Human factors and ergonomics

Italian hand gestures constitute a distinctive and widely recognized form of nonverbal communication, deeply embedded in everyday interaction and cultural identity. Despite their prominence, these gestures are rarely formalized or systematically taught, posing challenges for foreign speakers and visitors seeking to interpret their meaning and pragmatic use. Moreover, their ephemeral and embodied nature complicates traditional preservation and transmission approaches, positioning them within the broader domain of intangible cultural heritage. This paper introduces a machine learning–based framework for recognizing iconic Italian hand gestures, designed to support cultural learning and engagement among foreign speakers and visitors. The approach combines RGB–D sensing with depth-enhanced geometric feature extraction, employing interpretable classification models trained on a purpose-built dataset. The recognition system is integrated into a non-immersive virtual reality application simulating an interactive digital totem conceived for public arrival spaces, providing tutorial content, real-time gesture recognition, and immediate feedback within a playful and accessible learning environment. Three supervised machine learning pipelines were evaluated, and Random Forest achieved the best overall performance. Its integration with an Isolation Forest module was further considered for deployment, achieving a macro-averaged accuracy and F1-score of 0.82 under a 5-fold cross-validation protocol. An experimental user study was conducted with 25 subjects to evaluate the proposed interactive system in terms of usability, user engagement, and learning effectiveness, obtaining favorable results and demonstrating its potential as a practical tool for cultural education and intercultural communication.

Keywords: gesture recognition , human–computer interaction , intangible cultural heritage , Italian culture , machine learning , virtual reality

This study explores how integrating biophilic elements into automated vehicles affects users' experience, affective trust, perceived price value, connectedness to nature, and behavioral intention. Sixty-one participants experienced 4 driving scenarios with three types of biophilic interventions in a mixed reality environment. Results from Partial Least Squares revealed that perceived price value was the strongest predictor of behavioral intention, while final trust and connectedness to nature showed no direct impact. Initial trust influenced behavioral intention indirectly by enhancing user experience, which increased perceived price value. Demographic factors, did not significantly affect outcomes. These findings show the value of integrating biophilic elements in automated vehicles lies not only in making the ride more pleasant, but in strengthening the factors that drive adoption. This research provides pragmatic insights for automotive designers and industries. In a market where technology frequently feels impersonal, integrating natural elements can transform the driving experience, making it not only more pleasant but also more desirable to potential customers.HIGHLIGHTS Biophilic elements in AVs enhance user experience and behavioral intention. Perceived price value is the strongest predictor of behavioral intention. Initial trust boosts behavioral intention indirectly via improved user experience.

Keywords: Automated vehicle adoption , behavioural intention , biophilic design , human–machine interaction , mixed reality experience

Objective. Objective evaluation of operational training remains challenging, particularly when comparing physical training with virtual reality (VR) training. Behavioral and subjective measures capture performance and perceived workload but provide limited insight into the underlying neurocognitive adaptation. This study proposes and validates a synthetic EEG-derived cognitive training index (CTI) that integrates multiple training-sensitive neurometrics into a single composite metric, with two aims: to validate CTI as an objective marker of training progression in a physical training group, and to test whether VR-based training leads to neurocognitive adaptation comparable to physical training when participants subsequently execute the task in the real environment. Approach. Participants completed three repetitions of a realistic confined-space industrial maintenance procedure structured into Entrance, Rolling shutter, and Pipe replacement phases. Behavioral outcomes (execution time, procedural errors) and subjective workload NASA Task Load Index (NASA-TLX) were collected after each trial. Wearable EEG was recorded continuously; six neurometrics were computed from cleaned EEG using band-specific Global Field Power over predefined electrode sets. CTI was obtained via principal component analysis applied at the trial level to the multivariate neurometric time series, followed by segment-wise analysis. Temporal specificity of CTI was assessed through phase-randomized surrogate-data analyses. Main results. Execution time decreased across trials (F = 14.82, p <.001, η2p = 0.35), procedural errors decreased similarly (F = 10.31, p <.001, η2p = 0.27), and NASA-TLX declined (F = 11.94, p <.001, η2p = 0.30). CTI increased significantly with training (F = 18.21, p <.001, η2p = 0.39) and was selectively higher in learning-relevant phases (Entrance and Pipe replacement), while remaining near baseline in the low-demand Rolling shutter phase. Surrogate analyses showed that real CTI exceeded null expectations, supporting temporal specificity. Repeated-measures correlations indicated significant negative associations between CTI and procedural errors (R = − 0.42, p <.01) and between CTI and NASA-TLX (R = − 0.38, p <.01), while CTI was not significantly related to execution time (R = 0.18, p =.12). Significance. CTI provides a psychologically grounded, multivariate EEG marker that tracks training progression and aligns with operationally meaningful outcomes, namely procedural accuracy and perceived workload, rather than task speed alone. This approach supports objective comparison of cognitive adaptation across training modalities and provides a practical basis for neurophysiologically informed training evaluation and future adaptive training systems.

Keywords: EEG , principal component analysis , training

Human–Machine Interfaces (HMIs) are increasingly important in vehicles and other safety-critical systems, yet approaches to their usability and User eXperience (UX) evaluation remain fragmented. This systematic literature review investigates how HMIs are empirically evaluated across domains, with a primary focus on automotive HMIs, complemented by evidence from related safety-critical domains. The review examines UX and usability evaluation methodologies, tools, standards, and technological trends reported in recent research. Peer-reviewed journal articles published between 2015 and 2025 were considered if they addressed empirical usability or UX evaluation of HMIs. Searches were conducted in Scopus and ScienceDirect databases following PRISMA guidelines. From n = 659 records initially identified, n = 82 papers were included in the final analysis. The literature was synthesized using a descriptive and narrative approach, focusing on evaluation contexts, testing methodologies, sensor-based tools, applied standards, and assessment metrics. Most papers investigated automotive HMIs, while fewer addressed aerospace, industrial, maritime, and other safety-critical applications. Simulation-based user testing emerged as the dominant evaluation approach, frequently supported by eye-tracking and physiological sensing technologies and subjective evaluation questionnaires. A more detailed analysis revealed that adherence to international standards (e.g., ISO 9241 and ISO 26262) was not always consistently evident. Overall, the evidence highlights substantial methodological heterogeneity, fragmented adoption of standards, and limited cross-domain comparability. While today UX and usability evaluation can benefit from continuous technological advances, the field lacks standardized and replicable assessment protocols. Future research should prioritize stronger integration of standards, multimodal evaluation approaches, and longitudinal study designs.

Keywords: evaluation protocol , HMI , usability , User eXperience , user testing

Background: Serious games (SGs) with hand-tracking solutions are increasingly adopted in neurological rehabilitation. The Leap Motion Controller (LMC) is widely used in clinical and research contexts, while software-based systems, such as MediaPipe (MP), are emerging. Objectives: This study compared the usability of MP- and LMC-based SGs. Methods: Seven SGs were developed to evaluate Grasp and Pinch gestures and tested with fifty healthy volunteers. Usability was assessed with the System Usability Scale (SUS) after each device, and an ad-hoc comparative questionnaire (LMC, MP, Neutral) collected preferences on several aspects. Results: Both systems achieved excellent SUS scores (MP: 88/100; LMC: 90/100), with no statistical difference in usability. The comparative questionnaire showed a stronger preference for LMC (63%) versus MP (17%), with 20% neutral. Conclusion: Although both solutions demonstrated high usability, LMC was more frequently preferred in direct comparison. The SUS results of MP encourage further investigation, including large-scale tests with neurological patients.

Keywords: Comparative Study , Exergaming , Hand , Motion Capture , Neurological Rehabilitation

Remote maintenance is a key function to ensure reliable, safe, and long-term operation of fusion reactors. However, handling heavy components in tight spaces is a challenging task. As part of its goals, the ITER project also has to demonstrate the feasible design, integration, and operation of a remote handling system (RHS) able to perform both preplanned tasks and recovery/rescue operations in case of failure or abnormal conditions. The ITER Remote Handling Control System (RHCS) is based on a man-in-the-loop concept due to the complexity of the tasks. In order to make more informed and aware decisions while supervising or teleoperating RH equipment movements, the operators are provided with both real images coming from on-site cameras and virtual renderings, thanks to the virtual reality (VR) system. Generally, VR renderings are made of rigid CAD models and do not consider the effects of dynamic factors on the RH equipment (RH) structure. This work shows the development activities of the structural simulator (SS) for the calibration of the RH VR models, aimed at accurately representing the real configuration the robots assume due to their own weight and the handled load.

Keywords: ITER , remote handling , robot calibration , structural simulation , virtual joints (VJs) , virtual reality (VR)

This paper presents a novel analysis of haptic actuators for touchscreen interfaces within the automotive industry. The research, distinct from existing studies, aims to identify the most suitable actuators for delivering effective and efficient haptic feedback, focusing on practical implications. Six experts reviewed three haptic effects and four actuators, providing qualitative feedback on force, quality, reactivity, and duration using a 7-point Likert scale. The test was conducted in a car-simulated environment with a car seat and a central touchscreen display. The four actuators were positioned behind the touchscreen display, each connected to separate control modules communicating via serial interface with the computer managing the Graphic User Interface. This research provides practical insights into choosing haptic actuators for automotive touchscreen interfaces, focusing on enhancing user experience and feedback. The findings can be directly applied to enhance car haptic feedback systems, improving safety, user engagement, and driving experiences.

Keywords: Automotive , Haptic Feedback , Human-Machine Interface

Virtual Reality is used in design for both supporting design reviews and interacting with products being designed. The interactions Virtual Reality enables are often found engaging and this might affect product evaluation, as experiencing a virtual environment can be the prevalent element of an experience. This paper investigated to what extent product evaluations are affected by the sense of presence enabled by Virtual Reality, the duration of interactions with the whole products and its features, and the product information provided after exposure to the virtual environment. These effects were tested through an experiment involving the virtual tour of a tiny house whose interior was extracted through 360° images, which represents an element of originality of the paper, followed by administering participants informative stimuli. All these phases were supported by eye-tracking, also thanks to the use equipment combining Virtual Reality and eye-tracking, hitherto poorly used in design research, in the first part of the experiment. Based on the experiment results, the effects studied were limited to the gazing of few design elements on some product evaluation dimensions. These findings suggest that Virtual Reality can provide a strong product perception irrespective of sense of presence and other phenomena taking place during product interaction. This is particularly stressed by the failure of informative stimuli to make experiment participants modify the initial evaluations made just after the virtual tour. Overall, the outcomes of this study encourage the use of Virtual Reality for design interaction tasks, especially in relation to environments and building interiors.

Keywords: Eye-tracking , Product evaluation , Sense of presence , Virtual reality , Visual behavior

Human–Machine Interfaces (HMIs) in autonomous driving technology have recently gained significant research interest in public transportation. However, most of the studies are biased towards qualitative methods, while combining quantitative and qualitative approaches has yet to receive commensurate attention in measuring user acceptance of design outcome evaluation. To the best of our knowledge, no standardized test procedure that combines quantitative and qualitative methods has been formed to evaluate and compare the interrelationships between different designs of HMIs and their psychological effects on users. This paper proposes a practical and comprehensive protocol to guide assessments of user acceptance of HMI design solutions. We first defined user acceptance and analyzed the existing evaluation methods. Then, specific ergonomic factors and requirements that the designed output HMI should meet were identified. Based on this, we developed a protocol to evaluate a particular HMI solution from in- and out-of-vehicle perspectives. Our theoretical protocol combines objective and subjective measures to compare users' behavior when interacting with Autonomous Vehicles (AVs) in a virtual experimental environment, especially in public transportation. Standardized testing procedures provide researchers and interaction designers with a practical framework and offer theoretical support for subsequent studies.

Keywords: human–machine interfaces , shared autonomous vehicles , standardized test procedure , test protocol , user acceptance

This paper presents a cognitive model designed to reproduce human drivers' errors in predicting the motion of nearby vulnerable road users. We aim to define a computational model that, given both the trajectory of the eye gaze of a human driver and the trajectory of a bicycle, can compute the probability distribution of where the human driver believes the bicycle will be in the near future. For the design and validation of the proposed cognitive model, we tested 51 subjects in immersive virtual reality scenarios. The results indicate that the proposed model can generate probability distributions of the human drivers' beliefs about the future bicycle position that are very similar, though not statistically equivalent, to those obtained experimentally. Such models could easily be generalized to describe how drivers misjudge the motion of other road users. This may enable ADAS to evaluate and improve drivers' situational awareness. In the future, these models could also be used by autonomous cars to evaluate situational awareness of nearby humans, enabling a safer coexistence of autonomous vehicles and vulnerable road users.

Keywords: ADAS , cognitive model , motion prediction , situation awareness , vulnerable road users

Driver distraction due to emotional states gained attention in the last decades. Even though there are various studies examined and compared various emotions, so far changes in the emotional state was not examined. The aim of this paper was to explore the emotion-ordered effect on driver cognitive workload and contribute to the emotion distraction literature and draw practical implications for road safety. To study the effect, thirteen participants recruited for a driving simulator study and they were divided into two experimental groups in which they either tried Happy-to-Angry or Angry-to-Happy emotion transitions. Participants later assigned different driving scenarios. NASA-TLX data was collected after each scenario. Results reveal no statistically significant differences in perceived mental workload across different experimental conditions or their sequences, indicating consistent cognitive demands. However, observed trends suggest subtle variations that may require larger samples or more sensitive tools for detection.

Keywords: Affective distraction , Cognitive Workload , Driver distraction , Emotion order

This study looks at how integrating biophilic interventions into driverless cars can lower stress levels and increase passenger comfort and trust. Even though autonomous car usage is increasing, public trust in the technology is still low, which has an impact on consumers' acceptance and desire to buy them. Utilizing virtual reality and a dynamic driving simulator, the research used biophilic treatments in an entertaining game. A total of thirty students took part, divided into two groups and given varying game lengths. The findings indicated that a five-minute game decreased stress by 36.5% and raised trust by 38.46%, while a shorter three-minute game considerably lowered stress by 50.96% and increased trust by 64.9%. Additionally, the group with less exposure reported feeling more at ease. The results show that stress and trust are positively impacted by biophilic encounters, implying that shorter, more immersive virtual reality experiences could more successfully increase comfort and trust. To further corroborate these results, larger sample numbers and objective data like HRV and EEG should be included in future studies.

Keywords: Biophilic Autonomous Vehicles , Comfort , Gamification , Trust , Virtual reality

In the era of Industry 4.0, the study of Human–Robot Collaboration (HRC) in advancing modern manufacturing and automation is paramount. An operator approaching a collaborative robot (cobot) may have feelings of distrust, and experience discomfort and stress, especially during the early stages of training. Human factors cannot be neglected: for efficient implementation, the complex psycho-physiological state and responses of the operator must be taken into consideration. In this study, volunteers were asked to carry out a set of cobot programming tasks, while several physiological signals, such as electroencephalogram (EEG), electrocardiogram (ECG), Galvanic skin response (GSR), and facial expressions were recorded. In addition, a subjective questionnaire (NASA-TLX) was administered at the end, to assess if the derived physiological parameters are related to the subjective perception of stress. Parameters exhibiting a higher degree of alignment with subjective perception are mean Theta (76.67%), Alpha (70.53%) and Beta (67.65%) power extracted from EEG, recovery time (72.86%) and rise time (71.43%) extracted from GSR and heart rate variability (HRV) metrics PNN25 (71.58%), SDNN (70.53%), PNN50 (68.95%) and RMSSD (66.84%). Parameters extracted from raw RR Intervals appear to be more variable and less accurate (42.11%) so as recorded emotions (51.43%).

Keywords: Cobot programming , Human monitoring , Human–Robot Collaboration , Psycho-physiological signals , Stress evaluation , Wearable sensors

The concept of Operator 4.0 has been recently defined to evolve the modern industrial scenarios by defining a knowledge sharing process from/to operators and industrial systems, creating personalized skills, and introducing digital tools towards socially sustainable factories. In this context, dynamic and adaptive user interfaces can make humans part of the intelligent factory system, supporting human work contextually and providing specific contents when needed, preserving the human wellbeing. This paper defines a human-centric methodology for the symbiotic co-evolution of operators' skills, assistive digital tools and user interfaces, developed within the Horizon Europe project titled “DaCapo - Digital assets and tools for Circular value chains and manufacturing products”. The project focuses on defining a new set of human-centric digital tools and services for the manufacturing industry capable of boosting the application of circular economy (CE) throughout the manufacturing value chains. The proposed methodology can link the specific needs of an industrial case to the definition of the most proper assistive digital tools and functionalities to drive the design of adaptive, proactive user interfaces for the Operator 4.0. The method has been applied and validated on one of the project use cases, involving a manufacturing company operating in warehousing and logistics.

Keywords: Adaptive interfaces , Circular economy , Human-Machine Interfaces , Operator 4.0 , User experience design

In the realm of smart manufacturing, Extended Reality (XR) technologies stand as transformative tools capable of deeply reshaping the industrial landscape. XR has been extensively adopted across various industries, particularly for optimizing complex tasks such as product maintenance and repair. Nevertheless, many industries continue to rely solely on traditional approaches, resulting in low efficiency and productivity. This is often due to the challenges encountered in adopting effective XR solutions to provide comprehensive instructions during maintenance activities. This paper introduces an innovative XR platform tailored to foster collaboration between the technical office and maintainers. Specifically, this platform facilitates maintenance and repair operations on industrial products through close collaboration between a skilled user utilizing a VR application and an inexperienced operator utilizing an AR application. This cooperation is made possible by integrating and concurrently deploying these applications, which communicate via a central server managing the connection in a host/client model.

Keywords: Collaborative platform , Extended Reality , Maintenance

This paper investigates the relationship between brain activity, measured by electroencephalography (EEG) data, and the performance assessment result of engineering design activities involving different cognitive processes. Employing a novel signal processing pipeline, we analyzed EEG variations of 37 subjects during two design tasks that mostly leverage, respectively, convergent and divergent thinking: the design with morphological table (task and the problem-solving task. The EEG recordings underwent meticulous artifact removal, allowing for a comprehensive investigation into the statistical relationships between frequency bands, channels, and design outcome performance metrics. The developed models linking better design outcomes with brain (de)synchronization demonstrated remarkable accuracy, precision, and recall across performance metrics for both tasks. Notably, the EEG data in theta band measured from the frontal area at both hemispheres and a left parietal/occipital channel were essential for estimating better design performance with brain desynchronization. On the contrary, the model based on brain synchronization produces precise estimations of design performance with alpha band and channels in temporal and parietal areas. These findings highlight EEG variation as a viable proxy for design performance, paving the way for more effective performance prediction models with fewer sensors.

Keywords: conceptual design , creativity and concept generation , design evaluation

The paper presents an overview of current training endeavours for technology-related/professional skill acquisition in immersive environments together with educational/training needs that emerge from the literature. The aim is to establish a general framework for the design and development of educational initiatives for lifelong learners, both HEI students and professional. The paper details how immersive technologies enable a wide variety of skills (e.g. psychomotor, spatial, procedural, decisional skills) to be acquired and trained while reducing the costly and risky conditions of on the field training. On the other hand, the framework will also display how the immersive technologies might trigger high engagement and motivation in active/student-centred learning contexts, making them suitable to provide meaningful learning experiences for the largest possible variety of learning styles, which are vital to ensure life-long learning and embrace the principles of pedagogy and andragogy.

Keywords: Education Framework , Immersive Environment , Professional Skill Training

Digital transformation of industrial training processes is still an ongoing process. The transformation requires a specific training model to be applied to an already structured context, to experienced workers, sometimes with their needs and specific skills, and the solution is designed considering a series of boundary conditions. To design a new training model that the industry will accept, it is important to consider the design space offered by technology, but at the same time, to consider human complexity. This paper proposes a methodology to analyze current training strategies in the industrial sector to design new tools according to a digital transformation logic that considers the underlying principle of the human centricity of Industry 5.0.

Keywords: Digital transformation , Industrial training , Industry 5.0

No abstract available

One critical aspect in the design of an exoskeleton is its goodness of fit on the human body, preventing the generation of high pressures, which are commonly related to the perceived comfort. In this study, a multibody model of both the exoskeleton and the human body was set up, with the aim of providing a numerical tool for the ergonomic assessment of these devices. Contact force parameters were tuned to agree with experimental data found in literature, reporting peak pressure at the shoulder for a load carrying system. Then the multibody model was used to simulate a specific action (stoop) and it was validated against experimental data. Both strap tension required for the stability of the exoskeleton system and the relative displacement of the device during a 90° trunk flexion were assessed. The model was able to predict the actual system behaviour, with deviations of 7.6% and 8.

Keywords: Anthropomorphic ATB , Contact Pressure , Ergonomic Design , Exoskeleton , Multibody Model