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Cited 8 time in webofscience Cited 10 time in scopus
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Detection of Craving for Gaming in Adolescents with Internet Gaming Disorder Using Multimodal Biosignalsopen access

Authors
Kim, HodamHa, JihyeonChang, Won-DuPark, WanjooKim, LaehyunIm, Chang-Hwan
Issue Date
Jan-2018
Publisher
MDPI
Keywords
internet gaming disorder; internet game addiction; biosignal analysis; craving; machine learning
Citation
SENSORS, v.18, no.1, pp.1 - 13
Indexed
SCIE
SCOPUS
Journal Title
SENSORS
Volume
18
Number
1
Start Page
1
End Page
13
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/17869
DOI
10.3390/s18010102
ISSN
1424-8220
Abstract
The increase in the number of adolescents with internet gaming disorder (IGD), a type of behavioral addiction is becoming an issue of public concern. Teaching adolescents to suppress their craving for gaming in daily life situations is one of the core strategies for treating IGD. Recent studies have demonstrated that computer-aided treatment methods, such as neurofeedback therapy, are effective in relieving the symptoms of a variety of addictions. When a computer-aided treatment strategy is applied to the treatment of IGD, detecting whether an individual is currently experiencing a craving for gaming is important. We aroused a craving for gaming in 57 adolescents with mild to severe IGD using numerous short video clips showing gameplay videos of three addictive games. At the same time, a variety of biosignals were recorded including photoplethysmogram, galvanic skin response, and electrooculogram measurements. After observing the changes in these biosignals during the craving state, we classified each individual participant's craving/non-craving states using a support vector machine. When video clips edited to arouse a craving for gaming were played, significant decreases in the standard deviation of the heart rate, the number of eye blinks, and saccadic eye movements were observed, along with a significant increase in the mean respiratory rate. Based on these results, we were able to classify whether an individual participant felt a craving for gaming with an average accuracy of 87.04%. This is the first study that has attempted to detect a craving for gaming in an individual with IGD using multimodal biosignal measurements. Moreover, this is the first that showed that an electrooculogram could provide useful biosignal markers for detecting a craving for gaming.
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