Real-Time Activity Prediction: A Gaze-Based Approach for Early Recognition of Pen-Based Interaction Tasks

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dc.contributor.authorÇıg, Çaglaen_US
dc.contributor.authorSezgin, Tevfik Metinen_US
dc.contributor.editorErgun Aklemanen_US
dc.date.accessioned2015-06-22T07:06:52Z
dc.date.available2015-06-22T07:06:52Z
dc.date.issued2015en_US
dc.description.abstractRecently there has been a growing interest in sketch recognition technologies for facilitating human-computer interaction. Existing sketch recognition studies mainly focus on recognizing pre-defined symbols and gestures. However, just as there is a need for systems that can automatically recognize symbols and gestures, there is also a pressing need for systems that can automatically recognize pen-based manipulation activities (e.g. dragging, maximizing, minimizing, scrolling). There are two main challenges in classifying manipulation activities. First is the inherent lack of characteristic visual appearances of pen inputs that correspond to manipulation activities. Second is the necessity of real-time classification based upon the principle that users must receive immediate and appropriate visual feedback about the effects of their actions. In this paper (1) an existing activity prediction system for pen-based devices is modified for real-time activity prediction and (2) an alternative time-based activity prediction system is introduced. Both systems use eye gaze movements that naturally accompany pen-based user interaction for activity classification. The results of our comprehensive experiments demonstrate that the newly developed alternative system is a more successful candidate (in terms of prediction accuracy and early prediction speed) than the existing system for real-time activity prediction. More specifically, midway through an activity, the alternative system reaches 66% of its maximum accuracy value (i.e. 66% of 70.34%) whereas the existing system reaches only 36% of its maximum accuracy value (i.e. 36% of 55.69%).en_US
dc.description.sectionheadersStylizationen_US
dc.description.seriesinformationSketch-Based Interfaces and Modelingen_US
dc.identifier.doi10.2312/exp.20151179en_US
dc.identifier.pages59-65en_US
dc.identifier.urihttps://doi.org/10.2312/exp.20151179en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectH.1.2 [Models and Principles]en_US
dc.subjectUser/Machine Systemsen_US
dc.subjectHuman information processing H.5.2 [Information Interfaces and Presentation (e.g.en_US
dc.subjectHCI)]en_US
dc.subjectUser Interfacesen_US
dc.subjectInput devices and strategies (e.g.en_US
dc.subjectmouseen_US
dc.subjecttouchscreen) Keywordsen_US
dc.subjecteager activity recognitionen_US
dc.subjectsketch recognitionen_US
dc.subjectproactive interfacesen_US
dc.subjectmultimodal interactionen_US
dc.subjectsketchbased interactionen_US
dc.subjectgazeen_US
dc.subjectbased interactionen_US
dc.subjectfeature extractionen_US
dc.titleReal-Time Activity Prediction: A Gaze-Based Approach for Early Recognition of Pen-Based Interaction Tasksen_US
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SBM15: Sketch Based Interfaces and Modeling 2015