Designing a Unity Plugin to Predict Expected Affect in Games Using Biophilia
| dc.contributor.author | Zhang, Licheng | |
| dc.date.accessioned | 2022-09-28T16:57:19Z | |
| dc.date.available | 2022-09-28T16:57:19Z | |
| dc.date.issued | 2022-09-28 | |
| dc.date.submitted | 2022-09-23 | |
| dc.description.abstract | Video games can generate different emotional states and affective reactions, but it can sometimes be difficult for a game’s visual designer to predict the emotional response a player might experience when designing a game or game scene. In this thesis, I conducted a study to collect emotional responses to video game images. I then used that data to both confirm past research that suggests images can be used to predict affect and to build a model for predicting emotion that is specific to games. I built both a linear regression model and three neural network models to predict affective response and found that the neural net that leveraged ResNet-50 was most effective. I then incorporated that model into a Unity plug-in so that designers can use it to predict affect of players in real time. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/18834 | |
| dc.language.iso | en | en |
| dc.pending | false | |
| dc.publisher | University of Waterloo | en |
| dc.relation.uri | https://osf.io/pq8nd/?view_only=40a7012f090840f1a4443f19f9f0122e | en |
| dc.subject | affective computing | en |
| dc.subject | video games | en |
| dc.subject | machine learning | en |
| dc.title | Designing a Unity Plugin to Predict Expected Affect in Games Using Biophilia | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Mathematics | en |
| uws-etd.degree.department | David R. Cheriton School of Computer Science | en |
| uws-etd.degree.discipline | Computer Science | en |
| uws-etd.degree.grantor | University of Waterloo | en |
| uws-etd.embargo.terms | 0 | en |
| uws.comment.hidden | The image set used in the study can be accessed in an OSF repository. | en |
| uws.contributor.advisor | Hancock, Mark | |
| uws.contributor.advisor | Vogel, Daniel | |
| uws.contributor.affiliation1 | Faculty of Mathematics | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.published.city | Waterloo | en |
| uws.published.country | Canada | en |
| uws.published.province | Ontario | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |