20.500.12556/DiRROS-19676 Protocol of the study for predicting empathy during VR sessions using sensor data and machine learning 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. strojno učenje virtualna resničnost predvidevanje true false true PLOS ZDA Angleški jezik Slovenski jezik © 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. Neznano 2024-07-23 12:02:10 2024-07-23 12:02:10 2024-07-24 03:48:02 0000-00-00 00:00:00 2024 0 0 Opis vira z dne 23. 07. 2024; 19 str. July 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-10-11 2024-07-02 2024-07-18 004.7 1932-6203 10.1371/journal.pone.0307385 202535683 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0307385 1 e1f65be8-48c9-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=27174 RAZ_Kizhevska_Emilija_2024.pdf RAZ_Kizhevska_Emilija_2024.pdf 1 37ABA84E44BC85A6913B3E49963BB83C 197c64c7e70a7ef73d4af57fce70d14f7aa380e701641079797b668fc414e0d0 d562acd0-48ca-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=27175 Institut Jožef Stefan 0 0 0