Oscar I. Caldas, M. Mauledoux, O. Avilés, C. R. Guerrero
{"title":"Behavioral and Psychophysiological Measures of Engagement During Dynamic Difficulty Adjustment in Immersive Virtual Reality","authors":"Oscar I. Caldas, M. Mauledoux, O. Avilés, C. R. Guerrero","doi":"10.3897/jucs.89412","DOIUrl":null,"url":null,"abstract":"Dynamically Difficulty Adjustment (DDA) has been widely used to preserve engagement in serious and entertaining games, reach better learning, and enhance user performance. A variety of studies suggests that in DDA, task performance (score) rises until hitting a plateau associated with the skill level. However, the sense of engagement is individual and context-dependent, and the effect of DDA on other engagement indicators for immersive virtual environments is still unclear. This study measured objective indicators of engagement while study subjects played an immersive virtual game with DDA to find evidence of dynamic response, similar to game performance. Participants were demanded to perform repetitive upper-limb motions while recording the following indicators: Response Latency as perceptive engagement (elapsed time after sensory stimulus), Exercise Intensity as motion engagement (hand velocity), and psychophysiological responses as emotional engagement (Heart Rate, Skin Conductance, and Respiratory Rate). In addition, 30 features were extracted from the signals to evaluate their variations between time windows. Results indicate that response latency, vertical hand velocity, and heart rate showed significant changes over time during DDA and grew until hitting a plateau, i.e., at the subject's maximum performance. Moreover, some of the features extracted from the signals showed significant differences between time windows, and having strong correlation with the mean of score: max Latency, min velocity on the Y-axis, and mean heart rate, which suggest a promising application for evaluating changes in engagement between different experimental conditions in VR.","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"1 1","pages":"16-33"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Univers. Comput. Sci.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3897/jucs.89412","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Dynamically Difficulty Adjustment (DDA) has been widely used to preserve engagement in serious and entertaining games, reach better learning, and enhance user performance. A variety of studies suggests that in DDA, task performance (score) rises until hitting a plateau associated with the skill level. However, the sense of engagement is individual and context-dependent, and the effect of DDA on other engagement indicators for immersive virtual environments is still unclear. This study measured objective indicators of engagement while study subjects played an immersive virtual game with DDA to find evidence of dynamic response, similar to game performance. Participants were demanded to perform repetitive upper-limb motions while recording the following indicators: Response Latency as perceptive engagement (elapsed time after sensory stimulus), Exercise Intensity as motion engagement (hand velocity), and psychophysiological responses as emotional engagement (Heart Rate, Skin Conductance, and Respiratory Rate). In addition, 30 features were extracted from the signals to evaluate their variations between time windows. Results indicate that response latency, vertical hand velocity, and heart rate showed significant changes over time during DDA and grew until hitting a plateau, i.e., at the subject's maximum performance. Moreover, some of the features extracted from the signals showed significant differences between time windows, and having strong correlation with the mean of score: max Latency, min velocity on the Y-axis, and mean heart rate, which suggest a promising application for evaluating changes in engagement between different experimental conditions in VR.