## 4 Evaluation
### Results and Discussion  
Tables 3-5 show the results for each type of experiment. In the case of the architecture experiments (Table 3), adding more LSTM layers (A4) or more smaller, densely connected layers (A5) reduced the overall accuracy of the model no matter the number of predictions. The usage of bidirectional LSTMs (A6) was also detrimental. Generally, higher dropout regularization values (A2 and A3) produced better results. Overall, the A3 configuration offered the best results across the board. As can be seen in the table, the use of embeddings obtained better results when compared to the same configurations using one-hot vectors (A3 vs. A7, A2 vs. A8 and A4 vs. A9). A more semantically rich representation of the actions allows the same architecture to better model the user behaviour. The advantage of using embeddings is that the training process to obtain them is completely unsupervised, not requiring a significant overheat when comparing them against other representation strategies such as one-hot vectors. For the sequence-length experiments (Table 4), the best results were achieved using sequences with a length of 4 (S3) or 5 (A3) actions. In this specific case, the optimal sequence length was closely related with each deployment, being determined by the average action length of the activities in each scenario. This value should be adjusted in each specific case in order to achieve the best results, and it cannot be generalized. Finally for the time experiments (Table 5), the evaluation shows that none of the proposed options to take into account the timestamps (T1, T2 and T3) improved the results. When comparing these with an architecture with a similar configuration and no timestamps (A3), the results were clearly worse. Our initial intuition was that taking into account the timestamps would help to model the temporal patterns in the actions, but the results show that this information is not so relevant for the behaviour modelling task. We expect that the temporal data will be much more relevant in the activity recognition task, particularly when discriminating between activity patterns with similar actions that happen at different periods (preparing breakfast vs. preparing dinner). Figure 4: Architectures used in the time experiments. Each of the experiments used a different fusion strategy: T1 used late fusion, T2 used early fusion and T3 used slow fusion. \begin{table}
\begin{tabular}{l c c c c c} \hline
**ID** & **acc\_at\_1** & **acc\_at\_2** & **acc\_at\_3** & **acc\_at\_4** & **acc\_at\_5** \\ \hline A3 & **0.4744** & 0.6282 & 0.7179 & 0.7905 & **0.8589** \\ S1 & 0.4553 & 0.5957 & 0.7021 & 0.8 & 0.8553 \\ S2 & 0.4255 & 0.6255 & 0.7021 & **0.8085** & 0.8382 \\ S3 & 0.4658 & **0.6452** & **0.7264** & 0.7948 & 0.8504 \\ S4 & 0.4700 & 0.6196 & 0.6965 & 0.7692 & 0.8461 \\ S5 & 0.4592 & 0.6351 & 0.7210 & 0.7896 & 0.8369 \\ S6 & 0.4192 & 0.5589 & 0.6593 & 0.7554 & 0.8122 \\ \hline \end{tabular}
\end{table} 
Table 4: Sequence-length experiments: Accuracy for different number of predictions. \begin{table}
\begin{tabular}{l c c c c} \hline
**ID** & **acc\_at\_1** & **acc\_at\_2** & **acc\_at\_3** & **acc\_at\_4** & **acc\_at\_5** \\ \hline A3 & **0.4744** & **0.6282** & **0.7179** & **0.7905** & **0.8589** \\ T1 & 0.4487 & 0.6239 & 0.7094 & 0.7692 & 0.8076 \\ T2 & 0.4487 & 0.6111 & 0.7008 & 0.7692 & 0.8247 \\ T3 & 0.3846 & 0.5940 & 0.7051 & 0.7564 & 0.8076 \\ \hline \end{tabular}
\end{table} 
Table 5: Time experiments: Accuracy for different number of predictions. The A3 experiment depicted in the first row did not use any time information. Figure 5: Best results in all the experiments.