## 4 Comparative Evaluation of User Interaction in Wearable AR
### Experimental Results  
First, for the analysis of task performance depending on visual contexts, an ANOVA was performed for three visual augmentations. Note that most of the data satisfied ANOVA assumptions. However, some of them did not satisfy ANOVA assumptions (the assumption of homogeneity of variances) due to the limited number of experimental data. For this reason, we used [PERSON]'s ANOVA tests rather than general ANOVA tests through the study [46]. As shown in Figure 15, there was a significant difference (\(p<0.05\)) for the 3D matching task, which is complicated and challenging. On the other hand, there was no statistical difference in the dragging and dropping task, which is rather a simple task. The non-stereo rendering and indirect augmentation with video background outperformed the stereo rendering and direct augmentation. The main reason for these results is that the 3D matching task requires much attention and sophisticated manipulation of the control object, so the FOV played an essential role in task performance. On the other hand, the FOV did not influence the performance of the dragging and dropping task very significantly. The qualitative analysis was conducted based on the validated ten questions from the SUS questionnaire [45]. Summarized ten questions in the SUS questionnaire include (1) frequent system use, (2) unnecessarily complex, (3) easy to use, (4) need technical support, (5) functions well integrated, (6) too much inconsistency, (7) easy to learn, (8) very cumbersome, (9) confident using the system, and (10) need to learn many things beforehand. However, original questions w  
Figure 14: 3D (three-dimensional) matching task between a virtual control object and a physical target object; the gripper of the UR3 robot is used for 3D matching. Figure 15: Task completion time (s) of user interaction for visual contexts. were given to the subject and used for the questionnaire evaluation. Readers are referred to see the questions in Reference [45]. As shown in Figure 16, there were statistical differences in all questions except Q2 and Q4. Overall, the non-stereo rendering and indirect augmentation with video background showed better qualitative evaluation than the stereo rendering and direct augmentation and the non-stereo rendering and indirect augmentation without video background. The non-stereo rendering and indirect augmentation without video background showed the worst usability evaluation. For the post hoc analysis concerning the visual context, in Q1 and Q9, there was no difference between the stereo rendering and direct augmentation and the non-stereo rendering and indirect augmentation, but there was a statistical difference between the non-stereo rendering and indirect augmentation without video background and the others concerning the visual context (\(p<0.05\)). In particular, in Q3 and Q8, there was a significant difference between the three methods. The non-stereo rendering and indirect augmentation showed better easy-to-use and less cumbersome-to-use ratings. In Q6, Q7, and Q10, there was no difference between the non-stereo rendering and indirect augmentation and the stereo rendering and direct augmentation. We found that, although the stereo rendering and direct augmentation could match the virtual object with the real object and provide immersive visualization, its task performance was worse than the non-stereo rendering and indirect augmentation due to the narrower FOV. We found that the amount of information provided to the subject was a more influential factor than FOV in currently available AR smart glasses, although some mismatch problems did exist. Nevertheless, all the subjects agreed that visual augmentation using AR smart glasses is a promising tool for task assistance. For the quantitative analysis, according to interaction gestures in wearable AR, a \(t\)-test was performed for task completion time (comparison between two methods).