Angela E. B. Stewart, Nigel Bosch, Huili Chen, P. Donnelly, S. D’Mello
{"title":"Where's Your Mind At?: Video-Based Mind Wandering Detection During Film Viewing","authors":"Angela E. B. Stewart, Nigel Bosch, Huili Chen, P. Donnelly, S. D’Mello","doi":"10.1145/2930238.2930266","DOIUrl":null,"url":null,"abstract":"Mind wandering (MW) is a ubiquitous phenomenon in which attention involuntarily shifts from task-related processing to task-unrelated thoughts. This study reports preliminary results of a video-based MW detector during film viewing. We collected training data in a study where participants self-reported when they caught themselves MW over the course of watching a 32.5 minute commercial film. We trained classification models on automatically extracted facial features and bodily movement and were able to detect MW with an F1 of .30. The model was successful in reproducing the MW distribution obtained from the self-reports","PeriodicalId":339100,"journal":{"name":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2930238.2930266","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
Mind wandering (MW) is a ubiquitous phenomenon in which attention involuntarily shifts from task-related processing to task-unrelated thoughts. This study reports preliminary results of a video-based MW detector during film viewing. We collected training data in a study where participants self-reported when they caught themselves MW over the course of watching a 32.5 minute commercial film. We trained classification models on automatically extracted facial features and bodily movement and were able to detect MW with an F1 of .30. The model was successful in reproducing the MW distribution obtained from the self-reports