In shared virtual spaces, users tend to mimic real-world social behaviors, such as maintaining interpersonal distances and avoiding collisions. During remote collaboration, these behaviors can limit movement and positioning, despite users are not co-located. Prior work found that avatar transparency reduces users’ positioning constraints induced by social behaviors, as long as social presence between collaborators. Our goal was to develop a system that enhances navigation freedom without significantly increasing social presence. We designed a transparency management system based on interpersonal distances and collision avoidance. A user study involving groups of three remote collaborators indicated that our system reduced the distances between users compared to fully opaque avatars without any significant difference in social presence.

Collaborative Augmented Reality (CAR) offers disruptive ways for people to collaborate. However, this emerging technology must improve its acceptance, efficiency, and usability to scale up and, for example, support augmented operations executed by technicians. This paper presents our CAR system and its experimentation during a cooperative puzzle-solving task. Our system provides collaborators with a shared virtual space allowing verbal and non-verbal interpersonal communications, and intuitive interactions with shared virtual replicas of real objects. Our system also integrates avatars embodied by remote users. We conducted a dual-user study comparing colocated and remote solving of a puzzle virtual replica with its real solving. We evaluated task performance, collaboration, mutual awareness, spatial presence, and copresence, usability, and preference. We found that, if real is preferred and more efficient than our CAR system, CAR is reaching favorable usability levels. We also found that remote augmented reality including full-body avatars offers similar results to colocated augmented reality. This preliminary work paves the way for future research aiming to support and enhance the design and making of Collaborative Augmented Reality systems dedicated to augmented operations.

Users would experience individually different sickness symptoms during or after navigating through an immersive virtual environment, generally known as cybersickness. Previous studies have predicted the severity of cybersickness based on physiological and/or kinematic data. However, compared with kinematic data, physiological data rely heavily on biosensors during the collection, which is inconvenient and limited to a few affordable VR devices. In this work, we proposed a deep neural network to predict cybersickness through kinematic data. We introduced the encoded physiological representation to characterize the individual susceptibility; therefore, the predictor could predict cybersickness only based on a user’s kinematic data without counting on biosensors. Fifty-three participants were recruited to attend the user study to collect multimodal data, including kinematic data (navigation speed, head tracking), physiological signals (e.g., electrodermal activity, heart rate), and Simulator Sickness Questionnaire (SSQ). The predictor achieved an accuracy of 97.8% for cybersickness prediction by involving the pre-computed physiological representation to characterize individual differences, providing much convenience for the current cybersickness measurement.

Many studies have explored how individual differences can affect users' susceptibility to cybersickness in a VR application. However, the lack of strategy to integrate the influence of each factor on cybersickness makes it difficult to utilize the results of existing research. Based on the fuzzy logic theory that can represent the effect of different factors as a single value containing integrated information, we developed two approaches including the knowledge-based Mamdani-type fuzzy inference system and the data-driven Adaptive neuro-fuzzy inference system (ANFIS) to involve three individual differences (Age, Gaming experience and Ethnicity). We correlated the corresponding outputs with the simulator sickness questionnaire (SSQ) scores in a simple navigation scenario. The correlation coefficients obtained through a 4- fold cross validation were found statistically significant with both fuzzy logic approaches, indicating their effectiveness to influence the occurrence and the level of cybersickness. Our work provides insights to establish customized experiences for VR navigation by involving individual differences.

This study presents a driving simulator experiment to evaluate three different motion cueing algorithms based on model predictive control. The difference among these motion strategies lies in the type of mathematical model used. The first one contains only the dynamic model of the platform, while the others integrate additionally two different vestibular system models. We compare these three strategies to discuss the tradeoffs when including a vestibular system model in the control loop from the user's viewpoint. The study is conducted in autonomous mode and in free driving mode, as both play an important role in motion cueing validation. A total of 38 individuals participated in the experiment; 19 drove the simulator in free driving mode and the remaining using the autonomous driving mode. For both driving modes, substantial differences is observed. The analysis shows that one of the vestibular system models is suitable for driving simulators, as it thoroughly restores high-frequency accelerations and is well noted by the participants, especially those in the free driving mode. Further tests are needed to analyze the advantages of integrating the chosen vestibular system model in the control design for motion cuieng algorithms. Regarding the autonomous mode, further research is needed to examine the influence of the vestibular system model on the motion performance, as the behavior of the autonomous model may implicitly interfere with subjective assessments.

This paper deals with motion control for an 8-degree-of-freedom (DOF) high performance driving simulator. We formulate a constrained optimal control that defines the dynamical behavior of the system. Furthermore, the paper brings together various methodologies for addressing feasibility issues arising in implicit model predictive control-based motion cueing algorithms. The implementation of different techniques is described and discussed subsequently. Several simulations are carried out in the simulator platform. It is observed that the only technique that can provide ensured closed-loop stability by assuring feasibility over all prediction horizons is a braking law that basically saturates the control inputs in the constrained form.

Studies using virtual reality environments (VE) have shown that subjects can perform path integration tasks with acceptable performance. However, in these studies, subjects could walk naturally across large tracking areas, or researchers provided them with large- immersive displays. Unfortunately, these configurations are far from current consumer-oriented VEs (COVEs), and little is known about how their limitations influence this task. Using a triangle completion paradigm, we assessed the subjects' spatial performance when developing path integration tasks in two consumer-oriented displays (an HTC Vive and a GearVR) and two consumer-oriented interaction devices (a Virtuix Omni motion platform and a Touchpad Control). Our results show that when locomotion is available (motion platform condition), there exist significant effects regarding the display and the path. In contrast, when locomotion is mediated no effect was found. Some future research directions are therefore proposed.

Homing is a fundamental task which plays a vital role in spatial navigation. Its performance depends on the computation of a homing vector, where human beings can use simultaneously two different cognitive strategies: an online strategy based on the self-motion cues known as path integration (PI), and an offline strategy called piloting based on the spatial image of the path. Studies using virtual reality environments (VE) have shown that human being can perform homing tasks with acceptable performance. However, in these studies, subjects were able to walk naturally across large tracking areas, or researchers provided them with high-end large-immersive displays. Unfortunately, these configurations are far from current consumer-oriented devices, and very little is known about how their limitations can influence these cognitive processes. Using a triangle completion paradigm, we assessed homing tasks in two consumer-oriented displays (an HTC Vive and a GearVR) and two consumer-oriented interaction devices (a Virtuix Omni Treadmill and a Touchpad Control). Our results show that when locomotion is available (treadmill condition), there exist significant effects regarding display and path complexity. In contrast, when locomotion is restricted (touchpad condition), some effects on path complexity were found. Thus, some future research directions are therefore proposed.

One major and well-known issue that occurs during VR experience is the appearance of cybersickness, which refrains users from accepting VR technologies. The induced cybersickness is due to a self-motion feeling that is produced when users see objects moving in the virtual world. To reduce cybersickness several methods have been proposed in the literature, however they do not guarantee immersion and navigation quality. In this paper, a new method to reduce cybersickness is proposed. The geometric deformation of the virtual model displayed in the peripheral field of view allows reducing the self-motion perceived by the user. Pilot test results show that visually induced self-motion is reduced with a guaranteed immersion quality while the user navigation parameters are kept.

Travel in a real environment is a common task that human beings conduct easily and subconsciously. However transposing this task in virtual environments (VEs) remains challenging due to input devices and techniques. Considering the well-described sensory conflict theory, we present a semiautomatic travel method based on path planning algorithms and gaze-directed control, aiming at reducing the generation of conflicted signals that may confuse the central nervous system. Since gaze-directed control is user-centered and path planning is goal-oriented, our semiautomatic technique makes up for the deficiencies of each with smoother and less jerky trajectories.

This paper proposes a new objective metric of visually induced motion sickness (VIMS) in the context of navigation in virtual environments (VEs). Similar to motion sickness in physical environments, VIMS can induce many physiological symptoms such as general discomfort, nausea, disorientation, vomiting, dizziness and fatigue. To improve user satisfaction with VR applications, it is of great significance to develop objective metrics for VIMS that can analyze and estimate the level of VR sickness when a user is exposed to VEs. One of the well-known objective metrics is the postural instability. In this paper, we trained a LSTM model for each participant using a normal-state postural signal captured before the exposure, and if the postural sway signal from post-exposure was sufficiently different from the pre-exposure signal, the model would fail at encoding and decoding the signal properly; the jump in the reconstruction error was called loss and was proposed as the proposed objective measure of simulator sickness. The effectiveness of the proposed metric was analyzed and compared with subjective assessment methods based on the simulator sickness questionnaire (SSQ) in a VR environment, achieving a Pearson correlation coefficient of. 89. Finally, we showed that the proposed method had the potential to be deployed within a closed-loop system and get real-time performance to predict VR sickness, opening new insights to develop user-centered and customized VR applications based on physiological feedback.

International audience

We propose to demonstrate a novel immersive virtual reality simulator aimed at detecting whether potential workers at height are able to climb high up for dangerous operations. Our simulator consists in a dynamic platform that simulates the vibrations of an aerial device during elevation, a real ladder synchronized in position with a virtual one placed on top of a virtual electric pole, a harness that allows users safely climbing the ladder and a head-mounted display (HMD) for visualization. Our demonstration invites users to experience a high fidelity work-at-height situation triggering fear of heights.

This poster formulates the concept of heterogeneous distributed mixed reality (HDMR) applications in order to state some interesting research questions in this domain. HDMR applications give synchronous access to shared virtual worlds, from diverse mixed reality (MR) hardware, and at similar levels of functionality. We show the relationship between HDMR and previous concepts, state challenges in their development, and illustrate this concept and its challenges with an example.

We present WoaH, a virtual reality work-at-height simulator aimed at (i) testing whether future workers are able to manage their stress when high up and thus easily detect susceptibility to vertigo, and (ii) training in a typical work-at-height engineering operation. The simulator is composed of a real ladder synchronized in position with a virtual one placed 11 meters above the ground in a virtual environment. Visualization is done through a head-mounted display (HMD). We conducted a first user study evaluating our simulator in terms of cybersickness, perceived realism and anxiety, through both subjective (questionnaires) and objective (electrodermal activity) measurements, and testing whether vibratory cues could enhance the level of anxiety felt. Results indicate that WoaH generates anxiety as expected and is perceived as realistic. Adding vibrations had significant impact on the perceived realism but not on the electro-dermal activity. These first results bring insights to future developments for a deployment in companies dealing with work at height.

We propose an innovative method to navigate in a virtual environment by adapting the acceleration parameters to users in real time, in order to reduce cybersickness. Indeed, navigation parameters for most navigation interfaces are still determined by rate-control devices. Inappropriate parameter settings may lead to strong sickness, making the application unusable. Past research found that especially accelerations should not be set too high. Here, we define the accelerations as a function of a cybersickness indicator: the Electro-Dermal Activity (EDA). A pre-study was conducted to test the effectiveness of our approach and showed promising results where cybersickness tends to decrease with our adaptive navigation method.

We present a first study where we combine two asymetric virtual reality systems for telecollaboration purposes: a CAVE system and a head-mounted display (HMD), using a server-client type architecture. Experiments on a puzzle game in limited time, alone and in collaboration, show that combining asymetric systems reduces cognitive load. Moreover, the participants reported preferring working in collaboration and showed to be more efficient in collaboration. These results provide insights in combining several low cost HMDs with a unique expensive CAVE.

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.

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.

This paper proposes a navigation technique for traveling inside a real-scale 3D model based on human gesture analysis. In the first step, a simple threshold is used as a criterion to analyze gestures. In the next step, a complex criterion will be imposed to the analysis to improve the navigation technique. Walking is a periodic signal and moving feet up and down is a part which is repeated. A mother wavelet is allocated to the selected pattern. Then the position of the pattern is recognized by applying Multi Resolution Analysis (MRAs). The movement command is generated and sent to the graphic render (as a movement command). Analytical results show a very high precision performance in the presence of noise, scale variation and superposition of other signals. Practical experiments also verify the same promising results.

The paper presents a new mother wavelet adapted from a specific pattern. Wavelet multi-resolution analysis uses this wavelet to detect the position of the pattern in an Infra-Red (IR) signal under scale variation and the presence of noise. IR signal is extracted from IR image sequence recorded by an IR camera, Time of Flight (TOF) sensor configuration. The maximum correlation between the pattern and the signal of interest will be used as a criterion to define the mother wavelet. The proposed mother wavelet were tested and verified under the scale variation and the presence of noise. The experimental tests and performance analysis show promising results for both scale variation and noisy signal. 90% accuracy for the proposed wavelet under intensive noisy condition (50% of the signal amplitude) is guaranteed and high precision is expected under real condition.

This paper presents a prototype of a hands-on immersive peripheral device for controlling a virtual hand with high dexterity. Based on the results of users' tests on previous versions of our device and on the analysis of a manipulation task, this prototype is as easy as a mouse to use and allows the control of a high number of degrees of freedom (dofs) with tactile feedback. Design issues, physical phenomena and physiological behaviors are tightly linked and highly influence interaction. The goals corresponding to these issues include the choice of sensors' technology and their position on the device, low efforts exerted while using the device, relevant multi-sensorial feedback, performance of achieved tasks. An example of a grasping task illustrates the effectiveness of our device to achieve intuitive and efficient interactions, bringing new insights for collaborative interaction.

There are many kinds of large, heavy objects, or objects with geometrical constraints in our daily life, but non-fixed robots such as humanoid robots are still not able to manipulate them sufficiently well. In this paper we focus on a swing door as a heavy object with geometrical constraints, and present a method for the humanoid robots to open it by using impulsive forces. We first discuss on momentum transfer from the robot to the door. Then we propose a method of generating a whole body motion to impact on the door. We analyze the dynamic model of the door, and we confirm the validity of our method through simulation. At last, we realize a motion of the robot opening a swing door quickly by the method in experiment with the HRP-2 robot hardware.

A motion control method of lifting a heavy object up to a higher position with humanoid robots is developed. The key issue of lifting motion is how to reduce the load on humanoid arms in which low-power actuators are implemented. The use of singular postures of arms is well-known to avoid actuator saturation of the arms. By combining two different kinds of humanoid motions such as accelerating an object upward and sliding the body into under the object, we propose a method that enables to transit one singular posture of arms to another while lifting the object. Simulation results show the effectiveness of the proposed method for reducing the load on the arms. We realize a motion of lifting a heavy object dynamically with the humanoid robot HRP-2 through experiment.

In this paper we present an integrative frame work centered on haptic interaction with virtual avatars. This framework is devised for general prototyping and collaborative scenario studies with haptic feedback. First we present the software architecture of the framework and give details on some of its components. Then we show how this framework can be used to derive in a short time a virtual reality simulation. In this simulation, a user directly interacts with a virtual avatar to collaboratively manipulate a virtual object, with haptic feedback and using fast dynamics computation and constraint based methods with friction.

This paper describes a motion generation method for dynamic lifting by a humanoid robot. The proposed technique suggests the possibility of taking advantage of the whole body motion in order to facilitate the lifting movement. In particular, the idea is to perform a preliminary motion in order to generate a momentum which is instantaneously transferred to the object as an impulsive force. This allows the humanoid to lift up an object that could not be lifted up only by continuous force. However an impulsive force may make the humanoid unstable. Then, we propose to set the center of percussion (CoPn) of the whole system at the center of the support polygon of the humanoid when it lifts up the object. We also propose a design method of a preliminary motion of the humanoid that generates a sufficient momentum to lift up an object without any slip, tumble and hop of the whole system. The effectiveness of the proposed method is confirmed by simulation and experiment.