µGlyph: a Microgesture Notation
Authors
In the active field of hand microgestures, microgesture descriptions are typically expressed informally and are accompanied by images, leading to ambiguities and contradictions. An important step in moving the field forward is a rigorous basis for precisely describing, comparing, and analyzing microgestures. Towards this goal, we propose µGlyph, a hybrid notation based on a vocabulary of events inspired by finger biomechanics. First, we investigate the expressiveness of µGlyph by building a database of 118 microgestures extracted from the literature. Second, we experimentally explore the usability of µGlyph. Participants correctly read and wrote µGlyph descriptions 90% of the time, as compared to 46% for conventional descriptions. Third we present tools that promote µGlyph usage, including a visual editor with LaTeX export. We finally describe how µGlyph can guide research on designing, developing, and evaluating microgesture interaction. Results demonstrate the strong potential of µGlyph to establish a common ground for microgesture research.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 75%
Pentelligence: Combining Pen Tip Motion and Writing Sounds for Handwritten Digit Recognition
CHI '18· Hand Gesture Recognition +1
- 75%
The Voight-Kampff Machine for Automatic Custom Gesture Rejection Threshold Selection
CHI '22· Hand Gesture Recognition +1
- 75%
RestfulRaycast: Exploring Ergonomic Rigging and Joint Amplification for Precise Hand Ray Selection in XR
DIS '25· Hand Gesture Recognition +1
- 75%
Abacus Gestures: A Large Set of Math-Based Usable Finger-Counting Gestures for Mid-Air Interactions
UbiComp '23· Hand Gesture Recognition +1
- 75%
Gesture-aware Interactive Machine Teaching with In-situ Object Annotations
UIST '22· Hand Gesture Recognition +1
- 67%
Implementing Multi-Touch Gestures with Touch Groups and Cross Events
CHI '19· Hand Gesture Recognition
- 67%
CapContact: Super-resolution Contact Areas from Capacitive Touchscreens
CHI '21· Hand Gesture Recognition
- 60%
Show of Hands: Leveraging Hand Gestural Cues in Virtual Meetings for Intelligent Impromptu Polling Interactions
IUI '22· Hand Gesture Recognition +1
- 60%
Tip-Tap: Battery-free Discrete 2D Fingertip Input
UIST '19· Haptic Wearables +1
Based on Jaccard similarity of research subtopics & professions (≥60%)