## 8 Conclusions  
As many AR smart glasses are currently available [6], it is essential to make visual augmentation more natural and intuitive in terms of visual contexts and gesture interaction. However, there has been little research work on investigating this issue in depth. In this study, user interactions based on visual contexts and gesture interactions were classified and evaluated using AR smart glasses. We performed quantitative and qualitative analyses, including performance measurement and questionnaire evaluation. The results verified that there was a significant difference in task performance according to visual contexts and gesture interactions as the task complexity increased. They also showed that both FOV and visual registration between virtual and physical artifacts were important.  
We have analyzed not only the interaction between virtual objects but also the interaction between physical and virtual objects. Depending on the role of the control and target objects for the 3D matching task, there was a difference in task performance. The reason for this result is related to whether the control object with which the user interacts is virtual or real.  
We have also found that both the FOV and the gesture interaction influence task performance, and they can complement each other. However, currently available smart glasses have narrower FOV than is necessary, and the visual matching assistance is not sufficient for use in real-world tasks. Although interviews showed that interaction with hand gestures was more natural and effective, it was uncomfortable to wear another sensor for detecting hand gestures. Nevertheless, we have demonstrated the potential utilization of AR smart glasses. To overcome still existing limitations on the visual context and gesture interaction, it is necessary to provide a dynamic visual layout concerning narrow FOV and to support hybrid user interfaces for more accurate and natural interaction.  
Actual manufacturing systems may have different influential factors compared with the controlled laboratory, although a real use case study was performed qualitatively using industrial workers. Therefore, further study is required to analyze the visual context and interaction methods in different real manufacturing environments. For this reason, we plan to apply the proposed approach to a smart factory testbed that produces motors and to evaluate its performance quantitatively and qualitatively. Secondly, it is necessary to evaluate the relation between gesture interactions and visual contexts using different AR smart glasses. Thirdly, we need to improve the accuracy of the hand gesture and visual registration in wearable AR. Fourthly, we need further investigation of different types of AR smart glasses for analyzing visual contexts and gesture interactions concerning hardware specification and price. Finally, it would be valuable for the visualization and interaction methods to be extended to adopt some of the challenges described previously.  
Conceptualization, M.K. and J.Y.L.; methodology, M.K., S.H.C., K.-B.P., and J.Y.L.; formal analysis, M.K. and J.Y.L.; investigation, S.H.C., K.-B.P., and J.Y.L.; software, M.K., S.H.C., and K.-B.P.; validation, M.K. and J.Y.L.; supervision, J.Y.L.; writing-review and editing, J.Y.L.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Education (NRF-2016R1D1A1B03934697 and 2019R1I1A3A01059082).  
We thank [PERSON] for the experimental setup.  
The authors declare no conflict of interest.