An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 2; p. 496
• Main Authors: Bălan, Oana, Moise, Gabriela, Moldoveanu, Alin, Leordeanu, Marius, Moldoveanu, Florica
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 15.01.2020; MDPI
• Subjects: affective computing; Anxiety disorders; Cognition & reasoning; Deep learning; Discriminant analysis; emotional assessment; Emotions; Experiments; Fear & phobias; fear classification; feature selection; Machine learning; Medical research; Patients; Physiology; Software; Support vector machines; Virtual reality; fear classification; affective computing; feature selection; emotional assessment
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20020496

In this paper, we investigate various machine learning classifiers used in our Virtual Reality (VR) system for treating acrophobia. The system automatically estimates fear level based on multimodal sensory data and a self-reported emotion assessment. There are two modalities of expressing fear ratings: the 2-choice scale, where 0 represents relaxation and 1 stands for fear; and the 4-choice scale, with the following correspondence: 0—relaxation, 1—low fear, 2—medium fear and 3—high fear. A set of features was extracted from the sensory signals using various metrics that quantify brain (electroencephalogram—EEG) and physiological linear and non-linear dynamics (Heart Rate—HR and Galvanic Skin Response—GSR). The novelty consists in the automatic adaptation of exposure scenario according to the subject’s affective state. We acquired data from acrophobic subjects who had undergone an in vivo pre-therapy exposure session, followed by a Virtual Reality therapy and an in vivo evaluation procedure. Various machine and deep learning classifiers were implemented and tested, with and without feature selection, in both a user-dependent and user-independent fashion. The results showed a very high cross-validation accuracy on the training set and good test accuracies, ranging from 42.5% to 89.5%. The most important features of fear level classification were GSR, HR and the values of the EEG in the beta frequency range. For determining the next exposure scenario, a dominant role was played by the target fear level, a parameter computed by taking into account the patient’s estimated fear level.