NDLI: Creating personalized digital human models of perception for visual analytics

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Creating personalized digital human models of perception for visual analytics

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User modeling: a notoriously black art

Creating personalized digital human models of perception for visual analytics

Content Provider	ACM Digital Library
Author	Bennett, Mike Quigley, Aaron
Abstract	Our bodies shape our experience of the world, and our bodies influence what we design. How important are the physical differences between people? Can we model the physiological differences and use the models to adapt and personalize designs, user interfaces and artifacts? Within many disciplines Digital Human Models and Standard Observer Models are widely used and have proven to be very useful for modeling users and simulating humans. In this paper, we create personalized digital human models of perception (Individual Observer Models), particularly focused on how humans see. Individual Observer Models capture how our bodies shape our perceptions. Individual Observer Models are useful for adapting and personalizing user interfaces and artifacts to suit individual users' bodies and perceptions. We introduce and demonstrate an Individual Observer Model of human eyesight, which we use to simulate 3600 biologically valid human eyes. An evaluation of the simulated eyes finds that they see eye charts the same as humans. Also demonstrated is the Individual Observer Model successfully making predictions about how easy or hard it is to see visual information and visual designs. The ability to predict and adapt visual information to maximize how effective it is is an important problem in visual design and analytics.
Starting Page	25
Ending Page	37
Page Count	13
ISBN	9783642223617
ISSN	03029743
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-07-11
Publisher Place	Berlin, Heidelberg
Access Restriction	Subscribed
Subject Keyword	Physiology modeling Individual differences Virtual humans Human vision Computational user model Digital human model
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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