Background: Voice assistants allow users to control appliances and functions of a smart home by simply uttering a few words. Such systems hold the potential to significantly help users with motor and cognitive disabilities who currently depend on their caregiver even for basic needs (eg, opening a door). The research on voice assistants is mainly dedicated to able-bodied users, and studies evaluating the accessibility of such systems are still sparse and fail to account for the participants’ actual motor, linguistic, and cognitive abilities. Objective: The aim of this work is to investigate whether cognitive and/or linguistic functions could predict user performance in operating an off-the-shelf voice assistant (Google Home). Methods: A group of users with disabilities (n=16) was invited to a living laboratory and asked to interact with the system. Besides collecting data on their performance and experience with the system, their cognitive and linguistic skills were assessed using standardized inventories. The identification of predictors (cognitive and/or linguistic) capable of accounting for an efficient interaction with the voice assistant was investigated by performing multiple linear regression models. The best model was identified by adopting a selection strategy based on the Akaike information criterion (AIC). Results: For users with disabilities, the effectiveness of interacting with a voice assistant is predicted by the Mini-Mental State Examination (MMSE) and the Robertson Dysarthria Profile (specifically, the ability to repeat sentences), as the best model shows (AIC=130.11). Conclusions: Users with motor, linguistic, and cognitive impairments can effectively interact with voice assistants, given specific levels of residual cognitive and linguistic skills. More specifically, our paper advances practical indicators to predict the level of accessibility of speech-based interactive systems. Finally, accessibility design guidelines are introduced based on the performance results observed in users with disabilities.

This paper presents an experimental study of participants' response to the sudden appearance of a fire emergency in a virtual environment (VE) and of the adaptivity of their response pattern. A VE has been built in which participants meet two situations: first an explorative navigation and afterwards a hurried escape from the unexpected outbreak of fire. Fire intensity and participants' distance from the exit at the outbreak of fire have been varied as well, to create different degrees of danger and different degrees of difficulty in the task of leaving the premises. Participants' action has been collected automatically for quantitative analysis by registering each individual activation of the interaction devices (a triple button joystick). In addition, the movements in both virtual and real environment of additional groups of participants have been videorecorded for qualitative analysis. Results show that the appearance of the fire emergency triggers important changes in the way people move in the VE, and that such changes are all adaptive responses to an emergency situation. In conclusion, people show recognition of a dangerous situation in a VE and readily produce adaptive responses, making the VE suitable for emergency simulations and for use as an effective training tool.

For over two decades Virtual Reality (VR) has been used as a useful tool in several fields, from medical and psychological treatments, to industrial and military applications. Only in recent years researchers have begun to study the neural correlates that subtend VR experiences. Even if the functional Magnetic Resonance Imaging (fMRI) is the most common and used technique, it suffers several limitations and problems. Here we present a methodology that involves the use of a new and growing brain imaging technique, functional Near-infrared Spectroscopy (fNIRS), while participants experience immersive VR. In order to allow a proper fNIRS probe application, a custom-made VR helmet was created. To test the adapted helmet, a virtual version of the line bisection task was used. Participants could bisect the lines in a virtual peripersonal or extrapersonal space, through the manipulation of a Nintendo Wiimote^® controller in order for the participants to move a virtual laser pointer. Although no neural correlates of the dissociation between peripersonal and extrapersonal space were found, a significant hemodynamic activity with respect to the baseline was present in the right parietal and occipital areas. Both advantages and disadvantages of the presented methodology are discussed.

In the past few years, collaborative robots (i.e., cobots) have been largely adopted within industrial manufacturing. Although robots can support companies and workers in carrying out complex activities and improving productivity, human factors related to cobot operators have not yet been thoroughly investigated. The present study aims to understand the subjective experience of younger and senior workers interacting with an industrial collaborative robot. Results show that workers' acceptance of cobots is high, regardless of age and control modality used. Interesting differences between seniors and younger adults emerged in the evaluations of user experience, usability, and perceived workload of participants and are detailed and commented in the last part of the work.

Interest in the virtualization of human–robot interactions is increasing, yet the impact that collaborating with either virtual or physical robots has on the human operator’s mental state is still insufficiently studied. In the present work, we aimed to fill this gap by conducting a systematic assessment of a human–robot collaborative framework from a user-centric perspective. Mental workload was measured in participants working in synergistic co-operation with a physical and a virtual collaborative robot (cobot) under different levels of task demands. Performance and implicit and explicit workload were assessed as a function of pupil size variation and self-reporting questionnaires. In the face of a similar self-reported mental demand when maneuvering the virtual or physical cobot, operators showed shorter operation times and lower implicit workload when interacting with the virtual cobot compared to its physical counterpart. Furthermore, the benefits of collaborating with a virtual cobot most vividly manifested when the user had to position the robotic arm with higher precision. These results shed light on the feasibility and importance of relying on multidimensional assessments in real-life work settings, including implicit workload predictors such as pupillometric measures. From a broader perspective, our findings suggest that virtual simulations have the potential to bring significant advantages for both the user's mental well-being and industrial production, particularly for highly complex and demanding tasks.