Identifying emotional states through keystroke dynamics
| dc.contributor.advisor | Mandryk, Regan | en_US |
| dc.contributor.committeeMember | Gutwin, Carl | en_US |
| dc.contributor.committeeMember | McCalla, Gord | en_US |
| dc.contributor.committeeMember | Bolton, Ron | en_US |
| dc.creator | Epp, Clayton Charles | en_US |
| dc.date.accessioned | 2010-08-31T13:10:27Z | en_US |
| dc.date.accessioned | 2013-01-04T04:56:01Z | |
| dc.date.available | 2011-09-09T08:00:00Z | en_US |
| dc.date.available | 2013-01-04T04:56:01Z | |
| dc.date.created | 2010-07 | en_US |
| dc.date.issued | 2010-07 | en_US |
| dc.date.submitted | July 2010 | en_US |
| dc.description.abstract | The ability to recognize emotions is an important part of building intelligent computers. Extracting the emotional aspects of a situation could provide computers with a rich context to make appropriate decisions about how to interact with the user or adapt the system response. The problem that we address in this thesis is that the current methods of determining user emotion have two issues: the equipment that is required is expensive, and the majority of these sensors are invasive to the user. These problems limit the real-world applicability of existing emotion-sensing methods because the equipment costs limit the availability of the technology, and the obtrusive nature of the sensors are not realistic in typical home or office settings. Our solution is to determine user emotions by analyzing the rhythm of an individual‘s typing patterns on a standard keyboard. Our keystroke dynamics approach would allow for the uninfluenced determination of emotion using technology that is in widespread use today. We conducted a field study where participants‘ keystrokes were collected in situ and their emotional states were recorded via self reports. Using various data mining techniques, we created models based on 15 different emotional states. With the results from our cross-validation, we identify our best-performing emotional state models as well as other emotional states that can be explored in future studies. We also provide a set of recommendations for future analysis on the existing data set as well as suggestions for future data collection and experimentation. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10388/etd-08312010-131027 | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | keystroke dynamics | en_US |
| dc.subject | affective computing | en_US |
| dc.title | Identifying emotional states through keystroke dynamics | en_US |
| dc.type.genre | Thesis | en_US |
| dc.type.material | text | en_US |
| thesis.degree.department | Computer Science | en_US |
| thesis.degree.discipline | Computer Science | en_US |
| thesis.degree.grantor | University of Saskatchewan | en_US |
| thesis.degree.level | Masters | en_US |
| thesis.degree.name | Master of Science (M.Sc.) | en_US |