Name
Abstract | ||
|---|---|---|
Touch gestures on the screen of a wrist worn device are constrained by the size of the screen. Decoupling the gesture size from the screen size allows for larger gestures on smaller devices. Other approaches to decoupling screen size from gesture size on wrist worn devices support only a small set of gestures. We decouple screen size from gesture size by using an optical flow sensor. The user generates gestures by moving a finger over the optical flow sensor. Gestures can feasibly be detected through a small round window with a diameter of 3 mm. This window “dot” could be embedded in small wrist worn devices. A random forest trained on the EdgeWrite alphabet achieved 93% accuracy on 27 gestures generated using the optical flow sensor. We discuss the motivation for such a system and prove its feasibility using a prototype, noting that additional engineering work is needed to produce a small wrist-worn device based on a small optical flow sensor package. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/PERCOMW.2018.8480406 | 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) |
Keywords | Field | DocType |
Wrist computing,gestures,wrist input | Computer vision,Wrist,Display size,Computer science,Gesture,Decoupling (cosmology),Artificial intelligence,Random forest,Optical flow,Adaptive optics,Distributed computing,Alphabet | Conference |
ISSN | ISBN | Citations |
2474-2503 | 978-1-5386-3228-4 | 0 |
PageRank | References | Authors |
0.34 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Jones | 1 | 65 | 9.00 |
| Kevin D. Seppi | 2 | 335 | 41.46 |
| Jared Forsyth | 3 | 0 | 0.34 |
| Zann Anderson | 4 | 8 | 4.23 |