Collaborative robots are increasingly common in modern production systems, since they allow to merge the productivity of automated systems with the flexibility and dexterity of manual ones. The direct interaction between the human and the robot can be the greatest advantage and the greatest limit of collaborative systems at the same time, depending on how it affects human factors like ergonomics and mental stress. This work presents an overview of collaborative robotics considering three main dimensions: robot features, modern production systems characteristics and human factors. A literature review on how such dimensions interact is addressed and a discussion on the current state of the art is presented, showing the topics that have been already widely explored and the research gaps that should be fulfilled in the future.

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.

Industry 5.0 marks a significant transition in the industrial sector, integrating human centricity with technological advancements. It redefines work dynamics, emphasizing social sustainability and sustainable development in shaping career paths. The necessity for equitable workforce training, prioritizing worker well-being alongside technological adaptation, is pivotal. The rapid pace of technological advancement relates to significant investment in talent development, emphasizing the workforce's upskilling and reskilling and the importance of continuous learning to meet these new challenges. It advocates for inclusive and comprehensive training programs that cater to diverse workforce needs. Addressing challenges while leveraging opportunities for social and environmental sustainability is critical for Industry 5.0's success. This transition aims to create a sustainable, inclusive industrial future where technological advancements are harmonized with human values and environmental considerations. This approach sets a new standard for industrial operations and career development, positioning Industry 5.0 as a landmark for modern, responsible, and sustainable industrial practices.

The use of implicit relevance feedback from neurophysiology could deliver effortless information retrieval. However, both computing neurophysiologic responses and retrieving documents are characterized by uncertainty because of noisy signals and incomplete or inconsistent representations of the data. We present the first‐of‐its‐kind, fully integrated information retrieval system that makes use of online implicit relevance feedback generated from brain activity as measured through electroencephalography (EEG), and eye movements. The findings of the evaluation experiment (N = 16) show that we are able to compute online neurophysiology‐based relevance feedback with performance significantly better than chance in complex data domains and realistic search tasks. We contribute by demonstrating how to integrate in interactive intent modeling this inherently noisy implicit relevance feedback combined with scarce explicit feedback. Although experimental measures of task performance did not allow us to demonstrate how the classification outcomes translated into search task performance, the experiment proved that our approach is able to generate relevance feedback from brain signals and eye movements in a realistic scenario, thus providing promising implications for future work in neuroadaptive information retrieval (IR).

Introduction: As a result of Industry 5.0’s technological advancements, collaborative robots (cobots) have emerged as pivotal enablers for refining manufacturing processes while re-focusing on humans. However, the successful integration of these cutting-edge tools hinges on a better understanding of human factors when interacting with such new technologies, eventually fostering workers’ trust and acceptance and promoting low-fatigue work. This study thus delves into the intricate dynamics of human-cobot interactions by adopting a human-centric view.Methods: With this intent, we targeted senior workers, who often contend with diminishing work capabilities, and we explored the nexus between various human factors and task outcomes during a joint assembly operation with a cobot on an ergonomic workstation. Exploiting a dual-task manipulation to increase the task demand, we measured performance, subjective perceptions, eye-tracking indices and cardiac activity during the task. Firstly, we provided an overview of the senior workers’ perceptions regarding their shared work with the cobot, by measuring technology acceptance, perceived wellbeing, work experience, and the estimated social impact of this technology in the industrial sector. Secondly, we asked whether the considered human factors varied significantly under dual-tasking, thus responding to a higher mental load while working alongside the cobot. Finally, we explored the predictive power of the collected measurements over the number of errors committed at the work task and the participants’ perceived workload.Results: The present findings demonstrated how senior workers exhibited strong acceptance and positive experiences with our advanced workstation and the cobot, even under higher mental strain. Besides, their task performance suffered increased errors and duration during dual-tasking, while the eye behavior partially reflected the increased mental demand. Some interesting outcomes were also gained about the predictive power of some of the collected indices over the number of errors committed at the assembly task, even though the same did not apply to predicting perceived workload levels.Discussion: Overall, the paper discusses possible applications of these results in the 5.0 manufacturing sector, emphasizing the importance of adopting a holistic human-centered approach to understand the human-cobot complex better.

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.