Iteratively Designing Gesture Vocabularies: A Survey and Analysis of Best Practices in the HCI Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Gestural interaction has evolved from a set of novel interaction techniques developed in research labs, to a dominant interaction modality used by millions of users everyday. Despite its widespread adoption, the design of appropriate gesture vocabularies remains a challenging task for developers and designers. Existing research has largely used Expert-Led, User-Led, or Computationally-Based methodologies to design gesture vocabularies. These methodologies leverage the expertise, experience, and capabilities of experts, users, and systems to fulfill different requirements. In practice, however, none of these methodologies provide designers with a complete, multi-faceted perspective of the many factors that influence the design of gesture vocabularies, largely because a singular set of factors has yet to be established. Additionally, these methodologies do not identify or emphasize the subset of factors that are crucial to consider when designing for a given use case. Therefore, this work reports on the findings from an exhaustive literature review that identified 13 factors crucial to gesture vocabulary design and examines the evaluation methods and interaction techniques commonly associated with each factor. The identified factors also enable a holistic examination of existing gesture design methodologies from a factor-oriented viewpoint and highlighting the strengths and weaknesses of each methodology. This work closes with proposals of future research directions of developing an iterative user-centered and factor-centric gesture design approach as well as establishing an evolving ecosystem of factors that are crucial to gesture design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it