Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use

Title
Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use
Authors
김래현김호임창환
Keywords
internet gaming disorder; craving for gaming; machine learning; biosignal analysis; test-retest reliability
Issue Date
2019-08
Publisher
Sensors
Citation
VOL 19, NO 16
Abstract
Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse of addiction, and is extensively employed in addiction treatment. In a previous study, we proposed a machine-learning-based method to detect craving for gaming using multimodal physiological signals including photoplethysmogram, galvanic skin response, and electrooculogram. Our previous study demonstrated that a craving for gaming could be detected with a fairly high accuracy; however, as the feature vectors for the machine-learning-based detection of the craving of a userwereselectedbasedonthephysiologicaldataoftheuserthatwererecordedonthesameday,the eff ectivenessofthereuseofthemachinelearningmodelconstructedduringthepreviousexperiments, without any further calibration sessions, was still questionable. This “high test-retest reliability” characteristicisofimportanceforthepracticaluseofthecravingdetectionsystembecausethesystem needs to be repeatedly applied to the treatment processes as a tool to monitor the effi cacy of the treatment. Wepresentedshortvideoclipsofthreeaddictivegamestonineparticipants,duringwhich various physiological signals were recorded. This experiment was repeated with diff erent video clips on three diff erent days. Initially, we investigated the test-retest reliability of 14 features used in a craving detection system by computing the intraclass correlation coeffi cient. Then, we classifi ed whether each participant experienced a craving for gaming in the third experiment using various classifi ers— the support vector machine, k-nearest neighbors (kNN), centroid displacement-based kNN,lineardiscriminantanalysis,andrandomforest— trainedwiththephysiological
URI
http://pubs.kist.re.kr/handle/201004/62448
ISSN
1424-8220
Appears in Collections:
KIST Publication > Article
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