Subjectbook: Data Management and Visualization Methods for Affective Studies
| dc.contributor.advisor | Pavlidis, Ioannis T. | |
| dc.contributor.committeeMember | Deng, Zhigang | |
| dc.contributor.committeeMember | Chen, Guoning | |
| dc.contributor.committeeMember | Francis, David J. | |
| dc.creator | Taamneh, Salah 1983- | |
| dc.creator.orcid | 0000-0002-2414-0193 | |
| dc.date.accessioned | 2019-11-12T04:40:51Z | |
| dc.date.available | 2019-11-12T04:40:51Z | |
| dc.date.created | December 2016 | |
| dc.date.issued | 2016-12 | |
| dc.date.submitted | December 2016 | |
| dc.date.updated | 2019-11-12T04:40:51Z | |
| dc.description.abstract | Managing affective studies is very challenging for two main reasons. First, their life cycles consist of a series of cumbersome and time-consuming activities performed by different people. Second, investigators are increasingly overwhelmed by the size and complexity of data generated by affective studies. Such studies are longitudinal, and feature multimodal data, such as psychometric scores, imaging sequences, and signals from wearable sensors, with the latter streaming continuously for hours. The lack of tools for managing affective studies diminishes researchers' ability to finish collecting, analyzing, and sharing affective data sets within reasonable amounts of time and effort. Moreover, it is difficult to avoid human errors when some tasks, such as data collection and curation, are performed manually. Importantly, some critical tasks, such as quality assurance and exploratory data analysis, can not be performed efficiently unless using appropriate representations for presenting and displaying relationships among collected data. In this work, we introduce SubjectBook, an integrated tool for managing affective studies throughout their life cycles, from designing the experiments to analyzing and sharing the generated data. In this tool, data collection and curation phases have been automated and validated. This enables researchers to have access to their own data in real-time. Additionally, meaningful visual representations of data are provided. Various tools that were proposed to tackle this problem provide visualizations of the original data only; they do not support higher level abstractions. Uniquely, SubjectBook operates at three levels of abstraction, mirroring the stages of quantitative analysis in hypothesis-driven research. The top level uses a grid visualization to show the study's significant outcomes across subjects. The middle level summarizes, for each subject, context information along with the explanatory and response measurements in a construct reminiscent of an ID card. This enables the analyst to appreciate within subject phenomena. Finally, the bottom level brings together detailed information concerning the inner and outer state of human subjects along with their real-world interactions - a visualization fusion that supports cause and effect reasoning at the experimental session level. SubjectBook was evaluated using three case studies focused on driving behaviors. | |
| 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: Taamneh, Salah, Malcolm Dcosta, Kyeong-An Kwon, and Ioannis Pavlidis. "SubjectBook: Hypothesis-Driven Ubiquitous Visualization for Affective Studies." In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1483-1489. ACM, 2016. And in: Pavlidis, I., M. Dcosta, S. Taamneh, M. Manser, T. Ferris, R. Wunderlich, E. Akleman, and P. Tsiamyrtzis. "Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors." Scientific reports 6 (2016): 25651. | |
| dc.identifier.citation | Portions of this document appear in: Taamneh, Salah, Malcolm Dcosta, Kyeong-An Kwon, and Ioannis Pavlidis. "SubjectBook: Hypothesis-Driven Ubiquitous Visualization for Affective Studies." In Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1483-1489. ACM, 2016. And in: Pavlidis, I., M. Dcosta, S. Taamneh, M. Manser, T. Ferris, R. Wunderlich, E. Akleman, and P. Tsiamyrtzis. "Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors." Scientific reports 6 (2016): 25651. | |
| dc.identifier.uri | https://hdl.handle.net/10657/5387 | |
| 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 data sets | |
| dc.subject | Affective computing | |
| dc.subject | Data visualization | |
| dc.title | Subjectbook: Data Management and Visualization Methods for Affective Studies | |
| dc.type.dcmi | Text | |
| dc.type.genre | Thesis | |
| thesis.degree.college | College of Natural Sciences and Mathematics | |
| thesis.degree.department | Computer Science | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | University of Houston | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy |