Relative accuracy measures for stroke gestures

Vatavu, Radu-Daniel; Anthony, Lisa; Wobbrock, Jacob O.

doi:10.1145/2522848.2522875

Cited by 43 publications

(38 citation statements)

References 17 publications

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“…At this point, we note that other measures could be employed to further characterize the touch-screen performance of our participants, such as those found in the general literature on gesture analysis (Blagojevic et al, 2010;Rubine, 1991), relative accuracy measures that compare gesture path performance against ideal templates (Vatavu et al, 2013), or investigations on the presence of holdovers (Anthony et al, 2013a). However, that type of extended analysis would result, in our opinion, in an overwhelming amount of numerical data making presentation of results less clear for the reader.…”

Section: Methodsmentioning

confidence: 99%

“…Recognition of touch gestures has been done with algorithms specifically designed to discriminate between single-and multi-stroke gestures (Anthony and Wobbrock, 2010;Rekik et al, 2014;Vatavu, 2012;Vatavu et al, 2012;Wobbrock et al, 2007), and by employing multi-touch gesture toolkits Li, 2012, 2013). Researchers have also looked into users' gesture articulation patterns to understand more about their users and, consequently, improve the accuracy of existing recognition techniques (Anthony et al, 2013b;Rekik et al, 2013;Vatavu et al, 2013;Tu et al, 2012). For instance, Tu et al (2012) reported articulation differences between pen and finger stroke gestures.…”

Section: Understanding Touch and Designing Touch-screen Interactionsmentioning

confidence: 99%

“…For instance, Tu et al (2012) reported articulation differences between pen and finger stroke gestures. Vatavu et al (2013) analyzed gestures relatively to recognizers' stored templates, and revealed significant differences between stroke gestures in terms of geometric, kinematic, and articulation measures. Hinrichs and Carpendale (2011) showed that users' gesture preferences are influenced by the interaction and social context in which they occur, and that gestures are linked to previous articulations and form consistent patterns.…”

Section: Understanding Touch and Designing Touch-screen Interactionsmentioning

confidence: 99%

See 2 more Smart Citations

Touch interaction for children aged 3 to 6 years: Experimental findings and relationship to motor skills

Vatavu

Cramariuc

Schipor

2015

International Journal of Human-Computer Studies

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153

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Section: Methodsmentioning

confidence: 99%

Section: Understanding Touch and Designing Touch-screen Interactionsmentioning

confidence: 99%

Section: Understanding Touch and Designing Touch-screen Interactionsmentioning

confidence: 99%

See 1 more Smart Citation

Touch interaction for children aged 3 to 6 years: Experimental findings and relationship to motor skills

Vatavu

Cramariuc

Schipor

2015

International Journal of Human-Computer Studies

Self Cite

153

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“…Previous work has examined this question from several angles, such as users' consensus for multi-stroke gesture production [2], analysis of the articulation characteristics of specific user groups [14], the effect of gesture implementer on articulation [27], and explorations of how gestures vary relative to each other [29]. Several tools have been developed to enable designers to assess recognition performance and analyze users' gestures [2,5,18,29]. However, researchers and practitioners still lack adequate tools to readily visualize and explore gesture articulation patterns.…”

Section: Introductionmentioning

confidence: 99%

“…Gesture heatmaps go beyond today's gesture analysis practices that employ singlevalue descriptors to characterize gesture articulation, e.g., size, path length, or speed [2,14,27,29,31] by providing rich visualizations of how such descriptors vary along the gesture path. We demonstrate the use of gesture heatmaps with three case studies involving public datasets comprising 15,840 samples of 70 gesture types from 45 participants.…”

Section: Introductionmentioning

confidence: 99%

Gesture Heatmaps

Vatavu

Anthony

Wobbrock

2014

Proceedings of the 16th International Conference on Multimodal Interaction

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Figure 1. Gesture heatmap examples illustrating user variation during gesture articulation measured as localized absolute and relative turning angles ("alpha" and "flower"), articulation speed ("spiral"), and shape distance error from a template ("star").NOTE: Heatmaps were generated with our tool, GHoST (Gesture HeatmapS Toolkit). ABSTRACTWe introduce gesture heatmaps, a novel gesture analysis technique that employs color maps to visualize the variation of local features along the gesture path. Beyond current gesture analysis practices that characterize gesture articulations with single-value descriptors, e.g., size, path length, or speed, gesture heatmaps are able to show with colorful visualizations how the value of any such descriptors vary along the gesture path. We evaluate gesture heatmaps on three public datasets comprising 15,840 gesture samples of 70 gesture types from 45 participants, on which we demonstrate heatmaps' capabilities to (1) explain causes for recognition errors, (2) characterize users' gesture articulation patterns under various conditions, e.g., finger versus pen gestures, and (3) help understand users' subjective perceptions of gesture commands, such as why some gestures are perceived easier to execute than others. We also introduce chromatic confusion matrices that employ gesture heatmaps to extend the expressiveness of standard confusion matrices to better understand gesture classification performance. We believe that gesture heatmaps will prove useful to researchers and practitioners doing gesture analysis, and consequently, they will inform the design of better gesture sets and development of more accurate recognizers.

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PolyRec Gesture Design Tool: A tool for fast prototyping of gesture‐based mobile applications

et al. 2021

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In this article, we present PolyRec Gesture Design Tool (PolyRec GDT), a tool that allows the definition of gesture sets for mobile applications. It supports the entire process of designing gesture sets, from collecting gestures to analyzing and exporting the set created for use in a mobile application. It is equipped with the PolyRec gesture recognizer, a unistroke recognizer based on template matching with good accuracy and performances while still allowing support for other recognizers. The main features of PolyRec GDT include the ability to collect gestures directly on mobile devices, to detect similarities between gestures to help the designer to detect ambiguities in the design phase, and the ability to automatically select the most representative gestures for each gesture class by using a clustering technique to increase recognition speed without affecting accuracy. Experimental results show that this latter feature allows an increase in accuracy compared with when the same operation is performed manually by developers or randomly by software. Finally, a user study was carried out to evaluate PolyRec GDT, in which participants were asked to use it to add gesture functionality to a mobile application. The results showed that with the support of PolyRec GDT this operation took only a few minutes for participants with mobile development experience, whereas it would have taken much longer without the support of PolyRec GDT.

show abstract

Relative accuracy measures for stroke gestures

Cited by 43 publications

References 17 publications

Touch interaction for children aged 3 to 6 years: Experimental findings and relationship to motor skills

Touch interaction for children aged 3 to 6 years: Experimental findings and relationship to motor skills

Gesture Heatmaps

PolyRec Gesture Design Tool: A tool for fast prototyping of gesture‐based mobile applications

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