Designing Coherent Gesture Sets for Multi-scale Navigation on Tabletops
Multi-scale navigation interfaces were originally designed to enable single users to explore large visual information spaces on desktop workstations. These interfaces can also be quite useful on tabletops. However, their adaptation to co-located multi-user contexts is not straightforward. The literature describes different interfaces, that only offer a limited subset of navigation actions. In this paper, we first identify a comprehensive set of actions to effectively support multi-scale navigation. We report on a guessability study in which we elicited user-defined gestures for triggering these actions, showing that there is no natural design solution, but that users heavily rely on the now-ubiquitous slide, pinch and turn gestures. We then propose two interface designs based on this set of three basic gestures: one involves two-hand variations on these gestures, the other combines them with widgets. A comparative study suggests that users can easily learn both, and that the gesture-based, visually-minimalist design is a viable option, that saves display space for other controls.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 100%
Motion Correlation: Selecting Objects by Matching Their Movement
CHI '18· Hand Gesture Recognition +1
- 100%
Effect of Orientation on Unistroke Touch Gestures
CHI '19· Hand Gesture Recognition +1
- 100%
Adults' and Children's Mental Models for Gestural Interactions with Interactive Spherical Displays
CHI '20· Hand Gesture Recognition +1
- 100%
Dynamics of Aimed Mid-air Movements
CHI '20· Hand Gesture Recognition +1
- 100%
FingerMapper: Mapping Finger Motions onto Virtual Arms to Enable Safe Virtual Reality Interaction in Confined Spaces
CHI '23· Hand Gesture Recognition +1
- 100%
Emotion Embodied: Unveiling the Expressive Potential of Single-Hand Gestures
CHI '24· Hand Gesture Recognition +1
- 100%
STMG: A Machine Learning Microgesture Recognition System for Supporting Thumb-Based VR/AR Input
CHI '24· Hand Gesture Recognition +1
- 100%
T2IRay: Design of Thumb-to-Index based Indirect Pointing for Continuous and Robust AR/VR Input
CHI '25· Hand Gesture Recognition +1
- 100%
BodyTouch: Investigating Eye-Free, On-Body and Near-Body Touch Interactions with HMDs
UbiComp '24· Hand Gesture Recognition +1
- 100%
Unimanual Pen+Touch Input Using Variations of Precision Grip Postures
UIST '18· Hand Gesture Recognition +1
Based on Jaccard similarity of research subtopics & professions (≥60%)