User acceptance of virtual reality: an extended technology acceptance modelAlthough virtual reality (VR) has many applications, only few studies have investigated user acceptance of this type of immersive technology. We propose an extended version of the Technology Acceptance Model (TAM) that addresses some aspects of VR. Our model includes variables from the TAM, user experience, variables specific to VR, and variables relating to user characteristics. This model was tested with 89 users who performed an aeronautical assembly task in VR. Results suggest that intention to use VR is positively influenced by perceived usefulness and negatively influenced by cybersickness. Hedonic quality-stimulation and personal innovativeness are predictors of perceived usefulness. Perceived ease of use does not have a significant impact on intention to use and it is only influenced by pragmatic quality. These findings have a number of implications regarding user acceptance of VR.

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This narrative review synthesizes and introduces 386 previous works about virtual reality-induced symptoms and effects by focusing on cybersickness, visual fatigue, muscle fatigue, acute stress, and mental overload. Usually, these VRISE are treated independently in the literature, although virtual reality is increasingly considered an option to replace PCs at the workplace, which encourages us to consider them all at once. We emphasize the context of office-like tasks in VR, gathering 57 articles meeting our inclusion/exclusion criteria. Cybersickness symptoms, influenced by fifty factors, could prevent workers from using VR. It is studied but requires more research to reach a theoretical consensus. VR can lead to more visual fatigue than other screen uses, influenced by fifteen factors, mainly due to vergence-accommodation conflicts. This side effect requires more testing and clarification on how it differs from cybersickness. VR can provoke muscle fatigue and musculoskeletal discomfort, influenced by fifteen factors, depending on tasks and interactions. VR could lead to acute stress due to technostress, task difficulty, time pressure, and public speaking. VR also potentially leads to mental overload, mainly due to task load, time pressure, and intrinsically due interaction and interface of the virtual environment. We propose a research agenda to tackle VR ergonomics and risks issues at the workplace.

The convergence of technologies currently observed in the field of Virtual Reality, Augmented Reality, robotics and consumer electronic reinforces the trend of new applications appearing every day. But when transferring knowledge acquired from research to businesses, research laboratories are often at a loss because of a lack of knowledge of the design and integration processes in creating an industrial scale product. In fact, the innovation approaches that take a good idea from the laboratory to a successful industrial product are often little known to researchers. The objective of this paper is to present the results of the work of several research teams that have finalized a working method for researchers and manufacturers that allow them to design virtual or augmented reality systems and enable their users to enjoy "a compelling Virtual Reality experience". That approach, called "the I2I method", present 11 phases from "Establishing technological and competitive intelligence and industrial property" to "Improvements" through the "Definition of the Behavioral Interface, Virtual Environment and Behavioral Software Assistance". As a result of the experience gained by various research teams, this design approach benefits from contributions from current Virtual Reality and Augmented Reality research. Our objective is to validate and continuously move such multidisciplinary design team methods forward.

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Abstruct-The availability of a digital driver behavior model during emergency situations constitutes a major breakthrough dealing with active safety system tuning. This article presents a modeling approach based on an input-output system (initial conditions-driver's actions). The starting point of our work is a behavioral database gathered from a track experiment with common drivers. Subjects are confronted with the sudden braking of n released trailer, which they followed for n while. Our objective i s to predict driver's actions following a set of initial conditions (distance to collision, speeds, and friction). The core of our model is an inference system based on uugmenfed n u h Bayesian network. This article outlines the various stages leading to the construction of this model. It discusses its robustness using another database.