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INTERCHI '93 Proceedings of the INTERCHI '93 conference on Human factors in computing systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the 1st international conference on Computer graphics and interactive techniques in Australasia and South East Asia
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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CHI '04 Extended Abstracts on Human Factors in Computing Systems
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GI '05 Proceedings of Graphics Interface 2005
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GI '05 Proceedings of Graphics Interface 2005
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the 20th annual ACM symposium on User interface software and technology
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Proceedings of the 2008 symposium on Eye tracking research & applications
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the 21st annual ACM symposium on User interface software and technology
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the ACM International Conference on Interactive Tabletops and Surfaces
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Protractor: a fast and accurate gesture recognizer
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Learning and performance with gesture guides
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Gestural interfaces are now a familiar mode of user interaction and gestural input is an important part of the way that users can interact with such interfaces. However, entering gestures accurately and efficiently can be challenging. In this paper we present two styles of visual gesture autocompletion for 2D predictive gesture entry. Both styles enable users to abbreviate gestures. We experimentally evaluate and compare both styles of visual autocompletion against each other and against non-predictive gesture entry. The best performing visual autocompletion is referred to as SimpleFlow. Our findings establish that users of SimpleFlow take significant advantage of gesture autocompletion by entering partial gestures rather than whole gestures. Compared to nonpredictive gesture entry, users enter partial gestures that are 41% shorter than the complete gestures, while simultaneously improving the accuracy (+13%, from 68% to 81%) and speed (+10%) of their gesture input. The results provide insights into why SimpleFlow leads to significantly enhanced performance, while showing how predictive gestures with simple visual autocompletion impacts upon the gesture abbreviation, accuracy, speed and cognitive load of 2D predictive gesture entry.