Prediction of user intentions when operating with the cursor

Cezard, Lucas

Prediction of user intentions when operating with the cursor

dc.contributor.advisor	Kang, Ziho
dc.contributor.author	Cezard, Lucas
dc.contributor.committeeMember	Nicholson, Charles
dc.contributor.committeeMember	Trafalis, Theodore
dc.date.accessioned	2017-12-13T15:58:40Z
dc.date.available	2017-12-13T15:58:40Z
dc.date.issued	2017-12-15
dc.date.manuscript	2017-12-12
dc.description.abstract	Studies have shown that when we operate the cursor on a computer, several factors such as the type of device used (i.e. mouse or touchpad), aging (young vs. old) or motor-impairment can hinder performances. More precisely, using the touchpad can be difficult for any user, even for the most basic tasks, due to the absence of scrolling wheel and the reduced amplitude of cursor movement. To cope with these issues, I developed a set of tools to increase the usability of the touchpad by analyzing mouse-tracking data. More specifically, several movement patterns or cues were predefined and when they were detected, they would trigger the auto-completion of the related task which includes navigating on a web browser, selecting text and scrolling. The usability experiment conducted to assess the ease-of-use of the created tools and to compare the performances of participants showed promising results. Participants appreciated the help of the auto-completion tools and when they were able to trigger these tools, they were significantly faster. In particular, when moving the cursor to the URL address bar they even outperformed Fitts’ law predictions. However, it appeared that participants needed several attempts to draw certain cues correctly hence a longer completion time.	en_US
dc.identifier.uri	https://hdl.handle.net/11244/52771
dc.language	en_US	en_US
dc.subject	human factors	en_US
dc.subject	usability	en_US
dc.subject	pattern detection	en_US
dc.subject	auto-completion	en_US
dc.thesis.degree	Master of Science	en_US
dc.title	Prediction of user intentions when operating with the cursor	en_US
ou.group	College of Engineering::School of Industrial and Systems Engineering	en_US