# An Adaptive UI Based on User-Satisfaction Prediction in Mixed Reality
## 4 Experimental Results and Discussion
### Experiment for Implementation of Proposed Method  
In this section, we describe the experiment to develop the proposed method. As discussed in Section 2.1, the following can be used as interaction information: gaze, hand, head, and object. In order to use this information for user-satisfaction prediction, it is required to confirm the correlation between each piece of information and user satisfaction. For confirmation of the correlation between interaction information and user satisfaction, 26 subjects in their twenties to thirties were recruited [29]. Of the recruited subjects, 17 were male, and 9 were female. In addition, 8 of the subjects had used MR devices more than 10 times, 11 of the subjects had used MR devices less than 10 times, and 7 of the subjects had no experience using MR devices at all. In the experiment, six selecting tasks and six moving tasks were given to each subject, so that one subject performed a total of 48 interactions. During the experiment, the following information was measured: gaze information, hand information, head information, object information, task time, and user satisfaction. 1. Gaze information (3D point): composed to origin, direction, hit normal and hit position of gaze. 2. Hand information (3D point): composed to position and rotation of 25 keypoints of right hand. 3. Head information (3D point): composed to position and rotation of head. 4. Object information (3D point): composed to position and rotation of object. 5. Time (sec): time between the moment when virtual object is generated and the moment when interaction is completed. 6. User satisfaction (Likert 5 scale): satisfaction scores rated by users for performing interactions. Gaze, hand, head, object information and time were measured in real time during the experiment, and user satisfaction was directly input from the user. In order to analyze the correlation between the measured interaction information and the user satisfaction, it is required to convert the measured interaction information into a form comparable to the user satisfaction through the process shown in Figure 8.  
At first, the feature vector was extracted by calculating the average, top 10%, and bottom 10% of the raw data, then the extracted feature vector was used as the input of the regression method. Then, as shown in Figure 8, support vector regression (SVR), which had been extended from support vector machine (SVM) to solve multivariate regression problems, was used as the regression model [30]. Because SVR is useful in solving regression problems with high-dimensional features, it has already been used in various fields such as time-series prediction [30; 31; 32], and recently, it was used as a method for predicting user satisfaction in various conventional studies [33; 34; 35]. For the implementation of the SVR model, the kernel function was set to linear and the cost was set to 1.0, and for the training of the implemented SVR model, the measured data were divided into the train set (80%)  
Figure 8: Process of converting interaction information to user satisfaction using SVR. and test set (20%). Table 1 shows the results of analyzing the correlation between user satisfaction and the measured interaction information by using trained SVR. From the correlation analysis, it was found that the highest Pearson correlation coefficient was achieved when all the interaction information was used. Therefore, all four elements of interaction information were used for implementing the proposed method. Results of Table 1 were obtained by using the data measured from the start of the interaction to the end of the interaction. To predict user satisfaction, it is necessary to use only the data measured at the initial range. To specify the initial range, the average interaction time measured during the experiment was analyzed as shown in Figure 9.  
As a result of the analysis, the subjects consumed an average of 2.484 s for the selecting task and 4.267 s for the moving task on average.