2026-09-22T01:57:11Zhttps://api.mountainscholar.org/server/oai/requestoai:mountainscholar.org:10217/1669112025-12-30T03:24:53Zcom_10217_100532com_10217_100000com_10217_100388com_10217_100303col_10217_100538col_10217_100389
Morrison-Smith, Sarah, author
Ruiz, Jaime, advisor
Boucher, Christina, committee member
Peterson, Chris, committee member
2015-08-27T03:57:01Z
2015-08-27T03:57:01Z
2015
MorrisonSmith_colostate_0053N_12870.pdf
http://hdl.handle.net/10217/166911
https://doi.org/10.25675/3.018583
Motion gestures are an underutilized input modality for mobile interaction despite numerous potential advantages. Negulescu et al. found that the lack of feedback on attempted motion gestures made it difficult for participants to diagnose and correct errors, resulting in poor recognition performance and user frustration. In this paper, we describe and evaluate a training and feedback technique, Glissando, which uses audio characteristics to provide feedback on the systems interpretation of user input. This technique enables feedback by verbally confirming correct gestures and notifying users of errors in addition to providing continuous feedback by mapping distinct musical notes to each of three axes and manipulating pitch to specify both spatial and temporal information.
born digital
masters theses
English
eng
Colorado State University. Libraries
2000-2019
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human-computer interaction
motion gestures
mobile interaction
audio feedback
Using audio cues to support motion gesture interaction on mobile devices
Text
Master of Science (M.S.)
Masters
Computer Science
Colorado State University
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open.access