A Systematic Review of Gesture Elicitation Studies: What Can We Learn from 216 Studies?
Authors
Gesture elicitation studies represent a popular and resourceful method in HCI to inform the design of intuitive gesture commands, reflective of end-users’ behavior, for controlling all kinds of interactive devices, applications, and systems. In the last ten years, an impressive body of work has been published on this topic, disseminating useful design knowledge regarding users’ preferences for finger, hand, wrist, arm, head, leg, foot, and whole-body gestures. In this paper, we deliver a systematic literature review of this large body of work by summarizing the characteristics and findings ofN=216gesture elicitation studies subsuming 5,458 participants, 3,625 referents, and 148,340 elicited gestures. We highlight the descriptive, comparative, and generative virtues of our examination to provide practitioners with an effective method to (i) understand how new gesture elicitation studies position in the literature; (ii) compare studies from different authors; and (iii) identify opportunities for new research. We make our large corpus of papers accessible online as a Zotero group library at https://www.zotero.org/groups/2132650/gesture_elicitation_studies.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 100%
Intermanual Deictics: Uncovering Users' Gesture Preferences for Opposite-Arm Referential Input, from Fingers to Shoulder
CHI '25· Hand Gesture Recognition +2
- 67%
Motion Correlation: Selecting Objects by Matching Their Movement
CHI '18· Hand Gesture Recognition +1
- 67%
Introducing Transient Gestures to Improve Pan and Zoom on Touch Surfaces
CHI '18· Hand Gesture Recognition +1
- 67%
Designing Coherent Gesture Sets for Multi-scale Navigation on Tabletops
CHI '18· Hand Gesture Recognition +1
- 67%
Effect of Orientation on Unistroke Touch Gestures
CHI '19· Hand Gesture Recognition +1
- 67%
Adults' and Children's Mental Models for Gestural Interactions with Interactive Spherical Displays
CHI '20· Hand Gesture Recognition +1
- 67%
Dynamics of Aimed Mid-air Movements
CHI '20· Hand Gesture Recognition +1
- 67%
FingerMapper: Mapping Finger Motions onto Virtual Arms to Enable Safe Virtual Reality Interaction in Confined Spaces
CHI '23· Hand Gesture Recognition +1
- 67%
Emotion Embodied: Unveiling the Expressive Potential of Single-Hand Gestures
CHI '24· Hand Gesture Recognition +1
- 67%
STMG: A Machine Learning Microgesture Recognition System for Supporting Thumb-Based VR/AR Input
CHI '24· Hand Gesture Recognition +1
Based on Jaccard similarity of research subtopics & professions (≥60%)