Title
Exploring Visual Techniques for Boundary Awareness During Interaction in Augmented Reality Head-Mounted Displays
Abstract
Mid-air hand interaction has long been proposed as a ‘natural’ input method for Augmented Reality (AR) systems. Current AR Head-Mounted Displays (HMDs) have a limited area for hand-based interactions. Because of this, users may easily move their hand(s) outside this tracked area during interaction, especially in dynamic tasks (e.g., when translating an object). Compared to common midair interaction issues, such as gesture recognition, arm/hand fatigue, and unnatural ways of interacting with virtual objects (e.g., selecting a distant object), boundary awareness issues in AR devices have received little attention. In this research, we explore visual techniques for boundary awareness in AR HMDs, focusing on object translation tasks. Through a systematic formative study, we first identify the challenges that users might face when interacting with AR HMDs without any boundary awareness information (i.e., how current systems work). Based on the findings, we then propose four methods (i.e., static surfaces, dynamic surface(s), static coordinated lines, and dynamic coordinate line(s)) and evaluate them against the benchmark (i.e., baseline condition without boundary awareness) to make users aware of the tracked interaction area. Our results show that visual methods for boundary awareness can help with dynamic mid-air hand interactions in AR HMDs, but their effectiveness and application are user-dependent.
Year
DOI
Venue
2020
10.1109/VR46266.2020.00039
2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR)
Keywords
DocType
ISSN
Human-centered computing,Human computer interaction (HCI),Interaction paradigms,Mixed/augmented reality,Human-centered computing,Visualization,Visualization techniques
Conference
2642-5246
ISBN
Citations 
PageRank 
978-1-7281-5609-5
1
0.35
References 
Authors
23
5
Name
Order
Citations
PageRank
Wenge Xu1416.95
Hai-Ning Liang219837.97
Yuzheng Chen331.38
Xiang Li430.70
Kangyou Yu521.03