is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This article proposes a method based on wavelet transform and neural networks for relating pupillary behavior to psychological stress. The proposed method was tested by recording pupil diameter and electrodermal activity during a simulated driving task. Self-report measures were also collected. Participants performed a baseline run with the driving task only, followed by three stress runs where they were required to perform the driving task along with sound alerts, the presence of two human evaluators, and both. Self-reports and pupil diameter successfully indexed stress manipulation, and significant correlations were found between these measures. However, electrodermal activity did not vary accordingly. After training, the four-way parallel neural network classifier could guess whether a given unknown pupil diameter signal came from one of the four experimental trials with 79.2% precision. The present study shows that pupil diameter signal has good discriminating power for stress detection.

International audienceNavigation 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

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. ABSTRACTThis paper tries to demonstrate a very new real-scale 3D system and sum up some firsthand and cutting edge results concerning multi-modal navigation and interaction interfaces. This work is part of the CALLISTO-SARI collaborative project. It aims at constructing an immersive room, developing a set of software tools and some navigation/interaction interfaces. Two sets of interfaces will be introduced here: 1) interaction devices, 2) natural language (speech processing) and user gesture. The survey on this system using subjective observation (Simulator Sickness Questionnaire, SSQ) and objective measurements (Center of Gravity, COG) shows that using natural languages and gesture-based interfaces induced less cyber-sickness comparing to device-based interfaces. Therefore, gesture-based is more efficient than device-based interfaces.