# Activity Prediction Based on Deep Learning Techniques
## 2 Materials
### Analysis of PM\({}_{2.5}\) Exposure and Activity Pattern  
As described previously, we know that our daily activities are accompanied by fine dust particles with a diameter of less than 2.5 um, which are hazardous to our health, especially for the elderly and children [31]. To investigate the relationship between the distribution of PM\({}_{2.5}\) and each activity, we investigated the average sojourn time of each subject's activity and the corresponding distribution of PM\({}_{2.5}\). Table 2 shows the number of activities performed by each subject during a day and the proportion (%) of corresponding activities (mean \(\pm\) SD) that the subject spent for a day. The table shows that people spend more time in indoor-like environments than outdoor-like ones, indicating that fine dust distribution in indoor-like environments is more important to our health and requires careful study. Several activities were not observed at all, and the corresponding cases were omitted in Table 1. Table 3 shows the percentiles of PM\({}_{2.5}\) concentrations per each activity. It shows clearly that PM\({}_{2.5}\) concentration is high in indoor-like environments compared to outdoor environments. These tables imply that personalized environmental warning technologies are necessary to provide customized per-person environmental information. Machine-learning-based techniques can provide viable solutions for customized environmental services by predicting the next human activities properly.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{ID} & \multicolumn{2}{c}{**Commuting with a Bus**} & \multicolumn{4}{c}{**Commuting with a Car**} & \multicolumn{4}{c}{**Commuting with a Subway**} & \multicolumn{4}{c}{**S staying inside Home**} & \multicolumn{2}{c}{**S staying inside Work Place**} \\ \cline{2-11}  & **N (day)** & **Mean \(\pm\) SD** & **N (day)** & **N (day)** & **Mean \(\pm\) SD** & **N (day)** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **N (day)** & **Mean \(\pm\) SD** \\ \hline ID\_01 & 5 & 72.0 \(\pm\) 93.3 & 4 & 8 & 502.0 \(\pm\) 215.0 & 3 & 96.7 \(\pm\) 42.4 & 20.0 \(\pm\) 19.0 & 1 & 20.0 \(\pm\) NA \\ ID\_02 & 12 & 40.3 \(\pm\) 23.6 & 4 & 14 & 581.0 \(\pm\) 167.0 & 5 & 368.0 \(\pm\) 228 & 19.0 \(\pm\) 47.6 & 11 & 35.5 \(\pm\) 19.1 \\ ID\_03 & 1 & 6.0 \(\pm\) NA & 1 & 14 & 639.0 \(\pm\) 110.0 & 6 & 296.0 \(\pm\) 159.0 & 0.0 \(\pm\) NA & 3 & 98.0 \(\pm\) 68.4 \\ ID\_04 & 6 & 26.0 \(\pm\) 37.5 & 4 & 14 & 635.0 \(\pm\) 159.0 & 6 & 313.0 \(\pm\) 138.0 & 72.5 \(\pm\) 91.1 & 4 & 30.5 \(\pm\) 14.0 \\ Total & 15 & 69.1 \(\pm\) 58.0 & 10 & 19 & 672.0 \(\pm\) 98.8 & 7 & 433.0 \(\pm\) 221.0 & 48.4 \(\pm\) 57.7 & 14 & 55.4 \(\pm\) 52.6 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{ID} & \multicolumn{2}{c}{**Staying Outside**} & \multicolumn{4}{c}{**Visiting Other Commercial Place**} & \multicolumn{4}{c}{**Visiting Restaurant-Cafe**} & \multicolumn{2}{c}{**Walking at Outside**} \\ \cline{2-11}  & **N (day)** & **Mean \(\pm\) SD** & **N (day)** & **Mean \(\pm\) SD** & **N (day)** & **Mean \(\pm\) SD** & **N (day)** & **Mean \(\pm\) SD** \\ \hline \hline \multirow{4}{*}{ID} & 1 & 2.0 \(\pm\) NA & 7 & 222.0 \(\pm\) 177.0 & 6 & 58.3 \(\pm\) 49.9 & 8 & 80.2 \(\pm\) 40.5 \\  & 2 & 53.0 \(\pm\) 66.5 & 7 & 42.0 \(\pm\) 59.6 & 10 & 408.0 \(\pm\) 311 & 13 & 78.3 \(\pm\) 50.7 \\ \cline{1-1}  & 1 & 144.0 \(\pm\) NA & 8 & 87.8 \(\pm\) 46.0 & 6 & 35.0 \(\pm\) 30.7 & 11 & 77.6 \(\pm\) 51.0 \\ \cline{1-1}  & 2 & 135.0 \(\pm\) 191.0 & 12 & 174.0 \(\pm\) 107.0 & 11 & 167.0 \(\pm\) 147.0 & 14 & 63.0 \(\pm\) 48.5 \\ \cline{1-1}  &