dc.contributor.advisor | Collins, Christopher | |
dc.contributor.author | Panwar, Prateek | |
dc.date.accessioned | 2018-09-12T13:08:12Z | |
dc.date.accessioned | 2022-03-29T17:25:48Z | |
dc.date.available | 2018-09-12T13:08:12Z | |
dc.date.available | 2022-03-29T17:25:48Z | |
dc.date.issued | 2018-07-01 | |
dc.identifier.uri | https://hdl.handle.net/10155/956 | |
dc.description.abstract | The thesis demonstrates an idea for helping users in visual analytic tasks by investigating some critical steps required for providing recommendations. The proposed model uses mixed-initiative interaction approach by detecting users’ negative emotions, caused by the visual analytic tasks, as a cue to generate useful guidance. For building a negative emotion detection classifier, I have created a dataset from 28 participants carrying out intentionally difficult visualization tasks and collected their emotional responses using multiple biosensors. I used this dataset to built a real-time emotion detection model which predicts mental state in every 4s. Next, the visualization tool uses the detected emotions to generate a recommendation and decide when to intervene. Additionally, the system also adapts intrusion level by analyzing long-term emotions, and decide the best way to show the help. Finally, I have concluded this work by discussing the design space of interventions for providing just-in-time assistance in visual analytics. | en |
dc.description.sponsorship | University of Ontario Institute of Technology | en |
dc.language.iso | en | en |
dc.subject | Affective computing | en |
dc.subject | Information visualization | en |
dc.subject | Recommendation system | en |
dc.subject | Eye tracking | en |
dc.subject | GSR | en |
dc.title | Recommendations in visual analytics using emotions : a mixed-initiative interaction approach | en |
dc.type | Thesis | en |
dc.degree.level | Master of Science (MSc) | en |
dc.degree.discipline | Computer Science | en |