# A Personalised Emotion-Based Model for Relaxation in Virtual Reality
## Experimental Evaluation
### Results
#### 5.2.2 Adaptation Order
This could be explained by the fact that this user has many restrictions which resulted in only two effective adaptation. These same applies for user \(3_{out}\) which has only one effective adaptation. In Table 5 the 24 permutations of the adaptions are listed with the average relaxation over every user. This list is also sorted according to this relaxation metric. Sequences 1, 2, 3 and 5 have in common that the first two adaptation are dog (\(dg\)) and natural light (\(nl\)) while the last two adaptation in the sequence are music (\(ms\)) and fire (\(py\)) which is not the case for any of the other sequences. The highlighted row in Table 5 indicates the sequence which is used by the heuristic optimiser. Indeed this sequence is not the optimal order. Now we can consider the indoor environments in Figure 13 for which Table 6 shows some of the most interesting sequences. These are also indicated on the plots. The order of the adaptations  
Figure 13: The average relaxation over all simulations plotted for every permutation of adaptations for the indoor environment. The adaptation sequences are sorted according to decreasing final and average relaxation. does have an impact on the final relaxation. Certain permutations are sub-optimal in the sense that they do not guarantee maximal relaxation. Furthermore, the order also has an impact on the average relaxation per user and the average relaxation overall. Interestingly, the same three groups of users can be identified for the indoor environments as for the outdoor. Namely, users \(1_{in}\) and \(5_{in}\) achieve below average relaxation for every permutation, users \(2_{in}\) and \(3_{in}\) achieve above-average relaxation, while users \(4_{in}\) and \(6_{in}\) perform on average. Figure 13 and Table 6 indicate that from sequence 360 to 361 a big decrease in final and average relaxation occurs. The table shows that every sequence before 361 has the dog (\(dg\)) adaptation before the controlled breathing (\(cb\)). Next, out of all 720 sequences, exactly 72 achieve maximal final relaxation for all users. These 72 sequences turn out not to be random. As indicated earlier, in all 72 sequences \(dg\) occurs before \(cb\). For 60 of the 72 best sequences, the sequence ends with artificial light (\(al\)). The other 12 sequences end with the \(al\) adaptation immediately followed by the light colour (\(lc\)) adaptation. When inspecting the plots, it can be observed that for all users, except \(3_{in}\) and \(4_{in}\), the optimal sequence is within the first 72 sequences. Users \(3_{in}\) and \(4_{in}\) have the combination of a low optimal \(al\) and \(dg\) intensity. A low optimal \(dg\) intensity deactivates the \(dg\) adaptation, and thus the occurrence of \(dg\) before \(cb\) does not make a difference. Furthermore, the absence of the \(dg\) adaptation means that fear and stress need to be decreased by another adaptation before activating \(cb\), i.e., by \(nl\) and \(al\). The highlighted row in Table 6 indicates the sequence which is used by the heuristic optimiser. As for the outdoor environment this sequence is also not the optimal order in this case.