Next Article in Journal
Field Deployable Method for Gold Detection Using Gold Pre-Concentration on Functionalized Surfaces
Previous Article in Journal
Photolithography Fabricated Spacer Arrays Offering Mechanical Strengthening and Oil Motion Control in Electrowetting Displays
Open AccessArticle

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

1
Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, Bucharest 060042, Romania
2
Department of Computer Science, Information Technology, Mathematics and Physics, Petroleum-Gas University of Ploiesti, Ploiesti 100680, Romania
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(2), 496; https://doi.org/10.3390/s20020496
Received: 29 October 2019 / Revised: 7 January 2020 / Accepted: 13 January 2020 / Published: 15 January 2020
(This article belongs to the Section Biomedical Sensors)
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.
Keywords: fear classification; emotional assessment; feature selection; affective computing fear classification; emotional assessment; feature selection; affective computing
Show Figures

Figure 1

MDPI and ACS Style

Bălan, O.; Moise, G.; Moldoveanu, A.; Leordeanu, M.; Moldoveanu, F. An Investigation of Various Machine and Deep Learning Techniques Applied in Automatic Fear Level Detection and Acrophobia Virtual Therapy. Sensors 2020, 20, 496.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop