From Industry to Practice: Can Users Tackle Domain Tasks with Augmented Reality?

Doctoral thesis, 2023

Augmented Reality (AR) is a cutting-edge interactive technology. While Virtual Reality (VR) is based on completely virtual and immersive environments, AR superimposes virtual objects onto the real world. The value of AR has been demonstrated and applied within numerous industrial application areas due to its capability of providing interactive interfaces of visualized digital content. AR can provide functional tools that support users in undertaking domain-related tasks, especially facilitating them in data visualization and interaction by jointly augmenting physical space and user perception. Making effective use of the advantages of AR, especially the ability which augment human vision to help users perform different domain-related tasks is the central part of my PhD research.

Industrial process tomography (IPT), as a non-intrusive and commonly-used imaging technique, has been effectively harnessed in many manufacturing components for inspections, monitoring, product quality control, and safety issues. IPT underpins and facilitates the extraction of qualitative and quantitative data regarding the related industrial processes, which is usually visualized in various ways for users to understand its nature, measure the critical process characteristics, and implement process control in a complete feedback network. The adoption of AR in benefiting IPT and its related fields is currently still scarce, resulting in a gap between AR technique and industrial applications. This thesis establishes a bridge between AR practitioners and IPT users by accomplishing four stages. First of these is a need-finding study of how IPT users can harness AR technique was developed. Second, a conceptualized AR framework, together with the implemented mobile AR application developed in an optical see-through (OST) head-mounted display (HMD) was proposed. Third, the complete approach for IPT users interacting with tomographic visualizations as well as the user study was investigated.

Based on the shared technologies from industry, we propose and examine an AR approach for visual search tasks providing visual hints, audio hints, and gaze-assisted instant post-task feedback as the fourth stage. The target case was a book-searching task, in which we aimed to explore the effect of the hints and the feedback with two hypotheses: that both visual and audio hints can positively affect AR search tasks whilst the combination outperforms the individuals; that instant post-task feedback can positively affect AR search tasks. The proof-of-concept was demonstrated by an AR app in an HMD with a two-stage user evaluation. The first one was a pilot study (n=8) where the impact of the visual hint in benefiting search task performance was identified. The second was a comprehensive user study (n=96) consisting of two sub-studies, Study I (n=48) and Study II (n=48). Following quantitative and qualitative analysis, our results partially verified the first hypothesis and completely verified the second, enabling us to conclude that the synthesis of visual and audio hints conditionally improves AR search task efficiency when coupled with task feedback.