Ensuring Safety in Augmented Reality from Trade-off Between Immersion and Situation Awareness

Jung, Jaeho; Lee, Hyeopwoo; Choi, Jee‐Hye; Nanda, Abhilasha; Gruenefeld, Uwe; Stratmann, Tim Claudius; Heuten, Wilko

doi:10.1109/ismar.2018.00032

Cited by 26 publications

(9 citation statements)

References 38 publications

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“…In order to vary object distribution and achieve higher tracking fidelity of the objects in the study, we use VR to simulate the realworld object space and AR screen. Simulating an AR environment with VR is a common method to create a controlled environment in a user study [20], [21], [22].…”

Section: Ar Interface Implementationmentioning

confidence: 99%

“…While we have followed the guidance and practice from previous work [20], [21], [22], simulating AR interactions in VR might not be fully representative of a real-world use scenario.…”

Section: Limitations Generalization and Future Workmentioning

confidence: 99%

“…We also designed three conditions for labeling of out-of-view objects: height, where labels are placed at the same height as their linked objects will appear once the user rotates toward them; angle, where label positions indicate the angle between the user's viewing direction and the object, using a top-down view metaphor; and value, where labels are ordered by their associated values (e.g., restaurant ratings) on the left of the FoV. Following previous work in AR research [20], [21], [22], to easily manipulate the locations of physical objects, we used VR to simulate AR applications in our study. To achieve this, we created an AR FoV in VR, and render labels and leader lines only when they are within this AR FoV.…”

Section: Introductionmentioning

confidence: 99%

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Labeling Out-of-View Objects in Immersive Analytics to Support Situated Visual Searching

Lin,

Yang,

Beyer

et al. 2021

Preprint

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Augmented Reality (AR) embeds digital information into objects of the physical world. Data can be shown in-situ, thereby enabling real-time visual comparisons and object search in real-life user tasks, such as comparing products and looking up scores in a sports game. While there have been studies on designing AR interfaces for situated information retrieval, there has only been limited research on AR object labeling for visual search tasks in the spatial environment. In this paper, we identify and categorize different design aspects in AR label design and report on a formal user study on labels for out-of-view objects to support visual search tasks in AR. We design three visualization techniques for out-of-view object labeling in AR, which respectively encode the relative physical position (height-encoded), the rotational direction (angle-encoded), and the label values (value-encoded) of the objects. We further implement two traditional in-view object labeling techniques, where labels are placed either next to the respective objects (situated) or at the edge of the AR FoV (boundary). We evaluate these five different label conditions in three visual search tasks for static objects.Our study shows that out-of-view object labels are beneficial when searching for objects outside the FoV, spatial orientation, and when comparing multiple spatially sparse objects. Angle-encoded labels with directional cues of the surrounding objects have the overall best performance with the highest user satisfaction. We discuss the implications of our findings for future immersive AR interface design.

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Section: Ar Interface Implementationmentioning

confidence: 99%

“…While we have followed the guidance and practice from previous work [20], [21], [22], simulating AR interactions in VR might not be fully representative of a real-world use scenario.…”

Section: Limitations Generalization and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Labeling Out-of-View Objects in Immersive Analytics to Support Situated Visual Searching

Lin,

Yang,

Beyer

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Studies also used subjective methods such as: (a) Paas 9-step mentaleffort Likert scale (Paas) [13,16,112], in which score indexes of metal effort go from "very, very low effort" (1) to "very, very high effort" (9); (b) Subjective Mental Effort Question (SMEQ) [69], in which participants indicate their mental effort using a scale that goes from "not at all hard to do" (0) to "tremendously hard to do" (150); and (c) NASA-TLX [51,90,102] that is the assessment of total workload divided into six subscales: mental demand, physical demand, temporal demand, performance effort, and frustration. Presence.…”

Section: Cognitive Aspectsmentioning

confidence: 99%

“…The methods are based on questionnaires that participants in a study are asked to fill to cover various aspects of presence e.g., co-presence, spatial presence, social presence, social richness, closeness, or connectedness. Other methods that can be used to complement the analysis of presence are: (a) Ad-Hoc Usability Questionnaire (AD3) [51] to analyze the awareness of participants in an immersive environment; (b) Body Representation Questionnaire (BRQ) [97] for the analysis of embodiment; (c) McKnight Trust Questionnaire (MTQ) [57] to assess whether participants trust in technology, e.g., reliability, helpfulness, functionality, and situational normality.…”

Section: Cognitive Aspectsmentioning

confidence: 99%