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.

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.

Food choices are intimately related to individual health. Therefore, the food we buy should be carefully chosen. However, grocery shopping is typically done in noisy environments, and food products usually present cluttered labels with dense texts that make it hard to properly evaluate relevant nutritional data. Augmented reality (AR) allows a shopper to visualize digitally generated contents onto real objects and to interact with them. In this experiment, we investigated the effects of delivering nutritional information using AR technology on food choices. To this end, we ran a between-participants laboratory experiment in which participants were asked to choose among the products available. The experimental group received the food-related information via AR, while the control group had ordinary access to food packaging. We found that AR technology facilitated the choice of healthier food items. Additionally, participants in the experimental group reported that they based their decisions on nutritional information rather than on the appearance of the package. The present work highlights how AR can be exploited to bring to the foreground information that would otherwise be hard to spot, thereby increasing the consumer’s awareness of the overall characteristics of the product.

Background: Industry 5.0 emphasizes human centricity by prioritizing human well-being alongside technological advancements. Collaborative robots (cobots) in industrial settings represent one such advancement, and their integration, particularly in manufacturing, is reshaping production processes. Although previous studies have addressed these issues, no systematic review has yet synthesized findings on how cobots impact operators’ affective well-being and cognitive workload. Objective: This study focused on psychological dimensions, which are often overlooked, particularly affective states, addressing a gap in the existing literature that has mainly emphasized the impact of cobots on the physical and cognitive workload. Specifically, we aimed to systematically review empirical studies investigating affective well-being (ie, anxiety, stress, and depression symptoms) and cognitive workload in human-cobot collaboration (HCC) within industrial settings. Methods: We conducted a comprehensive systematic search of the literature using several databases (Web of Science, Scopus, ACM Digital Library, and IEEE Xplore). Eligibility criteria included peer-reviewed empirical studies reporting quantitative or qualitative data on cognitive workload or affective well-being in HCC. Two reviewers independently conducted study selection and data extraction. Results: This review included a total of 46 studies. Findings indicated a significant increase in publications from 2020 onward, reflecting the growing interest in HCC. Most studies (28/46, 61%) were conducted in controlled laboratory settings with university students or researchers, highlighting a gap in real-world industrial research. Results indicated that, while cobots have been shown to alleviate physical fatigue and enhance job satisfaction, they also introduce new psychological challenges, including stress and anxiety symptoms due to concerns about job security and the pressures of high-paced operations. The speed at which cobots operate represents a factor affecting operators’ affective well-being and cognitive workload alongside the proximity of cobots, the system usability, and the complexity of the tasks assigned. With regard to cognitive workload, studies using physiological and self-report measures (38/46, 83%) consistently found that higher task complexity significantly raised both cognitive workload and stress levels. Conclusions: This review identified key factors that influence operators’ affective well-being and cognitive workload when working with cobots. These insights can guide the development of longitudinal research and intervention strategies, ensuring that the integration of cobots supports both productivity and operators’ well-being in manufacturing environments. To support effective implementation, future studies should be conducted in real-world settings using standardized assessment instruments, physiological measures, and qualitative interviews.

Physical inactivity is a plague for public health, especially in Western Countries. Among the countermeasures, mobile applications promoting physical activity seem particularly promising, thanks to the spread and adoption of mobile devices. However, the dropout rates of users are high, thereby calling for strategies to increase retention rates. Moreover, user testing can be problematic, because it is typically conducted in a laboratory, leading to a limited ecological validity. In the present research, we developed a custom mobile app to promote physical activity. Three versions of the app were implemented, each featuring a different pattern of gamification elements. Moreover, the app was designed to work as a self-managed experimental platform. A remote field study was conducted to investigate the effectiveness of the different versions of the app. Behavioral log data of physical activity and interaction with the app were collected. Our results show the feasibility of using a mobile app running on personal devices as an independently managed experimental platform. Moreover, we found that gamification elements per se do not ensure higher retention rates, rather it emerged that the richer combination of gamified elements was effective.