| Title: | Protocol of the study for predicting empathy during VR sessions using sensor data and machine learning |
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| Authors: | ID Kizhevska, Emilija, Institut Jožef Stefan (Author)
ID Šparemblek, Kristina, Institut Jožef Stefan (Author)
ID Luštrek, Mitja, Institut Jožef Stefan (Author) |
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| Files: |  URL - Source URL, visit https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0307385
 PDF - Presentation file, download (1,12 MB)
MD5: 37ABA84E44BC85A6913B3E49963BB83C
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | Virtual reality (VR) technology is often referred to as the ‘ultimate empathy machine’ due to its capability to immerse users in alternate perspectives and environments beyond their immediate physical reality. In this study, participants will be immersed in 3-dimensional 360˚ VR videos where actors express different emotions (sadness, happiness, anger, and anxiousness). The primary objective is to investigate the potential relationship between participants’ empathy levels and the changes in their physiological attributes. The empathy levels will be self-reported with questionnaires, and physiological attributes will be measured using different sensors. The main outcome of the study will be a machine learning model to predict a person’s empathy level based on their physiological responses while watching VR videos. Despite the existence of established methodologies and metrics in research and clinical domains, our aim is to contribute to addressing the gap of a universally accepted “gold standard” for assessing empathy. Additionally, we expect to deepen our understanding of the relationship between different emotions and psychological attributes, gender differences in empathy, and the impact of narrative context on empathic responses. |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 11.10.2023 |
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| Article acceptance date: | 02.07.2024 |
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| Publication date: | 18.07.2024 |
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| Publisher: | PLOS |
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| Year of publishing: | 2024 |
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| Number of pages: | 19 str. |
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| Numbering: | July |
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| Source: | ZDA |
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| PID: | 20.500.12556/DiRROS-19676  |
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| UDC: | 004.7 |
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| ISSN on article: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0307385 |
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| COBISS.SI-ID: | 202535683 |
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| Copyright: | © 2024 Kizhevska et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited. |
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| Note: | Opis vira z dne 23. 07. 2024;
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| Publication date in DiRROS: | 23.07.2024 |
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| Views: | 274 |
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| Downloads: | 119 |
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