GREAT Tool

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Current Version: 1.0.0.0

Windows executable: EXE
GREAT source code: C#
DLL source code: C#
Paper: PDF

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

About

The Gesture RElative Accuracy Toolkit (GREAT) is an application and associated reusable library (DLL) named RelativeAccuracyMeasures.dll for calculating relative accuracy measures for a set of gestures. The 12 accuracy measures capture what happens during stroke gesture articulation, and are therefore more revealing than overall recognition accuracy. For example, one measure is Shape Variability (ShV), which captures how much a gesture's shape deviates along its path from a reference gesture. Reference gestures are determined by computing the average gesture from a set, and then finding which articulated gesture is closest to that average. The library provided encapsulates the computing of all 12 measures and can be reused in other applications.

The 12 relative accuracy measures defined in the library are:

Shape Error (ShE): Average spatial deviation from a reference gesture.
Shape Variability (ShV): Total spatial deviation from a reference gesture.
Length Error (LE): Amount of "stretch" relative to a reference gesture.
Size Error (SzE): Amount of space consumed relative to a reference gesture.
Bending Error (BE): Average "turn" relative to a reference gesture.
Bending Variability (BV): Total "turn" relative to a reference gesture.
Time Error (TE): Average temporal deviation from a reference gesture.
Time Variability (TV): Total temporal deviation from a reference gesture.
Speed Error (VE): Average deviation in speed compared to a reference gesture.
Speed Variability (VV): Total deviation in speed compared to a reference gesture.
Stroke Count Error (SkE): Difference in number of strokes compared to a reference gesture.
Stroke Ordering Error (SkOE): How similar the stroke ordering is compared to a reference gesture.

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.