## 4 Discussion  
The purpose of this pilot study was to compare the training time of the LSTM model using a common PC with typical specifications. Specifically, a simplified experimental protocol was used to determine the effect of the number of epochs and hidden units, and only a single accelerometer with a low sampling frequency (20 Hz) was used. In addition, to minimize model complexity, a basic LSTM model was used. The results of this study showed the model had the best accuracy within the range of 300-500 epochs for the three conditions of hidden units (100, 150, and 200), followed by a gradually decreasing trend (Table 2, Figure 5). Hence, the increase in training time based on the increase in the two factors (numbers of epochs and hidden units) did not always yield a high accuracy. The results reveal that, whereas the overall model accuracy varied according to the number of hidden units, the highest accuracy was found at approximately 250-400 epochs. This result was compared with the results of previous studies with similar conditions (regarding motion classification using the LSTM algorithm). [PERSON] et al. (2020) collected the acceleration data of six motions including smartphone-based walking and running, then checked the accuracy of the deep-learning model after training and subsequent motion classification [16]. Two methods were used for the algorithm: a CNN alone and a CNN combined with LSTM (CNN-LSTM). The accuracy of the two models was compared, and the CNN model achieved a 94% accuracy at 200 epochs whereas the CNN-LSTM model achieved a 97% accuracy at 250 epochs. For the CNN-LSTM model, the accuracy was higher than the CNN model despite the need for an additional 50 epochs. In the study of [PERSON] et al. (2020), the participants were instructed to wear an inertial measurement unit (IMU) sensor and perform 14 motions in total [17]. During each motion, 18 datasets (including nine datasets of 3-axis acceleration, 3-axis gyroscope, and 3-axis magnetic field data) were obtained for use in deep learning. Moreover, the training was performed with 64 hidden units and 400 epochs. As a result, the loss was found to have adequately converged after 350 epochs, with a 94.73% accuracy, indicating adequate classification. In these previous studies, the accuracy was high in epoch ranges similar to those of the present study despite the variation in the number of hidden units and sizes of the total datasets. The risk of overfitting was also confirmed through training progression and confusion matrix (Figure 7, Tables 4 and 5). Although the increase in model complexity and training frequency caused by the increases in hidden units and epochs resulted in the improvement of accuracy, overfitting occurred after a certain point, reducing accuracy. Thus, a confusion matrix was constructed to examine the relationship of misclassifications to different motions. A greater number of errors were found in the training results in Table 5 in comparison with Table 4, despite the higher numbers of epochs and hidden units. This was attributed \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Lying** & **Running** & **Sitting on a Chair** & **Sitting on the Floor** & **Climbing Stairs** & **Chasing Each Other** & **Walking** \\ \hline Lying & 16,862 & - & - & - & 2 & 4 & - \\ \hline Running & - & 20,980 & - & 499 & 891 & 10 & 9 \\ \hline Sitting on a chair & - & 4 & 20,991 & - & - & - & 16 \\ \hline Sitting on the floor & - & - & - & 18,931 & 21 & - & 1 \\ \hline Climbing stairs & 47 & - & - & 268 & 16,569 & 2 & - \\ \hline Chasing each other & 4091 & 15 & - & 1302 & 3517 & 20,984 & 18 \\ \hline Walking & - & 1 & 9 & - & - & - & 20,956 \\ \hline Correct & 80.296\% & 99.999\% & 99.999\% & 90.148\% & 78.9\% & 99.999\% & 99.998\% \\ \hline Incorrect & 19.704\% & 0.001\% & 0.001\% & 9.852\% & 21.1\% & 0.1\% & 0.002\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Confusion matrix for 200 hidden units and 900 epochs.