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Gesture Learning in Human Computer Interaction
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- Author / Creator
- Anderson, Fraser S
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After decades of research, gestural interfaces are becoming increasingly commonplace in our interactions with modern devices. They promise natural and efficient interaction, but suffer from a lack of affordances and thus require learning on the part of the user.
This thesis examines the declarative and procedural components of learning gestural interaction, and how designers can best support gesture learning within their interfaces. First, we show that user-defined gestures are not always consistent, even when the same user is defining a gesture for the same task, indicating that even when the user is able to select their own gestures some amount of gesture learning still may be necessary. Next, we present two studies that help us better understand the role of visual feedback, finding that it has a dramatic effect on the degree to which gestures are learned. Next, we examine the procedural component of gesture learning by varying the scale, location, and animation of visual feedback presented during training. We also show that evaluation using a retention and transfer paradigm is more appropriate for evaluating gestures than the other methodologies used previously. Lastly, we present YouMove, a full-body gesture training system that incorporates the lessons learned from the present work on stroke-based gestures. -
- Graduation date
- Fall 2014
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- Type of Item
- Thesis
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- Degree
- Doctor of Philosophy
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.