EngageMon: Multi-modal engagement sensing for mobile games

• Main Authors: HUYNH, Sinh, KIM, Seungmin, KO, JeongGil, BALAN, Rajesh Krishna, LEE, Youngki
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2018
• 主題: Human-centered computing; Ubiquitous and mobile computing systems and tools; Applied computing; Computer games; Software Engineering
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/4057; https://ink.library.smu.edu.sg/context/sis_research/article/5060/viewcontent/EngageMon_Multi_modal_2018_afv.pdf

Understanding the engagement levels players have with a game is a useful proxy for evaluating the game design and user experience. This is particularly important for mobile games as an alternative game is always just an easy download away. However, engagement is a subjective concept and usually requires fine-grained highly disruptive interviews or surveys to determine accurately. In this paper, we present EngageMon, a first-of-its-kind system that uses a combination of sensors from the smartphone (touch events), a wristband (photoplethysmography and electrodermal activity sensor readings), and an external depth camera (skeletal motion information) to accurately determine the engagement level of a mobile game player. Our design was guided by feedback obtained from interviewing 22 mobile game developers, testers, and designers. We evaluated EngageMon using data collected from 64 participants (54 in a lab-setting study and another 10 in a more natural setting study) playing six games from three different categories including endless runner, 3D motorcycle racing, and casual puzzle. Using all three sets of sensors, EngageMon was able to achieve an average accuracy of 85% and 77% under cross-sample and cross-subject evaluations respectively. Overall, EngageMon can accurately determine the engagement level of mobiles users while they are actively playing a game.