# Activity Prediction Based on Deep Learning Techniques
## 4 Results and Discussion
### Performance Evaluation
Overall, the training experiment had higher ratios compared to the test experiment. In the training data experiment, most activity patterns were correctly estimated, and activities involved with indoor environments had relatively higher ratios compared to outdoor activity cases. As shown in Figure 9, the ratios of correct predictions for each activity pattern are almost 1, indicating nearly 100% predictive accuracy. However, the test cases showed slightly lower ratios compared to the training cases. Among the test scenarios, activities associated with indoor-like environments, such as \"Staying inside work place\", \"Visiting other commercial place\", and \"visiting restaurants, cafe\" showed higher ratios compared to those of outdoor-like environments, except \"Home-BBQ\" and \"Pan-Frying\". In general, activities such as indoor cooking are the main cause of household air pollution and a leading environmental risk factor [35]. Sometimes, the PM2.5 concentration level of the cooking activities has a spiky distribution, resembling outliers, which could explain the corresponding lower accuracy in cooking activities. There were no data corresponding to Commuting with a train, Home-SHS, so there were no bars for these activities. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Batch Size**} & \multicolumn{2}{c}{**Training Accuracy**} & \multicolumn{2}{c}{**Test Accuracy**} \\ \cline{2-5}  & **MLP** & **LSTM** & **MLP** & **LSTM** \\ \hline
32 & 0.97 & 0.99 & 0.97 & 0.98 \\
64 & 0.97 & 0.98 & 0.96 & 0.98 \\
128 & 0.96 & 0.98 & 0.96 & 0.98 \\
256 & 0.95 & 0.98 & 0.95 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Training and test accuracy of ID_03. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Batch Size**} & \multicolumn{2}{c}{**Training Accuracy**} & \multicolumn{2}{c}{**Test Accuracy**} \\ \cline{2-5}  & **MLP** & **LSTM** & **MLP** & **LSTM** \\ \hline
32 & 0.97 & 0.98 & 0.97 & 0.97 \\
64 & 0.96 & 0.97 & 0.97 & 0.97 \\
128 & 0.95 & 0.97 & 0.97 & 0.97 \\
256 & 0.91 & 0.97 & 0.77 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Training and test accuracy of ID_04. As previously stated, our results clearly demonstrate that the predictive accuracy of our models can be improved if they are applied to homogeneous environmental settings. Additionally, we can further improve the performance of our current model if we elaborate more on behavior modeling as proposed by authors in [20] to predict the subject's next actions and collect data corresponding to the behavior model. The authors viewed human behaviors as a large collection of actions, activities, intra-activity behaviors, and inter-activity behaviors. Actions are defined as the simplest concepts of conscious muscular movements made by the subject. Activities are composed of several actions (e.g., taking a shower, watching a movie, etc.). The intra-activity behaviors describe how the subject performs a single activity at different times. The inter-activity behavior describes the chain of the subject's different activities. This issue will be included in our future studies. One noticeable observation from the experiment is that the temperature and relative humidity did not play a significant role in the performance improvement, as shown in Table 10, presumably because the two features did not change significantly during the observation period. Table 10 highlights the change in the test accuracy of both models as more features were added.