Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use
- Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use
- 김래현; 김호; 임창환
- internet gaming disorder; craving for gaming; machine learning; biosignal analysis; test-retest reliability
- Issue Date
- VOL 19, NO 16
- 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
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