Virtual reality (driving simulators) tends to generalize for the study of human behavior in mobility. It is thus crucial to ensure that perception of space and motion is little or not affected by the virtual environment (VE). The aim of this study was to determine a metrics of distance perception in VEs and whether this metrics depends on interactive factors: stereoscopy and motion parallax. After a training session, participants were asked, while driving, to estimate the relative location (5 to 80 m) of a car on the same road. The overall results suggest that distance perception in this range does not depend on interactive factors. In average, as generally reported, subjects underestimated the distances whatever the vision conditions. However, the study revealed a large interpersonal variability: two profiles of participants were defined, those who quite accurately perceived distances in VR and those who underestimated distances as usually reported. Overall, this classification was correlated to the level of performance of participants during the training phase. Furthermore, learning performance is predictive of the behavior of participants.

Despite growing interest over the decades, the question of estimating cognitive workload of operators involved in complex multitask operations, such as helicopter pilots, remains a key issue. One of the main difficulties facing workload inference models is that no single specific indicator of workload exists, so that multiple sources of information have to be inputted to the model. The question then arises as to the nature and the quantity of features to be used for increasing model performance. In this research, done in cooperation with Eurocopter, the effectiveness of physiological, psychological, and cognitive features for estimating helicopter pilots' workload was systematically investigated, using Bayesian networks (BNs). The study took place in two different contexts: a constrained laboratory situation with low ecological validity and a more realistic and challenging situation relying on virtual reality. The constrained conditions of the laboratory study allowed us for testing various combinations of entropy-based physiological, cognitive, and affect features as inputs of BN models. These three different kinds of features are shown to carry complementary information that can be used with advantage by the model. The results also suggest that increasing the number of physiological inputs improves the model performance. The second study aimed at challenging some of these conclusions in a more ecological context, by using the NH90 full-flight simulator of the Helisim company. The results emphasize the problem of accessing the ground truth, as well as the need for an efficient feature selection or extraction step prior to the classification step.

This paper presents an approach based on Bayesian Networks to estimate the workload of operators. The models take as inputs the entropy of different number of physiological features, as well as a cognitive feature (reaction time to a secondary task). They output the workload variation of subjects involved in successive tasks demanding different levels of cognitive resources. The performances of the classifiers are discussed in term of two criteria to be jointly optimized: the diversity, i.e. the ability of the model to perform on different subjects, and the accuracy, i.e., how close from the (subjectively estimated) workload level the model prediction is.

Shape and mechanical structure of instruments play a large part in the lack of ergonomics during laparoscopic surgery. Providing intra-abdominal mobility and rethinking handles design are two solutions to increase comfort and precision of gestures. Based on previous work that determined the optimal intra-abdominal kinematics, this study analyses the influence of handle design on both gesture and ergonomic performance. A virtual reality laparoscopic simulator was developed to perform an experimental comparison between two novel robotized instruments and standard ones. A group of 10 surgeons and 6 researchers in robotics carried out two representative surgical tasks with each instrument. Based on instrument and arm segments tracking, a gesture performance index and an ergonomic performance index were computed. The study demonstrates that distal mobilities combined with improved handle design and integration increase ergonomic level during laparoscopy and facilitate complex gestures.

While modern dynamic driving simulators equipped with six degrees-of-freedom (6-DOF) hexapods and X-Y platforms have improved realism, mechanical limitations prevent them from offering a fully realistic driving experience. Solutions are often sought in the ”washout” algorithm, with linear accelerations simulated by an empirically chosen combination of translation and tilt-coordination, based on the incapacity of otolith organs to distinguish between inclination of the head and linear acceleration. In this study, we investigated the most effective combination of tilt and translation to provide a realistic perception of movement. We tested 3 different braking intensities (deceleration), each with 5 inverse proportional tilt/translation ratios. Subjects evaluated braking intensity using an indirect method corresponding to a 2-Alternative-Forced-Choice Paradigm. We find that perceived intensity of braking depends on the tilt/translation ratio used: for small and average decelerations (0.6 and 1.0m/s2), increased tilt yielded an increased overestimation of braking, inverse proportionally with intensity; for high decelerations (1.4m/s2), on half the conditions braking was overestimated with more tilt than translation and underestimated with more translation than tilt. We define a mathematical function describing the relationship between tilt, translation and the desired level of deceleration, intended as a supplement to motion cueing algorithms, that should improve the realism of driving simulations.