Open AccessCybersickness Analysis Using Symbolic Machine Learning Algorithms
Author(s) -
Thiago Porcino,
Daniela Trevisan,
Esteban Clua
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/svr_estendido.2021.17647
Subject(s) - entertainment , computer science , immersion (mathematics) , virtual reality , popularity , algorithm , machine learning , rank (graph theory) , human–computer interaction , artificial intelligence , multimedia , mathematics , psychology , art , social psychology , combinatorics , pure mathematics , visual arts
Virtual reality (VR) and head-mounted displays are constantly gaining popularity in various fields such as education, military, entertainment, and health. Although such technologies provide a high sense of immersion, they can also trigger symptoms of discomfort. This condition is called cybersickness (CS) and is quite popular in recent VR publications. This work proposes a novel experimental analysis using symbolic machine learning to rank potential causes of CS in VR games. We estimate CS causes and rank them according to their impact on the classical machine learning classification task. Experiments are performed using two VR games and 6 experimental protocols along with 37 valid samples from a total of 88 volunteers.