# Activity Prediction Based on Deep Learning Techniques
## 5 Conclusions  
This paper strived to infer activity patterns in both indoor and outdoor environments using environmental information, which distinguishes it from most previous studies that focus on the levels of PM\({}_{2.5}\) concentration affecting human health. We used a commercial multipurpose sensor to collect the raw data and designed deep learning models to infer the activity patterns using the collected raw data. We chose both MLP and LSTM network models for this research. MLP, a popular model in the 1980s, has recently gained interest again today due to the success of deep learning techniques in various applications, including speech recognition, image recognition, machine translation, etc. LSTM is a deep learning model that characterizes itself to handle a time-series type of data and has proved its characteristic adaptability for various applications such as voice recognition, stock index prediction, weather forecast, etc. During the performance comparison, we found that LSTM outperformed MLP in terms of prediction accuracy, which was expected considering the nature of the LSTM. More specifically, the accuracy was higher in the indoor-like environments than in the outdoor-like environments in both training and test simulations, except for the case of indoor cooking activities. Moreover, considering that all four features used for this research could be unstable under outdoor environments compared to indoor environments, we believe that the test accuracy of around 90% is very high. However, LSTM took significant amounts of computation time compared to MLP due to the complexity of network architecture, especially when the number of hidden layers is more than two. Therefore, we aimed to reduce the number of hidden layers to make the model more practical. In the current study, we acknowledge the lack of large differences in lifestyles of subjects for various reasons, which could have led to degradation of performance. This issue will be included in our next research. Additionally, we will try to apply our model to more diverse heterogeneous environments based on the results of this research and enhance the performance of the current models. For this purpose, it is worth building a conceptual model as in [21] and collecting datasets corresponding to the model for more precise activity prediction. In addition, one of the ways to enhance the performance of the models is to incorporate other deep learning architectures, such as CNNs, into current LSTM architectures. Certain parts of the time series data show very frequent local and temporal changes of movements, which may be suitable for CNN to capture. If CNN is used together with LSTM, the current performance can be further improved. Conceptualization, J.P. and S.K. ; methodology, J.P. and S.K. ; software, J.P.; validation, J.P., C.S., M.K. and S.K. ; data analysis, J.P., C.S., M.K. and S.K. ; resources, J.P., C.S., M.K. and S.K. ; data curation, J.P. and S.K. ; writing--review and editing, J.P. and S.K. ; visualization, J.P.; project administration, S.K. ; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript. This study was funded by Environmental Health Research Center Project (2016001360002) by the Korea Environmental Industry and Technology Institute, Ministry of Environment, South Korea. Not applicable. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**MLP**} \\ \hline  & \multicolumn{2}{c}{**Training Accuracy**} & \multicolumn{2}{c}{**Test Accuracy**} & \multicolumn{2}{c}{**Training Accuracy**} & \multicolumn{1}{c}{**Test Accuracy**} \\ \hline  & Case 1 & 0.99 & 0.9 & Case 1 & 0.97 & 0.81 \\ ID\_3 & Case 2 & 0.99 & 0.78 & Case 2 & 0.98 & 0.78 \\  & Case 3 & 0.99 & 0.74 & Case 3 & 0.98 & 0.73 \\ \hline  & Case 1 & 0.99 & 0.9 & Case 1 & 0.97 & 0.81 \\ ID\_4 & Case 2 & 0.99 & 0.78 & Case 2 & 0.98 & 0.78 \\  & Case 3 & 0.99 & 0.74 & Case 3 & 0.98 & 0.73 \\ \hline \hline \end{tabular}
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