# Predicting Activities of Daily Living with Spatio-Temporal Information
## 6 Results and Discussion  
The first experiment was conducted using the first-order model for prediction. The main purpose is to test the model's ability in predicting user location given the time of the day (\(time\to location\)) and predicting user activity given the location of the user (\(location\to activity\)). The recognition accuracy is calculated based on the number of times the model correctly makes the prediction. Table 4 shows the recognition results for (\(time\to location\)) prediction. Our method achieved an average recognition of 95.40% on MIT PlaceLab dataset, 90.41% on van Kasteren dataset and 84.04% on Aruba dataset when predicting user location based on time. Referring to Table 4, a low recognition accuracy of 71.94% was observed in the 2 nd set of the van Kasteren dataset. In this test set, the user came home early around 1 pm, which was the only time and day that this was observed in the entire dataset. The PPM model is not able to make prediction since this event was not learned during training. Although the Aruba dataset has the lowest average recognition performance, only 18 days out of 27 days data were used for training, which is the lowest training:testing ratio as compared to the other two smart-home datasets. \begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{2}{c}{**Number of Days**} \\ \hline
**Split** & **Training Set** & **Test Set** \\ \hline \multicolumn{3}{l}{(a) MIT PlaceLab Dataset} \\ \hline
1 & 12 days & 4 days \\
2 & 8 days & 8 days \\
3 & 4 days & 12 days \\ \hline \multicolumn{3}{l}{(b) van Kasteren Dataset} \\ \hline
1 & 16 days & 8 days \\
2 & 12 days & 12 days \\
3 & 8 days & 16 days \\
4 & 4 days & 20 days \\ \hline \multicolumn{3}{l}{(c) Aruba Dataset} \\ \hline
1 & 16 days & 11 days \\
2 & 12 days & 15 days \\
3 & 8 days & 19 days \\
4 & 4 days & 23 days \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Different training splits on (a) MIT PlaceLab, (b) van Kasteren and (c) Aruba datasets. Table 5 shows the recognition results in predicting user activity given location. A high average recognition accuracy was observed when predicting user activity based on location across all the datasets (i.e., 99.77% on MIT PlaceLab, 99.86% on van Kasteren and 98.89% on Aruba). This was expected since there are certain locations in the home where an activity usually takes place. For example, showering usually occurs in the bathroom, while cooking usually occurs in the kitchen. The results from this experiment showed that location provides important context for inferring user activity. The results from experiment 1 showed that our approach is effective in predicting (_time_\(\rightarrow\)_location_) and (_location_\(\rightarrow\)_activity_) with more than 84% average recognition performance for (_time_\(\rightarrow\)_location_) and more than 98% average recognition performance for (_location_\(\rightarrow\)_activity_). The second experiment tests the model's ability to predict user activity given the time and location ((_time_,_location_) \(\rightarrow\)_activity_). This experiment used the second-order model for prediction. The results are shown in Table 6. An average accuracy of 81.74% is achieved on MIT PlaceLab dataset, 88.14% on van Kasteren dataset and 81.05% on Aruba dataset. A lower recognition accuracy was observed in this experiment compared to the first experiment. This was due to variations in the activities performed on different time of the day. Although the accuracy was lower compared to the first experiment, our method still able to achieve an average recognition of more than 81% on all the three datasets. The 1 st test set of MIT PlaceLab dataset has a low recognition accuracy of 63%. This was due to the variations in the way that the user performed his activity in this test set. For example, the second-order model learned that the user will be in the kitchen between 9.30 a.m. and 10 a.m. to 'prepare meal'. In this particular test set, the user was 'doi