# An Adaptive UI Based on User-Satisfaction Prediction in Mixed Reality
## 4 Experimental Results and Discussion
### Experiment for Implementation of Proposed Method
In order to successfully predict user satisfaction in as many tasks as possible, the initial range should be set so that the prediction can be performed correctly even when performing a shorter time-consuming selection task. The average consumption time of the selection task was 2.484 s, but the consumption time of the fastest selection task was 0.85 s, so the initial range was set to 10 frames and 20 frames, which is shorter than 0.85 s (25 frames at 30 fps). In order to evaluate prediction \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & **Gaze** & **Head** & **Hand** & **Object** & **Pearson** \\ \hline
1 & \(\bullet\) & & & & & 0.33 \\
2 & & \(\bullet\) & & & & 0.46 \\
3 & & & \(\bullet\) & & & 0.23 \\
4 & & & & & \(\bullet\) & 0.05 \\
5 & \(\bullet\) & \(\bullet\) & & & & 0.51 \\
6 & \(\bullet\) & & & \(\bullet\) & & 0.11 \\
7 & \(\bullet\) & & & & \(\bullet\) & 0.23 \\
8 & & & \(\bullet\) & & & 0.54 \\
9 & & & \(\bullet\) & & \(\bullet\) & 0.45 \\
10 & & & \(\bullet\) & & \(\bullet\) & 0.21 \\
11 & \(\bullet\) & \(\bullet\) & & \(\bullet\) & & 0.55 \\
12 & \(\bullet\) & \(\bullet\) & & & \(\bullet\) & 0.53 \\
13 & \(\bullet\) & & \(\bullet\) & & \(\bullet\) & 0.21 \\
14 & & & \(\bullet\) & \(\bullet\) & & 0.43 \\
15 & \(\bullet\) & & \(\bullet\) & & \(\bullet\) & 0.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Correlation between each measured interaction-information combination and user satisfaction. Figure 9: Interaction time measured during experiment. performance of SVR model using the set initial frame, the mean absolute error (MAE) expressed as Equation (1) was used. \[\text{MAE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\lvert P_{i}-A_{i}\rvert} \tag{1}\]  
where, \(P_{i}\) is \(i\)-th predicted user satisfaction; \(A_{i}\) is \(i\)-th ground truth user satisfaction; \(n\) is the total number of predictions. Table 2 shows the results of user-satisfaction prediction using the initial frame, and the SVR model. As a result of Table 2, it was confirmed that when the initial frame was set to 10 frames (MAE = 0.654), the prediction performance was improved than when the initial frame was set to 20 frames (MAE = 0.691). To evaluate the suitability of this SVR model, the multi-layer perceptron (MLP) model was additionally designed [31; 36]. Like SVR, MLP is being used to solve various problems in various fields, and recently, it was also used in research to predict satisfaction [35; 37]. Data for the initial frames (10 frames and 20 frames) were input into the input layer of the MLP model as in the SVR model, and the predicted user satisfaction was outputted to the output layer. In addition, the hidden layer included two hidden layers composed of 32 neurons and one hidden layer composed of 8 neurons [38; 39], and swish [40] was used as an activation function to prevent the vanishing-gradient problem from occurring. For the optimal MLP model, user-satisfaction prediction was performed by adjusting the epoch (1\(\sim\)30) and batch size (2, 4, 8, 16, 32), and the satisfaction prediction results of the top 3 models are shown in Table 3.  
In Table 3, the MLP model with the best performance was set to use 10 frames and trained with 24 epochs and a batch size of 4, and the MAE of this model (MAE = 0.531) was reduced by 0.123 compared to the SVR model (MAE = 0.654). Therefore, in the proposed method, the MLP model, which was confirmed to have improved performance over the SVR model, was selected as the user-satisfaction-prediction method. To provide an adaptive UI based on this user-satisfaction-prediction result, correction is performed based on object information among input information in the proposed method. Before the object-information-based correction, user satisfaction according to object information must be analyzed. For this, as shown in Figure 10, eight object-generation positions were classified into a total of six type, and the result of the user-satisfaction analysis based on this classification is shown in Figure 11.