ミカミ　コウジ   Koji Mikami   三上　浩司    所属   メディア学部 メディア学科    職種   教授
言語種別	英語
発行・発表の年月	2020/06
形態種別	学術論文
査読	査読あり
標題	Pressure Sensitivity Pattern Analysis Using Machine Learning Methods
執筆形態	共著
掲載誌名	IIEEJ Transactions on Image Electronics and Visual Computing
掲載区分	国外
出版社・発行元	The Institute of Image Electronics Engineers of Japan
巻・号・頁	8(1),pp.27-34
総ページ数	8
著者・共著者	Henry FERNANDEZ, Koji MIKAMI, Kunio KONDO
概要	As a consequence of a lack of balance between the levels of difficulty of a game and the players’ skills, the resulting experience for players might be frustrating (too difficult) or boring (too easy). Players having a bad experience could impact game creators negatively, leading to irreparable damage. The main motivation of this study was to find effective ways to reduce the gap between skills and difficulty,to help developers create a more suitable experience for players. This paper shows the results of applying Neural Networks and Support Vector Machines to data collected from the pressure exerted to a gamepad’s button with the purpose of finding patterns that can help predict: difficulty, fun, frustration, boredom,valence, arousal and dominance at a determined time. We obtained results with an accuracy of 83.64 % when predicting boredom, around 70 % of accuracy classifying frustration, fun, difficulty and dominance.
DOI	https://doi.org/10.11371/tievciieej.8.1_27
外部リンクURL	https://www.jstage.jst.go.jp/article/tievciieej/8/1/8_27/_article/-char/ja/