@Article{2019.100000141,
AUTHOR = {Shinjin Kang, Taiwoo Park},
TITLE = {Detecting Outlier Behavior of Game Player Players Using Multimodal Physiology Data},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {1},
PAGES = {205--214},
URL = {http://www.techscience.com/iasc/v26n1/39855},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2019.100000141}
}