Detecting Outlier Behavior of Game Player Players Using Multimodal Physiology Data
- Authors
- Kang, Shinjin; Park, Taiwoo
- Issue Date
- Mar-2020
- Publisher
- TSI PRESS
- Keywords
- Physiology multimodal system; Game behavior analysis
- Citation
- INTELLIGENT AUTOMATION AND SOFT COMPUTING, v.26, no.1, pp.205 - 214
- Journal Title
- INTELLIGENT AUTOMATION AND SOFT COMPUTING
- Volume
- 26
- Number
- 1
- Start Page
- 205
- End Page
- 214
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/12410
- DOI
- 10.31209/2019.100000141
- ISSN
- 1079-8587
- Abstract
- This paper describes an outlier detection system based on a multimodal physiology data clustering algorithm in a PC gaming environment. The goal of this system is to provide information on a game player's abnormal behavior with a bio-signal analysis. Using this information, the game platform can easily identify players with abnormal behavior in specific events. To do this, we propose a mouse device that measures the wearer's skin conductivity, temperature, and motion. We also suggest a Dynamic Time Warping (DTW) based clustering algorithm. The developed system examines the biometric information of 50 players in a bullet dodge game. This paper confirms that a mouse coupled with a physiology multimodal system is useful for detecting outlier behavior of game players in a non-intrusive way.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - School of Games > Game Software Major > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.