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HRV-Based Detection of Fear of Heights in a VR Environment

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Extended Reality (XR Salento 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14218))

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Abstract

In the present study, an electrocardiographic (ECG) -based system is proposed for the classification of three levels of fear of heights. A virtual reality (VR) environment was employed for the gradual exposure of the participants to the fear arousing stimuli. The VR scenario consists of a canyon in which a wooden lift brings the subjects to three different height levels. 20 subjects participated in the experimental activities and carried out three experimental sessions. The use of psychometric tools like the Acrophobia Questionnaire (AQ) and the Subjective Unit Of Distress (SUD) allowed to carry out an initial screening of the sample to assess the severity of fear of heights and the effectiveness of the VR environment in the induction of fear. According to the AQ and SUD, three clusters of subjects with different levels of acrophobia severity were identified. A 1-lead ECG recording was acquired during the exposure to the eliciting VR environment. Hearth rate variability (HRV) -related features were extracted from the ECG signal, specifically linear (statistical and geometric) and nonlinear features. Those features were input to different classifiers for discriminating three-levels of fear. Domain adaptation methods resulted effective in improving the generalizability of the results. On the contrary, clustering the subjects according to their acrophobia severity level did not impact on the classification performances. Average accuracies of \(40.9\,\pm \,5.9\) in the inter-subject setting and of \(43.8\,\pm \,7.6\) in the intra-subjects setting were achieved by employing the Linear Correlation Alignment (CORAL) DA method.

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Acknowledgment

The authors thank the Institute of Neural Engineering (BCI Lab) at the Graz University of Technology for the support in the research activities. The authors also thank the PhD Grant “AR4ClinicSur- Augmented Reality for Clinical Surgery" (INPS - National Social Security Institution - Italy). This work was carried out as part of the “ICT for Health" project, which was financially supported by the Italian Ministry of Education, University and Research (MIUR), under the initiative ‘Departments of Excellence’ (Italian Budget Law no. 232/2016), through an excellence grant awarded to the Department of Information Technology and Electrical Engineering of the University of Naples Federico II, Naples, Italy.

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Naples, Italy

    Pasquale Arpaia, Giovanna Mastrati & Rachele Robbio

  2. Interdepartmental Center for Research on Management and Innovation in Healthcare (CIRMIS), University of Naples Federico II, Naples, Italy

    Pasquale Arpaia

  3. Department of Applied Science and Technology, Polytechnic University of Turin, Turin, Italy

    Giovanni D’Errico

  4. Department of Engineering for Innovation University of Salento, Lecce, Italy

    Nicola Moccaldi

  5. Institute of Neural Engineering, Graz University of Technology, Graz, Austria

    Selina Christin Wriessenegger

  6. Idego Digital Psychology, Rome, Italy

    Simone Barbato

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  1. Pasquale Arpaia
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  2. Simone Barbato
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  3. Giovanni D’Errico
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  4. Giovanna Mastrati
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  5. Nicola Moccaldi
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  6. Rachele Robbio
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  7. Selina Christin Wriessenegger
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Correspondence to Giovanna Mastrati .

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Editors and Affiliations

  1. University of Salento, Lecce, Italy

    Lucio Tommaso De Paolis

  2. University of Naples Federico II, Naples, Italy

    Pasquale Arpaia

  3. CNR-STIIMA, Lecco, Italy

    Marco Sacco

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Arpaia, P. et al. (2023). HRV-Based Detection of Fear of Heights in a VR Environment. In: De Paolis, L.T., Arpaia, P., Sacco, M. (eds) Extended Reality. XR Salento 2023. Lecture Notes in Computer Science, vol 14218. Springer, Cham. https://doi.org/10.1007/978-3-031-43401-3_33

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  • DOI: https://doi.org/10.1007/978-3-031-43401-3_33

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  • Online ISBN: 978-3-031-43401-3

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