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PRODID:Linklings LLC
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TZID:Australia/Brisbane
X-LIC-LOCATION:Australia/Brisbane
BEGIN:STANDARD
DTSTART:19920301T030000
TZOFFSETFROM:+1100
TZOFFSETTO:+1000
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BEGIN:VEVENT
DTSTAMP:20191203T080506Z
LOCATION:Plaza Meeting Room P2
DTSTART;TZID=Australia/Brisbane:20191119T171800
DTEND;TZID=Australia/Brisbane:20191119T173900
UID:siggraphasia_SIGGRAPH Asia 2019_sess115_papers_299@linklings.com
SUMMARY:Modeling Endpoint Distribution of Pointing Selection Tasks in Virt
 ual Reality Environments
DESCRIPTION:Technical Papers\n\nModeling Endpoint Distribution of Pointing
  Selection Tasks in Virtual Reality Environments\n\nYu, Liang, Lu, Fan, En
 s\n\nUnderstanding the endpoint distribution of pointing selection tasks c
 an reveal the underlying patterns on how users tend to acquire a target, w
 hich is one of the most essential and pervasive tasks in interactive syste
 ms. It could further aid designers to create new graphical user interfaces
  and interaction techniques that are optimized for accuracy, efficiency, a
 nd ease of use. Previous research has explored the modeling of endpoint di
 stribution outside of virtual reality (VR) systems that have shown to be u
 seful in predicting selection accuracy and guide the design of new interac
 tive techniques. This work aims at developing an endpoint distribution of 
 selection tasks for VR systems which has resulted in EDModel, a novel mode
 l that can be used to predict endpoint distribution of pointing selection 
 tasks in VR environments. The development of EDModel is based on two users
  studies that have explored how factors such as target size, movement ampl
 itude, and target depth affect the endpoint distribution. The model is bui
 lt from the collected data and its generalizability is subsequently tested
  in complex scenarios with more relaxed conditions. Three applications of 
 EDModel inspired by previous research are evaluated to show the broad appl
 icability and usefulness of the model: correcting the bias in Fitts's law,
  predicting selection accuracy, and enhancing pointing selection technique
 s. Overall, EDModel can achieve high prediction accuracy and can be adapte
 d to different types of applications in VR.\n\nRegistration Category: Plat
 inum Pass, Full Conference Pass, Full Conference One-Day Pass
URL:https://sa2019.conference-program.com/presentation?id=papers_299&sess=
 sess115
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