Recognizers: $1  •  $N  •  $P  •  $P+  •  $Q  •  Impact of $-family
Tools: GECKo  •  GREAT  •  GHoST  •  AGATe

$Q Super-Quick Recognizer

Radu-Daniel Vatavu, University Stefan cel Mare of Suceava
Lisa Anthony, University of Florida
Jacob O. Wobbrock, University of Washington [contact]

Download

$Q source code: JavaScript, C#
Pseudocode: $Q
Multistroke gesture logs: XML
Paper: PDF

This software is distributed under the New BSD License agreement.

About

The $Q Super-Quick Recognizer is a 2-D gesture recognizer designed for rapid prototyping of gesture-based user interfaces, especially on low-power mobiles and wearables. It builds upon the $P Point-Cloud Recognizer but optimizes it to achieve a whopping 142× speedup, even while improving its accuracy slightly. $Q is currently the most performant recognizer in the $-family. Despite being incredibly fast, it is still fundamentally simple, easy to implement, and requires minimal lines of code. Like all members of the $-family, $Q is ideal for people wishing to add stroke-gesture recognition to their projects, now blazing fast even on low-capability devices.

The $-family recognizers have been built into numerous projects and even industry prototypes, and have had many follow-ons by others. Read about the $-family's impact.

Demo (Under construction!)

In the demo below, only one point-cloud template is loaded for each of the 16 gesture types. You can add additional templates as you wish, and even define your own custom gesture templates.


Make strokes on this canvas. Right-click the canvas to recognize. If a misrecognition occurs, add the mis- recognized gesture as an example of the intended gesture.

The <canvas> element is not supported by this browser.
Add as example of existing type:
Add as example of custom type:
Delete all user-defined gestures:  

Our Gesture Software Projects

Our Gesture Publications

  1. Vatavu, R.-D., Anthony, L. and Wobbrock, J.O. (2018). $Q: A super-quick, articulation-invariant stroke-gesture recognizer for low-resource devices. Proceedings of the ACM Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '18). Barcelona, Spain (September 3-6, 2018). New York: ACM Press. To appear.
  2. Vatavu, R.-D. (2017). Improving gesture recognition accuracy on touch screens for users with low vision. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '17). Denver, Colorado (May 6-11, 2017). New York: ACM Press, pp. 4667-4679.
  3. Vatavu, R.-D. and Wobbrock, J.O. (2016). Between-subjects elicitation studies: Formalization and tool support. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '16). San Jose, California (May 7-12, 2016). New York: ACM Press, pp. 3390-3402.
  4. Vatavu, R.-D. and Wobbrock, J.O. (2015). Formalizing agreement analysis for elicitation studies: New measures, significance test, and toolkit. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '15). Seoul, Korea (April 18-23, 2015). New York: ACM Press, pp. 1325-1334.
  5. Vatavu, R.-D., Anthony, L. and Wobbrock, J.O. (2014). Gesture heatmaps: Understanding gesture performance with colorful visualizations. Proceedings of the ACM International Conference on Multimodal Interfaces (ICMI '14). Istanbul, Turkey (November 12-16, 2014). New York: ACM Press, pp. 172-179.
  6. Vatavu, R.-D., Anthony, L. and Wobbrock, J.O. (2013). Relative accuracy measures for stroke gestures. Proceedings of the ACM International Conference on Multimodal Interfaces (ICMI '13). Sydney, Australia (December 9-13, 2013). New York: ACM Press, pp. 279-286.
  7. Anthony, L., Vatavu, R.-D. and Wobbrock, J.O. (2013). Understanding the consistency of users' pen and finger stroke gesture articulation. Proceedings of Graphics Interface (GI '13). Regina, Saskatchewan (May 29-31, 2013). Toronto, Ontario: Canadian Information Processing Society, pp. 87-94.
  8. Vatavu, R.-D., Anthony, L. and Wobbrock, J.O. (2012). Gestures as point clouds: A $P recognizer for user interface prototypes. Proceedings of the ACM International Conference on Multimodal Interfaces (ICMI '12). Santa Monica, California (October 22-26, 2012). New York: ACM Press, pp. 273-280.
  9. Anthony, L. and Wobbrock, J.O. (2012). $N-Protractor: A fast and accurate multistroke recognizer. Proceedings of Graphics Interface (GI '12). Toronto, Ontario (May 28-30, 2012). Toronto, Ontario: Canadian Information Processing Society, pp. 117-120.
  10. Anthony, L. and Wobbrock, J.O. (2010). A lightweight multistroke recognizer for user interface prototypes. Proceedings of Graphics Interface (GI '10). Ottawa, Ontario (May 31-June 2, 2010). Toronto, Ontario: Canadian Information Processing Society, pp. 245-252.
  11. Wobbrock, J.O., Wilson, A.D. and Li, Y. (2007). Gestures without libraries, toolkits or training: A $1 recognizer for user interface prototypes. Proceedings of the ACM Symposium on User Interface Software and Technology (UIST '07). Newport, Rhode Island (October 7-10, 2007). New York: ACM Press, pp. 159-168.

Copyright © 2018 Jacob O. Wobbrock. All rights reserved.
Last updated July 14, 2018.