The increasingly aging workforce is bringing particular attention to senior individuals in production sectors. While the interest in Virtual Reality (VR) applications for industrial robotics grows, the question of whether and how senior workers can withstand VR-based repetitive tasks arises. We here aimed to answer such questions by systematically assessing young and senior users’ experiential, behavioural, and cognitive factors during simulated robotic teleoperations in VR. Two control systems for VR telerobotics, button- and action-based controls, were employed. Human performance, vigilance, and workload were measured through self-reports and a VR-integrated eye-tracker. Additionally, age-dependent differences in individual cultural and experiential factors were explored via self-report measures. Despite being slower and experiencing increased fatigue under specific conditions, as suggested by the eye-tracking measures, senior users demonstrated comparable precision in operating the robotic arm to their younger counterparts. Notably, both age groups reported similar levels of perceived fatigue. The paper provides an in-depth analysis of the advantages and challenges of adopting advanced telerobotics control systems across different age groups, consistently emphasising the human-centered dimension.

COVID-19 had substantial effects on the IoT community which designs systems for safety: the urge to face masks worn by everyone, the analysis of crowds to avoid the spread of the disease, and the sanitization of public environments has led to exceptional research acceleration and fast engineering of the related solutions. Now that the pandemic is losing power, some applications are becoming less important, while others are proving to be useful regardless of the criticality of COVID-19. The Safe Place project is a prime example of this situation (DATE23 MPP category: final stage). Safe Place is an Italian 3M euro regional industrial/academic project, financed by European funds, created to ensure a multidisciplinary choral reaction to COVID-19 in critical environments such as rest homes and public places. Safe Place consortium was able to understand what is no longer useful in this post-pandemic period, and what instead is potentially attractive for the market. For example, the detection of face masks has little importance, while sanitization does have much. This paper shares such analysis, which emerged through a co-design process of three public Safe Place project demonstrators, involving heterogeneous figures spanning from scientists to lawyers.

Virtual Reality (VR) technologies are widely employed to investigate human behavior in dangerous situations that cannot be safely reproduced in the real world, allowing researchers to study in an ecological way complex scenarios such as training for risky jobs, safety procedures, emergencies and, more recently, moral dilemmas in driving context. Understanding how people act when facing severe accidents involving unavoidable collisions has extremely important implications for the design and development of the “decisional system” of Autonomous Vehicles (AV s). However, previous studies have not focused on the differences between being the driver acting in a complex moral situation or being in a self-driving car that chooses for you. In the present paper, we described a case study that uses a first-person virtual reality simulation to investigate people's emotional reactions, perceived sense of responsibility, and acceptability of moral behavior in human and autonomous driving modalities. The main findings showed that participants experienced a high sense of presence in our simulation and react differently to the two driving conditions, showing a greater arousal, a more negative valence, and an increased sense of responsibility when faced moral dilemmas as drivers. Instead, in scenarios that did not involve killing someone (non-moral dilemmas), being in a fully autonomous vehicle was judged less pleasant than being the actual driver. These results suggest that people prefer to be in control only in common driving situations and not when their actions have deadly consequences on other people, suggesting the need to consider emotional factors in studying decision-making applied to autonomous vehicles, as a mean to reach a more complete understanding of people's reactions to this new technology, and to possibly gain insights for the design of autonomous driving systems and, more generally, AI-driven machines.

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).