Dr. Cynthia Kersey Current methods for researching and modeling
affect require peripheral devices, many of which are
uncomfortable for or disconcerting to the user. To more
realistically identify predictors of certain affective states,
we are developing an application that will use the multiple
sensors that are incorporated into mobile devices, such as
cameras, pressure sensors, accelerometers, and gyroscopes, to
collect data from user interactions with a task that is designed
to induce user frustration. Data collected from these
interactions will then be used to derive a predictive model of
frustration. Such a model could be incorporated into
applications such as intelligent tutoring systems, where
frustration has been shown to impede learning. Having knowledge
of the user affective state could lead to initiating actions to
alleviate stress and enhance learning.