AGATe Tool

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

AGreement Analysis Toolkit (AGATe)

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

Download

Current Version: 2.0 May 2016

Windows executable: EXE
AGATe source code: C#
DLL source code: C#
Papers: PDF, PDF

Microsoft .NET 4.5 Framework required. Download it here.
This software is distributed under the New BSD License agreement.

About AGATe

The AGreement Analysis Toolkit (AGATe) is an application and associated reusable library (DLL) named AgreementRates.dll that supports the user-defined gesture elicitation methodology by calculating agreement rate statistics on data collected using this methodology. The methodology allows users to propose gestures to accomplish specified tasks in human-computer interaction, and final gesture sets are designed to maximize the guessability of the adopted gestures.

R Code for Agreement Rate Statistics

Download this "R-agreement.zip" archive for R code that implements simulation experiments to estimate the Type I error and power for a variety of statistical tests, including V_rd (Vatavu & Wobbrock, 2015), V_b (Vatavu and Wobbrock, 2016), the percentile bootstrap, and others, for comparing agreement rates in within- and between-subjects end-user elicitation studies. Our code uses Rand Wilcox's "Rallfun" R library for robust statistics. The main entry point of our code is the "main.R" file that runs all the simulation experiments.

AGATe Video

Our Gesture Software Projects

$Q: Super-quick multistroke recognizer - optimized for low-power mobiles and wearables
$P+: Point-cloud multistroke recognizer - optimized for people with low vision
$P: Point-cloud multistroke recognizer - for recognizing multistroke gestures as point-clouds
$N: Multistroke recognizer - for recognizing simple multistroke gestures
$1: Unistroke recognizer - for recognizing unistroke gestures

AGATe: AGreement Analysis Toolkit - for calculating agreement in gesture-elicitation studies
GHoST: Gesture HeatmapS Toolkit - for visualizing variation in gesture articulation
GREAT: Gesture RElative Accuracy Toolkit - for measuring variation in gesture articulation
GECKo: GEsture Clustering toolKit - for clustering gestures and calculating agreement

Our Gesture Publications

Vatavu, R.-D. and Wobbrock, J.O. (2022). Clarifying agreement calculations and analysis for end-user elicitation studies. ACM Transactions on Computer-Human Interaction 29 (1). Article No. 5.
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. Article No. 23.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.