Recognizing trust as a pivotal element for success within Human–Robot Collaboration (HRC) environments, this article examines its nature, exploring the different dimensions of trust, analysing the factors affecting each of them, and proposing alternatives for trust measurement. To do so, we designed an experimental procedure involving 50 participants interacting with a modified ‘Inspector game’ while we monitored their brain, electrodermal, respiratory, and ocular activities. This procedure allowed us to map dispositional (static individual baseline) and learned (dynamic, based on prior interactions) dimensions of trust, considering both demographic and psychophysiological aspects. Our findings challenge traditional assumptions regarding the dispositional dimension of trust and establish clear evidence that the first interactions are critical for the trust-building process and the temporal evolution of trust. By identifying more significant psychophysiological features for trust detection and underscoring the importance of individualized trust assessment, this research contributes to understanding the nature of trust in HRC. Such insights are crucial for enabling more seamless human–robot interaction in collaborative environments.

Objective: Long term behavioural disturbances and interventions in healthy habits (mainly eating and physical activity) are the primary cause of childhood obesity. Current approaches for obesity prevention based on health information extraction lack the integration of multi-modal datasets and the provision of a dedicated Decision Support System (DSS) for health behaviour assessment and coaching of children. Methods: Continuous co-creation process has been applied in the frame of the Design Thinking Methodology, involving children, educators and healthcare professional in the whole process. Such considerations were used to derive the user needs and the technical requirements needed for the conception of the Internet of Things (IoT) platform based on microservices. Results: To promote the adoption of healthy habits and the prevention of the obesity onset for children (9-12 years old), the proposed solution empowers children -including families and educators- in taking control of their health by collecting and following-up real-time information about nutrition, physical activity data coming from IoT devices, and interconnecting healthcare professionals to provide a personalised coaching solution. The validation has two phases involving +400 children (control/intervention group), on four schools in three countries: Spain, Greece and Brazil. The prevalence of obesity decreased in 75.5% from baseline levels in the intervention group. The proposed solution created a positive impression and satisfaction from the technology acceptance perspective. Conclusions: Main findings confirm that this ecosystem can assess behaviours of children, motivating and guiding them towards achieving personal goals.

This paper researches the classification of human emotions in a virtual reality (VR) context by analysing psychophysiological signals and facial expressions. Key objectives include exploring emotion categorisation models, identifying critical human signals for assessing emotions, and evaluating the accuracy of these signals in VR environments. A systematic literature review was performed through peer-reviewed articles, forming the basis for our methodologies. The integration of various emotion classifiers employs a ‘late fusion’ technique due to varying accuracies among classifiers. Notably, facial expression analysis faces challenges from VR equipment occluding crucial facial regions like the eyes, which significantly impacts emotion recognition accuracy. A weighted averaging system prioritises the psychophysiological classifier over the facial recognition classifiers due to its higher accuracy. Findings suggest that while combined techniques are promising, they struggle with mixed emotional states as well as with fear and trust emotions. The research underscores the potential and limitations of current technologies, recommending enhanced algorithms for effective interpretation of complex emotional expressions in VR. The study provides a groundwork for future advancements, aiming to refine emotion recognition systems through systematic data collection and algorithm optimisation.

Today museums around the world offer their content through two basic methods: a simple view of their artworks through a content viewer, or through a custom designed 2D or 3D virtual exhibition in which the pieces and the scene are static. This paper describes a 3DPublish tool which represents an alternative to these two static solutions thereby giving the possibility to dynamically manage a 3D virtual scenario (real or imaginary) and the artwork that composes it. This gives the user a most realistic experience through different exhibitions, using various added value methods like storytelling or virtual tours. 3DPublish will facilitate the museum curator's daily tasks and will improve the final results for 3D virtual museum exhibitions. This application was created as part of the Tourspheres project, whose challenge is to explore new measurement systems to reach a more valuable tourist behavior comprehension. But after the experience (presented in this paper as a case study) of creating a custom development for an exhibition for the Kubo Gallery in San Sebastian (SPAIN), the work focused on abstracting all processes to package the 3DPublish tool with the aim that it becomes a commercial solution.

The construction industry faces significant challenges due to the physically demanding and hazardous nature of tasks such as manual filling of expansion joints with mastic. Automating mastic filling presents additional difficulties due to the variability of mastic density with temperature, which creates a constantly changing environment that requires adaptive control strategies to ensure consistent application quality. This pilot study focuses on testing a new human–robot collaborative approach for automating the mastic application in concrete expansion joints. The system learns the task from demonstrations performed by expert construction operators teleoperating the robot. This study evaluates the usability, efficiency, and adoption of robotic assistance in joint-filling tasks compared to traditional manual methods. The study analyzes execution time and joint quality measurements, psychophysiological signal analysis, and post-task user feedback. This multi-source approach enables a comprehensive assessment of task performance and both objective and subjective evaluations of technology acceptance. The findings underscore the effectiveness of automated systems in improving safety and productivity on construction sites, while also identifying key areas for technological improvement.

Recognizing trust as a pivotal element for success within Human-Robot Collaboration (HRC) environments, this article examines its nature, exploring the different dimensions of trust, analysing the factors affecting each of them, and proposing alternatives for trust measurement. To do so, we designed an experimental procedure involving 50 participants interacting with a modified 'Inspector game' while we monitor their brain, electrodermal, respiratory, and ocular activities. This procedure allowed us to map dispositional (static individual baseline) and learned (dynamic, based on prior interactions) dimensions of trust considering both demographic and psychophysiological aspects. Our findings challenge traditional assumptions regarding the dispositional dimension of trust and establish clear evidence that the first interactions are critical for the trust-building process and the temporal evolution of trust. By identifying more significant psychophysiological features for trust detection and underscoring the importance of individualized trust assessment, this research contributes to understanding the nature of trust in HRC. Such insights are crucial for enabling more seamless human-robot interaction in collaborative environments.