NeighboAR: Efficient Object Retrieval using Proximity-and Gaze-based Object Grouping with an AR System
Journal
Proceedings of the ACM on Human-Computer Interaction
ISSN
2573-0142
Type
journal article
Date Issued
2024-05-28
Author(s)
Research Team
Interaction- and Communication-based Systems (https://interactions.ics.unisg.ch)
Abstract
Humans only recognize a few items in a scene at once and memorize three to seven items in the short term. Such limitations can be mitigated using cognitive offloading (e.g., sticky notes, digital reminders). We studied whether a gaze-enabled Augmented Reality (AR) system could facilitate cognitive offloading and improve object retrieval performance. To this end, we developed NeighboAR, which detects objects in a user's surroundings and generates a graph that stores object proximity relationships and user's gaze dwell times for each object. In a controlled experiment, we asked N=17 participants to inspect randomly distributed objects and later recall the position of a given target object. Our results show that displaying the target together with the proximity object with the longest user gaze dwell time helps recalling the position of the target. Specifically, NeighboAR significantly reduces the retrieval time by 33%, number of errors by 71%, and perceived workload by 10%.
Language
English
Keywords
augmented reality
cognitive offloading
eye tracking
object detection
human augmentation
mixed reality
working memory
visual search
innovation
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
ACM
Publisher place
New York, NY, USA
Volume
8
Number
ETRA
Pages
19
Subject(s)
Division(s)
Contact Email Address
kenan.bektas@unisg.ch
File(s)![Thumbnail Image]()
open.access
Name
Slavuljica et al_NeighboAR_ETRA24.pdf
Size
2.74 MB
Format
Adobe PDF
Checksum (MD5)
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