Keywords
Adaptive intelligent user interfaces, Affective computing, Emotion recognition, Pattern recognition, User modeling
Abstract
The focus of this dissertation is on creating Adaptive Intelligent User Interfaces to facilitate enhanced natural communication during the Human-Computer Interaction by recognizing users' affective states (i.e., emotions experienced by the users) and responding to those emotions by adapting to the current situation via an affective user model created for each user. Controlled experiments were designed and conducted in a laboratory environment and in a Virtual Reality environment to collect physiological data signals from participants experiencing specific emotions. Algorithms (k-Nearest Neighbor [KNN], Discriminant Function Analysis [DFA], Marquardt-Backpropagation [MBP], and Resilient Backpropagation [RBP]) were implemented to analyze the collected data signals and to find unique physiological patterns of emotions. Emotion Elicitation with Movie Clips Experiment was conducted to elicit Sadness, Anger, Surprise, Fear, Frustration, and Amusement from participants. Overall, the three algorithms: KNN, DFA, and MBP, could recognize emotions with 72.3%, 75.0%, and 84.1% accuracy, respectively. Driving Simulator experiment was conducted to elicit driving-related emotions and states (panic/fear, frustration/anger, and boredom/sleepiness). The KNN, MBP and RBP Algorithms were used to classify the physiological signals by corresponding emotions. Overall, KNN could classify these three emotions with 66.3%, MBP could classify them with 76.7% and RBP could classify them with 91.9% accuracy. Adaptation of the interface was designed to provide multi-modal feedback to the users about their current affective state and to respond to users' negative emotional states in order to decrease the possible negative impacts of those emotions. Bayesian Belief Networks formalization was employed to develop the User Model to enable the intelligent system to appropriately adapt to the current context and situation by considering user-dependent factors, such as: personality traits and preferences.
Notes
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Graduation Date
2004
Semester
Summer
Advisor
Lisetti, Christine L.
Degree
Doctor of Philosophy (Ph.D.)
College
College of Engineering and Computer Science
Department
Computer Science
Degree Program
Engineering and Computer Science
Format
application/pdf
Identifier
CFE0000126
URL
http://purl.fcla.edu/fcla/etd/CFE0000126
Language
English
Release Date
January 2004
Length of Campus-only Access
None
Access Status
Doctoral Dissertation (Open Access)
Subjects
Adaptive intelligent user interfaces; Affective computing; Dissertations, Academic -- Engineering and Computer Science
STARS Citation
Nasoz, Fatma, "Adaptive Intelligent User Interfaces With Emotion Recognition" (2004). Electronic Theses and Dissertations. 146.
https://stars.library.ucf.edu/etd/146