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INCORPORATING EMOTION RECOGNITION IN CO-ADAPTIVE SYSTEMS

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Date Issued:
2022
Abstract/Description:
The collaboration between human and computer systems has grown astronomically over the past few years. The ability of software systems adapting to human's input is critical in the symbiosis of human-system co-adaptation, where human and software-based systems work together in a close partnership to achieve synergetic goals. However, it is not always clear what kinds of human’s input should be considered to enhance the effectiveness of human and system co-adaptation. To address this issue, this research describes an approach that focuses on incorporating human emotion to improve human-computer co-adaption. The key idea is to provide a formal framework that incorporates human emotions as a foundation for explainability into co-adaptive systems, especially, how software systems recognize human emotions and adapt the system’s behaviors accordingly. Detecting and recognizing optimum human emotion is a first step towards human and computer symbiosis. As the first step of this research, we conduct a comparative review for a number of technologies and methods for emotion recognition. Specifically, testing the detection accuracy of facial expression recognition of different cloud-services, algorithms, and methods. Secondly, we study the application of emotion recognition within the areas of e-learning, robotics, and explainable artificial intelligence (XAI). We propose a formal framework that incorporates human emotions into an adaptive e-learning system, to create a more personalized learning experience for higher quality of learning outcomes. In addition, we propose a framework for a co-adaptive Emotional Support Robot. This human-centric framework adopts a reinforced learning approach where the system assesses its own emotional re-actions.
Title: INCORPORATING EMOTION RECOGNITION IN CO-ADAPTIVE SYSTEMS.
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Name(s): Al-Omair, Osamah M. , author
Huang, Shihong , Thesis advisor
Florida Atlantic University, Degree grantor
Department of Computer and Electrical Engineering and Computer Science
College of Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Created: 2022
Date Issued: 2022
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 152 p.
Language(s): English
Abstract/Description: The collaboration between human and computer systems has grown astronomically over the past few years. The ability of software systems adapting to human's input is critical in the symbiosis of human-system co-adaptation, where human and software-based systems work together in a close partnership to achieve synergetic goals. However, it is not always clear what kinds of human’s input should be considered to enhance the effectiveness of human and system co-adaptation. To address this issue, this research describes an approach that focuses on incorporating human emotion to improve human-computer co-adaption. The key idea is to provide a formal framework that incorporates human emotions as a foundation for explainability into co-adaptive systems, especially, how software systems recognize human emotions and adapt the system’s behaviors accordingly. Detecting and recognizing optimum human emotion is a first step towards human and computer symbiosis. As the first step of this research, we conduct a comparative review for a number of technologies and methods for emotion recognition. Specifically, testing the detection accuracy of facial expression recognition of different cloud-services, algorithms, and methods. Secondly, we study the application of emotion recognition within the areas of e-learning, robotics, and explainable artificial intelligence (XAI). We propose a formal framework that incorporates human emotions into an adaptive e-learning system, to create a more personalized learning experience for higher quality of learning outcomes. In addition, we propose a framework for a co-adaptive Emotional Support Robot. This human-centric framework adopts a reinforced learning approach where the system assesses its own emotional re-actions.
Identifier: FA00013926 (IID)
Degree granted: Dissertation (Ph.D.)--Florida Atlantic University, 2022.
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): Includes bibliography.
Subject(s): Emotion recognition
Human-computer interaction
Affective Computing
Persistent Link to This Record: http://purl.flvc.org/fau/fd/FA00013926
Use and Reproduction: Copyright © is held by the author with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.