Features of the Postural Sway Signal as Indicators to Estimate and Predict Visually Induced Motion Sickness in Virtual Reality

Abstract : Navigation in a 3D immersive virtual environment is known to be prone to visually induced motion sickness (VIMS). Several psychophysiological and behavioral methods have been used to measure the level of sickness of a user, among which is postural instability. This study investigates all the features that can be extracted from the body postural sway: area of the projection of the center of gravity (mainly considered in past studies) and its shape and the frequency components of the signal's spectrum, in order to estimate and predict the occurrence of sickness in a typical virtual reality (VR) application. After modeling and simulation of the body postural sway, an experiment on 17 subjects identified a relation between the level of sickness and the variation both in the time and frequency domains of the body sway signal. The results support and go further into detail of findings of past studies using postural instability as an efficient indicator of sickness, giving insight to better monitor VIMS in a VR application.
Type de document :
Article dans une revue
International Journal of Human-Computer Interaction, Taylor & Francis, 2017, 33 (10), pp.771-785. 〈http://www.tandfonline.com/doi/abs/10.1080/10447318.2017.1286767?journalCode=hihc20〉. 〈10.1080/10447318.2017.1286767〉
Liste complète des métadonnées

Littérature citée [49 références]  Voir  Masquer  Télécharger

https://hal-univ-bourgogne.archives-ouvertes.fr/hal-01627120
Contributeur : Le2i - Université de Bourgogne <>
Soumis le : jeudi 21 décembre 2017 - 11:42:46
Dernière modification le : mercredi 12 septembre 2018 - 01:27:13

Fichier

LE2I_2017_IJHCI_CHARDONNET.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Jean-Rémy Chardonnet, Mohammad Ali Mirzaei, Frédéric Merienne. Features of the Postural Sway Signal as Indicators to Estimate and Predict Visually Induced Motion Sickness in Virtual Reality. International Journal of Human-Computer Interaction, Taylor & Francis, 2017, 33 (10), pp.771-785. 〈http://www.tandfonline.com/doi/abs/10.1080/10447318.2017.1286767?journalCode=hihc20〉. 〈10.1080/10447318.2017.1286767〉. 〈hal-01627120〉

Partager

Métriques

Consultations de la notice

198

Téléchargements de fichiers

64