Abstract
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.
Highlights
According to statistics, 13% of the world’s population is affected by phobias, a type of anxiety disorder manifested by an extreme and irrational fear towards an object or a situation. 275 million people suffer from anxiety disorders throughout the world and anxiety disorders are ranked as the6th-most common contributors to global disability [1]
We focus our study on the machine learning (ML) and deep learning (DL) methods, which automatically classify fear level using physiological data
We extended our work by defining a ML-based decision support that relies on various ML techniques such as Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Random Forest (RF) and 4 deep neural network models (Figure 1)
Summary
13% of the world’s population is affected by phobias, a type of anxiety disorder manifested by an extreme and irrational fear towards an object or a situation. 275 million people suffer from anxiety disorders throughout the world and anxiety disorders are ranked as the6th-most common contributors to global disability [1]. 13% of the world’s population is affected by phobias, a type of anxiety disorder manifested by an extreme and irrational fear towards an object or a situation. 275 million people suffer from anxiety disorders throughout the world and anxiety disorders are ranked as the. 6th-most common contributors to global disability [1]. Phobias are classified into social phobias (fear of relating to others or speaking in public) and specific phobias (generated by particular objects or situations). Social phobias affect people of all ages, though they usually start to manifest in adolescence. 17% of people with social phobias develop depression. With regard to specific phobias, a significant percent (15–20%) of the world’s population faces one specific phobia during their lifetime [3].
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