Real-time classification of anxiety in virtual reality therapy using biosensors and a convolutional neural network

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dc.contributor.authorMevlevioğlu, Denizen
dc.contributor.authorTabirca, Sabinen
dc.contributor.authorMurphy, Daviden
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-11-26T15:05:56Z
dc.date.available2025-11-26T15:05:56Z
dc.date.issued2024-03-03en
dc.description.abstractVirtual Reality Exposure Therapy is a method of cognitive behavioural therapy that aids in the treatment of anxiety disorders by making therapy practical and cost-efficient. It also allows for the seamless tailoring of the therapy by using objective, continuous feedback. This feedback can be obtained using biosensors to collect physiological information such as heart rate, electrodermal activity and frontal brain activity. As part of developing our objective feedback framework, we developed a Virtual Reality adaptation of the well-established emotional Stroop Colour–Word Task. We used this adaptation to differentiate three distinct levels of anxiety: no anxiety, mild anxiety and severe anxiety. We tested our environment on twenty-nine participants between the ages of eighteen and sixty-five. After analysing and validating this environment, we used it to create a dataset for further machine-learning classification of the assigned anxiety levels. To apply this information in real-time, all of our information was processed within Virtual Reality. Our Convolutional Neural Network was able to differentiate the anxiety levels with a 75% accuracy using leave-one-out cross-validation. This shows that our system can accurately differentiate between different anxiety levels.en
dc.description.sponsorshipScience Foundation Ireland (Grant No. SFI 18/CRT/6222)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid131en
dc.identifier.citationMevlevioğlu, D., Tabirca, S. and Murphy, D. (2024) ‘Real-time classification of anxiety in virtual reality therapy using biosensors and a convolutional neural network’, Biosensors, 14(3), 131 (18pp). https://doi.org/10.3390/bios14030131en
dc.identifier.doi10.3390/bios14030131en
dc.identifier.eissn2079-6374en
dc.identifier.endpage18en
dc.identifier.issued3en
dc.identifier.journaltitleBiosensorsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/18283
dc.identifier.volume14en
dc.language.isoenen
dc.publisherMDPIen
dc.rights© 2024, the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectVRETen
dc.subjectBiosensorsen
dc.subjectHuman–computer interactionen
dc.subjectMachine learningen
dc.subjectAffective computingen
dc.subjectVRen
dc.subjectEDAen
dc.subjectPPGen
dc.subjectEEGen
dc.titleReal-time classification of anxiety in virtual reality therapy using biosensors and a convolutional neural networken
dc.typeArticle (peer-reviewed)en
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