# Predicting Activities of Daily Living with Spatio-Temporal Information
## 6 Results and Discussion
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Test Sets**} & \multicolumn{3}{c}{**Recognition Accuracy (\%)**} \\ \cline{2-4}  & **MIT PlaceLab Dataset** & **van Kasteren Dataset** & **Aruba Dataset** \\ \hline
1 st Set & 88.07 & 92.82 & 96.79 \\
2 nd Set & 92.78 & 71.94 & 75.24 \\
3 rd Set & 97.18 & 98.32 & 79.94 \\
4 th Set & 94.63 & 92.67 & 84.17 \\
5 th Set & 96.31 & 96.69 & – \\
6 th Set & 100 & 90.03 & – \\
7 th Set & 98 & – & – \\
8 th Set & 96.24 & – & – \\ \hline
**Average** & **95.40** & **90.41** & **84.04** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recognition performance in predicting user location based on time for each test set. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Test Sets**} & \multicolumn{3}{c}{**Recognition Accuracy (\%)**} \\ \cline{2-4}  & **MIT PlaceLab Dataset** & **van Kasteren Dataset** & **Aruba Dataset** \\ \hline
1 st Set & 99.39 & 100 & 100 \\
2 nd Set & 100 & 100 & 95.57 \\
3 rd Set & 100 & 100 & 100 \\
4 th Set & 99.17 & 100 & 100 \\
5 th Set & 99.59 & 99.17 & – \\
6 th Set & 100 & 100 & – \\
7 th Set & 100 & – & – \\
8 th Set & 100 & – & – \\ \hline
**Average** & **99.77** & **99.86** & **98.89** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Recognition performance in predicting user activity based on location for each test set. The third experiment was conducted to determine the amount of training data required to train the PPM. Various training-test splits were examined. The results presented in Figure 4 showed that the size of training data does have an impact on recognition performance. Such results are expected since compression is more effective when patterns are repeated frequently. When trained on 4 days, an average accuracy of 77.48% is achieved on MIT PlaceLab, 52.50% on [PERSON] dataset and 60.49% on Aruba dataset for \(time\to location\) prediction. A lower accuracy was observed in van [PERSON] dataset. This was due to variations in the time when the user performed the activities. Such variations were not repeated frequent enough for PPM to learn the representations. For \((time,location)\to activity\) prediction, an average accuracy of 52.85% is achieved on MIT PlaceLab, 51.17% on [PERSON] dataset and 57.15% on Aruba dataset. However, for both \(time\to location\) and \((time,location)\to activity\) predictions, the average accuracy increases when trained with more training data, as shown in the first and third grouped bar chart in Figure 4. For \(location\to activity\) prediction, our method achieved a high recognition accuracy across the three datasets. A consistent performance of more than 91% average recognition accuracy was observed across the different training-tests splits. The results showed that the size of training data does not have much impact on \(location\to activity\) prediction. The high recognition performance achieved in this experiment (and also in the first experiment) showed that knowing which room that the user is in provides a better inference of what activity the user might be involved in. The fourth experiment evaluates the time it takes to train the PPM model and the time to predict from the trained PPM. Table 7 shows the computational performance in terms of (a) training time (in minutes) and (b) prediction time (in seconds). The value in parentheses shows the number of user-interaction instances in each respective training/test set. The training time across the number of instances presented in Figure 5 clearly show that the training time grows approximately linearly with the number of instances. As for the prediction time, it takes 0.1216 s to predict 1377 instances on the second test set of Aruba dataset (see Table 7b), which has the highest number of instances among all the test sets. The prediction time reduces when test on a smaller number of instances.