TY - GEN
T1 - The challenges and potential of end-user gesture customization
AU - Oh, Uran
AU - Findlater, Leah
PY - 2013
Y1 - 2013
N2 - The vast majority of work on understanding and supporting the gesture creation process has focused on professional designers. In contrast, gesture customization by end users- which may offer better memorability, efficiency and accessibility than pre-defined gestures-has received little attention. To understand the end-user gesture creation process, we conducted a study where 20 participants were asked to: (1) exhaustively create new gestures for an openended use case; (2) exhaustively create new gestures for 12 specific use cases; (3) judge the saliency of different touchscreen gesture features. Our findings showed that even when asked to create novel gestures, participants tended to focus on the familiar. Misconceptions about the gesture recognizer's abilities were also evident, and in some cases constrained the range of gestures that participants created. Finally, as a calibration point for future research, we used a simple gesture recognizer ($N) to analyze recognition accuracy of the participants' custom gesture sets: accuracy was 68-88% on average, depending on the amount of training and the customization scenario. We conclude with implications for the design of a mixed-initiative approach to support custom gesture creation.
AB - The vast majority of work on understanding and supporting the gesture creation process has focused on professional designers. In contrast, gesture customization by end users- which may offer better memorability, efficiency and accessibility than pre-defined gestures-has received little attention. To understand the end-user gesture creation process, we conducted a study where 20 participants were asked to: (1) exhaustively create new gestures for an openended use case; (2) exhaustively create new gestures for 12 specific use cases; (3) judge the saliency of different touchscreen gesture features. Our findings showed that even when asked to create novel gestures, participants tended to focus on the familiar. Misconceptions about the gesture recognizer's abilities were also evident, and in some cases constrained the range of gestures that participants created. Finally, as a calibration point for future research, we used a simple gesture recognizer ($N) to analyze recognition accuracy of the participants' custom gesture sets: accuracy was 68-88% on average, depending on the amount of training and the customization scenario. We conclude with implications for the design of a mixed-initiative approach to support custom gesture creation.
KW - Customization
KW - Gestures
KW - Personalization
KW - Touchscreen
UR - http://www.scopus.com/inward/record.url?scp=84877971285&partnerID=8YFLogxK
U2 - 10.1145/2470654.2466145
DO - 10.1145/2470654.2466145
M3 - Conference contribution
AN - SCOPUS:84877971285
SN - 9781450318990
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 1129
EP - 1138
BT - CHI 2013
T2 - 31st Annual CHI Conference on Human Factors in Computing Systems: Changing Perspectives, CHI 2013
Y2 - 27 April 2013 through 2 May 2013
ER -