracy and training time for the motion classification models according to the numbers of hidden units and epochs used during training are presented in Tables 2 and 3, respectively, whereas the visual representations are given in Figures 5 and 6. The accuracy increased as the number of epochs increased from 150 to 300 regardless of the number of hidden units. Depending on the conditions, the accuracy was the highest at approximately 300 and 500 epochs and after that, it showed a general decreasing trend. A detailed look at the training model accuracy in each condition reveals that the accuracy was the highest at 300 epochs for hidden units 150 and 200 and at 500 epochs for hidden unit 100. For 150 hidden units, the accuracy after 750 epochs slightly increased although a decreasing trend was found overall. The training time of each model increased from a minimum of 28.1 min (100 hidden units and 150 epochs) to a maximum of 320 min (200 hidden units and 900 epochs), which verifies the increase in training time in relation to increases in the numbers of hidden units and epochs. However, across all conditions, the model with the highest accuracy (99.82%) had 150 hidden units and 300 epochs and took 78.15 min to train. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Training Accuracy (\%)**} & \multicolumn{5}{c}{**Epoch**} \\ \cline{2-7}  & **150** & **300** & **500** & **750** & **900** \\ \hline \multirow{3}{*}{Hidden unit} & 100 & 93.46\% & 98.02\% & 98.50\% & 94.86\% & 90.53\% \\  & 150 & 96.19\% & 99.82\% & 95.32\% & 98.58\% & 97.54\% \\ \cline{1-1}  & 200 & 95.94\% & 98.90\% & 95.98\% & 94.74\% & 90.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training accuracy according to numbers of epochs and hidden units. Figure 4: Model construction. Figure 7 presents the training progression for 900 epochs and 100 hidden units. The accuracy graph shows the accuracy according to the increase in epochs, where the solid line indicates the training accuracy for the training data and the dotted line indicates the validation accuracy. The loss graph shows the error (or loss) based on the increase in epochs, where the solid line indicates the loss according to the training progression and the dotted line indicates the validation loss; here, lower values indicate better training performance, in contrast to accuracy. On the accuracy graph, increases in the number of epochs first led to increases in training accuracy and validation accuracy. Then, after a certain level, a fall in validation accuracy was observed. On the loss graph, an interval of increases in validation loss was observed, after which a gap between the training accuracy and loss appeared. The validation accuracy was the highest at approximately 400 epochs in the 900 epochs and 100 hidden units' condition. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Training Time (min)**} & \multicolumn{5}{c}{**Epoch**} \\ \cline{2-7}  & **150** & **300** & **500** & **750** & **900** \\ \hline \multirow{3}{*}{Hidden unit} & 100 & 28.10 & 50.37 & 81.01 & 120.06 & 148.37 \\  & 150 & 49.41 & 78.15 & 112.39 & 202.35 & 231.14 \\  & 200 & 66.29 & 99.02 & 183.43 & 276.18 & 320.01 \\ \hline \multirow{3}{*}{Hidden unit} & 100.00\% & \multirow{3}{*}{96.00\%} & \multirow{3}{*}{94.00\%} & \multirow{3}{*}{94.00\%} & \multirow{3}{*}{92.00\%} \\  & 98.00\% & & & & \\ \cline{1-1}  & 94.00\% & & & & \\ \cline{1-1}  & 92.00\% & & & &  
Tables 4 and 5 present the confusion matrices for 150 hidden units and 300 epochs and for 200 hidden units and 900 epochs, respectively. The rows in each table indicate the classification result, and the columns indicate the actual class. The diagonal cells of the matrix indicate the successful classifications, whereas the non-diagonal cells indicate misclassifications. A greater number of errors were found in the training results in Table 5 than in Table 4, despite the higher numbers of epochs and hidden units.