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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, email

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.

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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.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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