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TOWARD BUILDING A SOCIAL ROBOT WITH AN EMOTION-BASED INTERNAL CONTROL AND EXTERNAL COMMUNICATION TO ENHANCE HUMAN-ROBOT INTERACTION
Title
TOWARD BUILDING A SOCIAL ROBOT WITH AN EMOTION-BASED INTERNAL CONTROL AND EXTERNAL COMMUNICATION TO ENHANCE HUMAN-ROBOT INTERACTION.
Creator
Marpaung, Andreas, Lisetti, Christine, University of Central Florida
Abstract / Description
In this thesis, we aim at modeling some aspects of the functional role of emotions on an autonomous embodied agent. We begin by describing our robotic prototype, Cherry--a robot with the task of being a tour guide and an office assistant for the Computer Science Department at the University of Central Florida. Cherry did not have a formal emotion representation of internal states, but did have the ability to express emotions through her multimodal interface. The thesis presents the results of...
Show moreIn this thesis, we aim at modeling some aspects of the functional role of emotions on an autonomous embodied agent. We begin by describing our robotic prototype, Cherry--a robot with the task of being a tour guide and an office assistant for the Computer Science Department at the University of Central Florida. Cherry did not have a formal emotion representation of internal states, but did have the ability to express emotions through her multimodal interface. The thesis presents the results of a survey we performed via our social informatics approach where we found that: (1) the idea of having emotions in a robot was warmly accepted by Cherry's users, and (2) the intended users were pleased with our initial interface design and functionalities. Guided by these results, we transferred our previous code to a human-height and more robust robot--Petra, the PeopleBot™--where we began to build a formal emotion mechanism and representation for internal states to correspond to the external expressions of Cherry's interface. We describe our overall three-layered architecture, and propose the design of the sensory motor level (the first layer of the three-layered architecture) inspired by the Multilevel Process Theory of Emotion on one hand, and hybrid robotic architecture on the other hand. The sensory-motor level receives and processes incoming stimuli with fuzzy logic and produces emotion-like states without any further willful planning or learning. We will discuss how Petra has been equipped with sonar and vision for obstacle avoidance as well as vision for face recognition, which are used when she roams around the hallway to engage in social interactions with humans. We hope that the sensory motor level in Petra could serve as a foundation for further works in modeling the three-layered architecture of the Emotion State Generator.
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Date Issued
2004
Identifier
CFE0000286, ucf:46228
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000286
ADAPTIVE INTELLIGENT USER INTERFACES WITH EMOTION RECOGNITION
Title
ADAPTIVE INTELLIGENT USER INTERFACES WITH EMOTION RECOGNITION.
Creator
NASOZ, FATMA, Christine Lisetti, Dr L., University of Central Florida
Abstract / Description
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...
Show moreThe 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.
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Date Issued
2004
Identifier
CFE0000126, ucf:46201
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000126
Context-Centric Affect Recognition From Paralinguistic Features of Speech
Title
Context-Centric Affect Recognition From Paralinguistic Features of Speech.
Creator
Marpaung, Andreas, Gonzalez, Avelino, DeMara, Ronald, Sukthankar, Gita, Wu, Annie, Lisetti, Christine, University of Central Florida
Abstract / Description
As the field of affect recognition has progressed, many researchers have shifted from having unimodal approaches to multimodal ones. In particular, the trends in paralinguistic speech affect recognition domain have been to integrate other modalities such as facial expression, body posture, gait, and linguistic speech. Our work focuses on integrating contextual knowledge into paralinguistic speech affect recognition. We hypothesize that a framework to recognize affect through paralinguistic...
Show moreAs the field of affect recognition has progressed, many researchers have shifted from having unimodal approaches to multimodal ones. In particular, the trends in paralinguistic speech affect recognition domain have been to integrate other modalities such as facial expression, body posture, gait, and linguistic speech. Our work focuses on integrating contextual knowledge into paralinguistic speech affect recognition. We hypothesize that a framework to recognize affect through paralinguistic features of speech can improve its performance by integrating relevant contextual knowledge. This dissertation describes our research to integrate contextual knowledge into the paralinguistic affect recognition process from acoustic features of speech. We conceived, built, and tested a two-phased system called the Context-Based Paralinguistic Affect Recognition System (CxBPARS). The first phase of this system is context-free and uses the AdaBoost classifier that applies data on the acoustic pitch, jitter, shimmer, Harmonics-to-Noise Ratio (HNR), and the Noise-to-Harmonics Ratio (NHR) to make an initial judgment about the emotion most likely exhibited by the human elicitor. The second phase then adds context modeling to improve upon the context-free classifications from phase I. CxBPARS was inspired by a human subject study performed as part of this work where test subjects were asked to classify an elicitor's emotion strictly from paralinguistic sounds, and then subsequently provided with contextual information to improve their selections. CxBPARS was rigorously tested and found to, at the worst case, improve the success rate from the state-of-the-art's 42% to 53%.
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Date Issued
2019
Identifier
CFE0007836, ucf:52831
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007836