Generalized Arousal Prediction through Machine Learning
| dc.contributor.advisor | Pavlidis, Ioannis T. | |
| dc.contributor.committeeMember | Vilalta, Ricardo | |
| dc.contributor.committeeMember | Johnsson, Lennart | |
| dc.contributor.committeeMember | Gutierrez-Osuna, Ricardo | |
| dc.creator | Zaman, Shaila | |
| dc.date.accessioned | 2023-06-02T18:04:51Z | |
| dc.date.created | December 2022 | |
| dc.date.issued | 2022-11-28 | |
| dc.date.updated | 2023-06-02T18:04:52Z | |
| dc.description.abstract | Arousal manifests across the spectrum of human activities, which is a key contributor to human behavior. The right level of arousal is an intriguing part of optimal performance with balancing healthy physical and mental conditions. For this reason, the study of arousal is of immense importance in human-machine interactions and human-human interactions. With the advent of ubiquitous unobtrusive physiological sensing, continuous measurement of arousal in the lab and in the wild became feasible. In this work, we investigate the question if there are underlying universal features of arousal across the problem space that would allow a unifying treatment. We define the problem space along two dimensions: the type of subject activity (cognitive and dexterous) vs. the realism of the activity (controlled and naturalistic). We use datasets from major studies in each of the four cells arising from this decomposition problem. We experiment with machine learning training configurations and feature architectures. Our ultimate goal is to identify the minimum type of training that leads to maximum universal performance for arousal detection. Our effort comes to fill a gap in the literature, which has been treating arousal in the context of specific study paradigms rather than generally. | |
| dc.description.department | Computer Science, Department of | |
| dc.format.digitalOrigin | born digital | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.citation | Portions of this document appear in: Zaman, Shaila. "Amanveer Wesley, Dennis Rodrigo Da Cunha Silva, Pradeep Buddharaju, Fatema Akbar, Ge Gao, Gloria Mark, Ricardo Gutierrez-Osuna, and Ioannis Pavlidis. 2019." Stress and productivity patterns of interrupted, synergistic, and antagonistic office activities. Scientific Data 6, no. 1 (2019): 1-18. | |
| dc.identifier.uri | https://hdl.handle.net/10657/14401 | |
| dc.language.iso | eng | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.subject | Affective computing | |
| dc.subject | Arousal | |
| dc.subject | Machine learning | |
| dc.subject | Driving | |
| dc.subject | Office tasks | |
| dc.subject | Thermal imaging | |
| dc.title | Generalized Arousal Prediction through Machine Learning | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| dcterms.accessRights | The full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period. | |
| local.embargo.lift | 2024-12-01 | |
| local.embargo.terms | 2024-12-01 | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Computer Science, Department of | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |