Open Access
ARTICLE
Detecting Outlier Behavior of Game Player Players Using Multimodal Physiology Data
Shinjin Kang1, Taiwoo Park2,*
1 School of Games, Hongik University, South Korea
1 Department of Media and Information, Michigan State University, USA
2 Department of Engineering and Computer Science, Seattle Pacific University, USA
* Corresponding Author: Taiwoo Park,
Intelligent Automation & Soft Computing 2020, 26(1), 205-214. https://doi.org/10.31209/2019.100000141
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.
Keywords
Cite This Article
S. Kang and T. Park, "Detecting outlier behavior of game player players using multimodal physiology data,"
Intelligent Automation & Soft Computing, vol. 26, no.1, pp. 205–214, 2020.