Modeling Gesture-Typing Movements
Bibliographic Data
| ID | 15215786 |
|---|---|
| Authors | Philip Quinn (0000-0002-6340-0568, Google Inc, corresponding author), Shumin Zhai (0000-0003-0752-2090, Google Inc) |
| Year | 2016 |
| Volume | 33 |
| Issue | 3 |
| Pages | 234-280 |
| Publication date | 2016-08-26 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Human-Computer Interaction (JOURNAL) |
| Journal identifiers | ISSN: 0737-0024 • E-ISSN: 1532-7051 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/07370024.2016.1215922 |
| OpenAlex | W2513314960 |
| Language | EN |
| Citations received | 1 |
| References cited | 73 |
Word–Gesture keyboards allow users to enter text using continuous input strokes (also known as gesture typing or shape writing). We developed a production model of gesture typing input based on a human motor control theory of optimal control (specifically, modeling human drawing movements as a minimization of jerk—the third derivative of position). In contrast to existing models, which consider gestural input as a series of concatenated aiming movements and predict a user’s time performance, this descriptive theory of human motor control predicts the shapes and trajectories that users will draw. The theory is supported by an analysis of user-produced gestures that found qualitative and quantitative agreement between the shapes users drew and the minimum jerk theory of motor control. Furthermore, by using a small number of statistical via-points whose distributions reflect the sensorimotor noise and speed–accuracy trade-off in gesture typing, we developed a model of gesture production that can predict realistic gesture trajectories for arbitrary text input tasks. The model accurately reflects features in the figural shapes and dynamics observed from users and can be used to improve the design and evaluation of gestural input systems
Computer vision · Control (management · Gesture · Gesture recognition · Human–computer interaction · Jerk · Motion (physics · Speech recognition · Word (group theory · Computer Science · Mathematics · Motor Control and Adaptation · Muscle activation and electromyography studies · Tactile and Sensory Interactions · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,17 |
| Citation span | 2020 - 2020 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |