Human factors and ergonomics

Food spoilage is a pervasive issue with significant implications for public health, economic stability, and environmental sustainability. Addressing food spoilage is essential to promoting healthier living, reducing economic waste, and ensuring sustainable practices. When storing food in refrigerators, it would be crucial to monitor at least factors such as temperature, humidity, and signs of spoilage to ensure food quality and safety. The paper presents a concept for a smart fridge equipped with sensors to monitor critical factors such as temperature, humidity, and spoilage indicators. These sensors collect real-time data, which can be visualized through a multisensory eXtended Reality (XR) Digital Twin that provides a highly realistic 3D visualization of the real fridge, incorporating dynamic temperature simulations represented through 3D color maps and an olfactory simulation that replicates the smell of rotten food. The first impressions of the Multisensory XR Digital Twin emphasize its effectiveness in bridging the gap between raw data and user perception, allowing for an immersive interaction with the fridge as if physically present. Key benefits identified include enhanced spatial awareness and realism, multisensory feedback, and greater user engagement.

Keywords: Circular Economy , Digital Twins , eXtended Reality , Multisensory experience

While the benefits of biophilic design, incorporating natural elements to promote well-being, are well-demonstrated in Immersive Virtual Reality (IVR) environments, its application remains underexplored. Specifically, no studies have clarified how biophilic elements should be spatially and contextually integrated into IVR environments. Most studies focus on the presence or type of elements rather than on design strategies or spatial criteria. This gap is critical in IVR, where the absence of real-world constraints enables a high degree of design freedom, potentially leading to ineffective or inauthentic integration. To address this issue, we engaged 13 expert designers in a focus group to develop guidelines for biophilic design in IVR workspaces. Each expert was asked to enrich four virtual offices using four types of indoor plants: floor plant, table plant, hanging plant, green wall. Their insights were synthesized into practical spatial design guidelines. These were then combined to generate 16 biophilic configurations, evaluated by 81 users. Results indicated no statistically significant preference among configurations, suggesting all combinations were similarly received. In a second evaluation phase, 117 participants compared four specific configurations: based on guidelines; with an unconstrained (but realistic) spatial arrangement; with an unrealistic spatial arrangement; without plants. The guidelines-based configuration significantly outperformed the others across subjective measures including willingness to work, focus, and comfort. These findings suggest the proposed guidelines are effective in supporting the design of engaging, pleasant biophilic IVR environments.

Keywords: Biophilic Design , Immersive Virtual Reality , Well-being , Workspace

Collaborative robotics is increasingly considered in manufacturing to improve efficiency while reducing operators physical and cognitive workloads. However, the lack of comprehensive methodologies has limited the consistent implementation of human–robot collaborative workstations across industries. Existing approaches are often fragmented, require robotics expertise, and pose challenges for non-experts, leading to suboptimal station designs and inefficient task allocation. This study presents a structured design framework to transition traditional assembly processes into collaborative ones. The framework provides a practical, scalable solution for optimizing collaborative workstations, balancing performance, ergonomics, and industrial applicability. It starts from the analysis of the assembly tasks, followed by classification and allocation between human operators and robots, and concludes with virtual prototyping and performance optimization through simulation using a commercial tool. The adopted methodology integrates task analysis, ergonomic assessment, and workspace design to ensure accessible and efficient implementation. Validated through two industrial case studies involving a gear pump and a worm gearbox, the approach demonstrated significant reductions in cycle time and notable improvements in the ergonomic working conditions. Additionally, physical prototyping and testing conducted within a research collaborative cell further confirmed the achieved results.

Keywords: Collaborative assembly , Collaborative robotics , Human–robot simulation , Integrated design framework , Task allocation , Virtual prototyping

Despite the increasing environmental awareness, sustainable products are not routinely adopted, revealing a persistent attitude–behavior gap. While this gap has been widely studied through psychological and behavioral approaches, less attention has been given to how product design attributes shape users' perceptions and influence their interaction with products. Therefore, this article aims to explore the literature on the role of design attributes in this context, analyzing how product design attributes and sustainability cues affect user experience and product-related decisions. Based on a systematic literature review process, 52 articles were extracted from the SCOPUS database. These articles were classified and analyzed according to their main objectives. Based on the findings, attribute centrality (i.e., whether a product feature is perceived as central or peripheral to the product's identity) and product category (utilitarian or hedonic) are relevant to shape environmental attitudes and behavior. Specifically, sustainability cues tend to be perceived more negatively in utilitarian products, where functionality is prioritized, and more positively in hedonic products, where they may enhance emotional value, especially when perceived as central attributes. Therefore, these characteristics must be strategically integrated in product design to reduce the perceived trade-off between sustainability and performance. The review conclusions highlight the need for further investigation to identify how specific product design attributes resulting from the application of ecodesign principles trigger sustainability perception.

Keywords: Attitude–behavior gap , Design attributes , Product category effects , Sustainability perception , User perception

In rehabilitation, the proper use of robotic devices offers significant advantages in both safety and treatment effectiveness. Correct use specifically includes accurate positioning of the user on the device to prevent misalignments that could result in discomfort or pain, and isolation of the movement to the intended body segment to avoid compensation strategies with other joints. This paper presents the design and development of an innovative wrist alignment and locking system implemented on the EDUSA® PRO robotic rehabilitation device. The proposed mechanism enables automatic centering of the human joint, aligning the user's axes with those of the robot, and securely maintains the wrist position throughout the rehabilitation session. Additionally, it automatically adapts to different wrist sizes and user anthropometric characteristics, making it suitable for a wider range of individuals. The ergonomic design ensures comfort, ease of use, and safety, while the modularity of the system allows for application to other joints. These features help reduce setting errors by therapists and contribute to greater effectiveness and reliability of the rehabilitation process.

Keywords: Product Design , Rehabilitation Robotics , Self-Centering Mechanism

Car seat comfort represents a fundamental dimension of automotive ergonomics, yet its assessment and optimization remain predominantly reliant on subjective feedback and iterative physical prototyping, limiting scalability and responsiveness in the design process. This study presents a novel data-driven framework that synergistically integrates statistical analysis and deep learning architectures enhanced with attention mechanisms to predict perceived driver discomfort from pressure distribution patterns. A comprehensive dataset acquired from 192 participants was used to extract biomechanical descriptors, such as contact area and peak pressure, across 9 anatomical regions, which were subsequently correlated with subjective discomfort ratings collected on a 10-point Likert scale. The proposed hybrid deep neural network achieved a modelling accuracy of R2 = 0.82 for overall discomfort, with peak pressure emerging as the most influential factor (Spearman's r = 0.78). Moreover, Finite Element Analysis (FEA) was employed to validate geometry refinements suggested by the AI model, resulting in an 18% reduction in peak seat pressure without compromising structural integrity. The findings underscore the transformative potential of integrating artificial intelligence with CAD–CAE methodologies to enable rapid, user-centered, and simulation-driven ergonomic design early in the vehicle development cycle, minimizing the need for physical prototypes and enhancing occupant comfort.

Keywords: AI-Driven Comfort Models , Deep Learning with Attention Mechanisms , Ergonomic Seat Design Optimization , Finite Element Validation of Human-Centric Design , Pressure Mapping and Biomechanical Analysis

Background: Pumping in Laser-class sailing is a dynamic propulsion technique used in marginal wind conditions and characterized by repetitive, coordinated oscillations of the sailor–sail system. Despite its practical relevance, its biomechanical and ergonomic demands remain insufficiently characterized. Methods: A mixed-methods framework was applied combining questionnaire data, kinematic analysis, ergonomic assessment, and musculoskeletal modelling. Thirty-six competitive Laser sailors completed a Borg CR-10-based questionnaire on perceived discomfort/fatigue across body regions at predefined time points (during pumping, immediately after training, and the following day). A controlled land-based multi-angle video acquisition was used to reconstruct a standardized pumping posture and parameterize a digital human model in DELMIA® for postural/kinematic analysis. Ergonomic risk was assessed using REBA, and muscle activity was estimated using the AnyBody® Modeling System (simulation-derived normalized muscle activity across 129 muscles). Results: the simulation identified high neuromuscular demand in the trunk and shoulder complex, with several deep trunk stabilizers and the left latissimus dorsi reaching 100% modeled normalized muscle activity. Marked lateral asymmetry was observed, with right-sided trunk dominance and left-sided shoulder dominance. Kinematic analysis showed substantial joint excursions, with large lumbar motion amplitudes, while REBA yielded a score of 11 (Very-High Risk). Questionnaire data indicated a high prevalence of pumping-related musculoskeletal discomfort (72.2%), most frequently involving the lower back, shoulders, and knees. A dissociation was observed between modeled muscle activity and perceived fatigue, with the lower limbs rated as most fatigued despite lower modeled activation than the trunk. Conclusions: Findings identify the deep trunk stabilizers, latissimus dorsi, and lower extremities as key regions involved in pumping, with marked lateral asymmetry and high ergonomic risk. They support targeted training, injury-prevention, and ergonomic strategies to improve performance and reduce injury risk in competitive sailing.

Keywords: discomfort , laser sailing , muscle activation , musculoskeletal simulation , pumping maneuver , REBA ergonomic assessment

The global demographic shift toward an aging population presents significant implications for automotive design and safety. This research addresses a critical gap in the literature by conducting a comprehensive ergonomic assessment of elderly drivers during two essential driving tasks: reverse gear maneuvers and seat belt engagement. Through a methodologically rigorous approach combining quantitative discomfort evaluations and postural analyses, this study examined a sample of 20 elderly people, a B-segment car and a strategically positioned recording equipment. The investigation revealed statistically significant patterns of discomfort concentration in the upper body regions, particularly the shoulders and cervical area, with Rapid Upper Limb Assessment (RULA) scores indicating substantial musculoskeletal risk factors. The findings demonstrate that conventional automotive configurations impose asymmetric postural stresses that are particularly problematic for elderly users due to age-related physiological changes. This research contributes to the theoretical understanding of aging-specific ergonomic requirements and provides empirical evidence supporting the necessity for adaptive design modifications to enhance safety, comfort, and continued mobility independence among the elderly population.

Keywords: Elderly population , Ergonomics , Postural Discomfort

The use of multi-layered foam pad as filler for cushions, seats components and furniture, is widely spread in many industries, especially in car-seat manufacturing ones. The mechanical behavior of these materials is directly connected to the perceived comfort and discomfort by users. This work presents a numerical investigation of the mechanical response of two foam materials under localized compression loading: a standard polyurethane foam and a viscoelastic memory foam. The analysis focuses on two key phenomena relevant to comfort applications: pressure absorption, i.e. the material's ability to attenuate peak stresses, and pressure diffusion, i.e. the spatial redistribution of contact forces across internal layers. Finite Element Method (FEM) simulations were performed on a layered foam block (200 × 200 × 100 mm), indented centrally by a rigid cylindrical indenter (30 mm diameter) up to 50 mm depth. The block was discretized into ten 10-mm-thick layers, enabling pressure analysis throughout the thickness. Results show that memory foam achieves significantly lower peak pressures and smoother vertical gradients compared to standard foam, indicating superior performance in both absorption and diffusion. Conventional foam, while more resilient, exhibits greater pressure concentration at deeper layers. These outcomes support the use of memory foam in applications requiring high comfort and soft tissue protection, such as bedding, seating, and wearable protective equipment. Moreover, the study highlights FEM is effective for predicting pressure behavior in layered, deformable media.

Keywords: Finite Element Method , Pressure distribution , Viscoelastic foams

Background: In-depth studies highlight that trust is essential for the effective interaction with autonomous vehicles, which, have not yet gained public trust. Objective: The aim of this study was to develop and validate a comprehensive questionnaire to evaluate people's trust in autonomous vehicles. Methods: After identifying influential factors in trust by interviews and brainstorming sessions, 75 questions across 5 dimensions were developed, which were then narrowed to 69 in 3 dimensions using “thinking aloud”. This version was assessed by 24 experts, resulting in 19 responses and the exclusion of 22 questions based on CVI and CVR. Finally, the reliability was determined using Cronbach's Alpha after an experiment with 24 participants. Results: A 47-item questionnaire, with 3 dimensions including personal, social, and technical factors, and 21 sub-dimensions was developed. The lowest CVI (0.63) was for “mental complexity” and the highest (0.90) belonged to “personality”. The least CVR (0.56) for “meaningfulness attitude” was acquired, while the highest CVR (1.00) was recorded for 9 questions. Total Cronbach's Alpha coefficient (0.93) showed satisfactory reliability. Conclusions: This validated instrument offers a comprehensive tool for evaluating trust in AVs for future studies.

Keywords: automated driving , autonomous vehicles , questionnaire , reliability , trust , validity

Comfort is a pivotal construct in human-centered design, connecting technical functionality with well-being across products, services, and systems. The 2023 International Comfort Congress (ICC2023) brought together researchers and practitioners to explore how new contexts of use, predictive approaches, and user diversity are reshaping this field. Contributions in this special issue address comfort in automated driving and turboprop aviation, where physical, psychological, and environmental factors converge. Advances in predictive comfort science demonstrate how objective measures, physiological sensing, and modeling complement traditional self-reports. At the same time, inclusive design approaches—ranging from XR-based co-creation to analyses of posture and movement variability—highlight the importance of accounting for heterogeneous user needs and comfort trade-offs. Together, these studies illustrate a shift toward more adaptive, accessible, and sustainable systems, underscoring comfort as a multidimensional construct that evolves with technological innovation and societal change.

Keywords: accessibility , automated driving , comfort research , human-centered design , predictive modeling , user diversity

Nowadays, to counteract the sedentary behavior in children, playground parks with their pieces of playground equipment can have a significant impact. In this context, the paper proposes a methodology based on wearable surface electromyography and inertial sensors data to derive functional data sheets for each piece of playground equipment and two novel synthetic indices for the assessment of physical fitness and motor coordination. These indices are built from: (i) muscle activation of nine forearm and leg muscles; (ii) acceleration data from two inertial sensors located on the forearm and lower back. The synthetic indices are based on the assessment of the muscular force rate and motor control in terms of smoothness. The validation of the proposed methodology is carried out through field tests, on seven pieces of playground equipment. The results confirm that: (i) the functional data sheet offers the possibility to obtain quick and intuitive visualization of the pieces of playground equipment in terms of required muscle activation; (ii) the proposed methodology represents a reliable and valuable tool to estimate physical fitness and motor coordination. This approach can be implemented in different playground parks even to guide the choice of the equipment to ensure specific levels of physical fitness and motor coordination.

Keywords: motor coordination , physical fitness , playground equipment , wearable sensors

Evaluations of injuries and the assessment of residual dexterity is carried out by clinicians by means of standardized tests. Several dexterity tests have been developed and all of them require the test administrator to sit in front of the subject and visually assess the motions performed. Automatic tools can reduce the time needed for the evaluation and can provide additional useful information. For many of the available dexterity tests, automated evaluation methods have been developed. However, for the Minnesota dexterity test, despite being widely used, just one automated solution based on multiple depth sensor cameras was developed. This study aims to provide a wearable and flexible method to automatically evaluate the outcomes of Minnesota dexterity tests. The proposed methodology consists of a parametric model that is capable of processing acceleration data collected with an IMU attached to the centre of mass of the subject's dominant hand. The developed model was successfully validated against a subject study with ten participants.

Keywords: Automatic assessment , Biomechanics , Manual dexterity , Parametric model , Wearable devices

This vision paper presents a prospective architecture for the phased deployment of intelligent and connected vehicle (ICV) systems under mixed traffic conditions. We propose a Vehicle–Road–Cloud–Human (VRCH) integrated architecture to address key technical and socio-technical challenges in autonomous mobility, including edge-case handling, behavioral predictability, and infrastructure compatibility. The architecture reallocates decision-making across distributed roadside infrastructure and cloud services to reduce vehicle-side burden, while ensuringsafety, efficiency and user trust. A phased deployment roadmap for Dedicated Autonomous Lanes (DALs) is introduced, featuring digitally credentialed access, in-transit transition mechanisms (ITTM). Emphasizing human-machine interaction during mode transitions, the framework supports scalable, cost-effective deployment without capital-intensive infrastructure overhaul. Future work includes pilot zone deployment, infrastructure co-design, and scenario-based validation to support the evolution to realize a robust, human-centered intelligent transport systems (ITS) ecosystem.

Keywords: 5G-V2X , Dedicated Autonomous Lanes , Human Factors in Automation , Human–Machine Interaction (HMI) , Intelligent transport systems (ITS) , Intelligent Transportation Infrastructure , Mobility System Design , Urban Autonomy , Vehicle–Road–Cloud–Human (VRCH) Architecture

This study investigates how the immersion level, experienced by users during their interaction with a virtual museum application, affect Visitor Experience and Learning Performance. A between group experimental study, has been conducted in a laboratory setting. A total of 60 participants has been randomly divided in two groups (A and B). Group A used a virtual museum application to interact with two digital replicas of archaeological finds via a low immersive desktop-based interface. Group B interact with the same application using a high immersive HMD-based interface. Immediately after the interaction with the VR application, participants were asked to answer a questionnaire in order to measure their perceived visitor experience (VX), their attitude toward the experience and their learning performance in terms of immediate knowledge retention. The results suggest that the use of HMD-based interface can improve the quality of VX and the attitude toward the experience reported by the participants, but it can negatively affect the resulting learning outcome. These findings emphasize the need for museum curators to find a balance between utilizing technology for attract new visitors and preserving the educational value of the exhibits.

Keywords: Immersion , Learning Performance , Virtual Museum , Virtual Reality , Visitor Experience

Ankle injuries are common due to the joint's role in weight-bearing and dynamic motion. Finite element method (FEM) has become a valuable tool for understanding joint biomechanics and planning clinical interventions. However, many existing models are limited by static simulations, simplified geometry, or generalised approaches that do not focus specifically on the talus-tibial joint. This study presents a subject-specific FEM framework of the ankle, integrating magnetic resonance imaging (MRI) and gait data from a healthy 49-year-old male. The tibia and talus were reconstructed from MRI and cartilages were modeled using a custom method to compensate for limitations in image resolution. Kinematic data was collected using the Xsens motion capture system and applied to a dynamic FEM developed in Abaqus. The model yielded contact pressures between 1.2 and 21.8 MPa and maximum von Mises stresses up to 5.7 MPa. The stress distribution was predominantly lateral, consistent with known biomechanical patterns. This framework allows realistic simulation of ankle mechanics and provides a basis for assessment of pathological conditions and surgical planning.

Keywords: 3D Modeling , Ankle , Finite Element Analysis , Gait Analysis

With the growing popularity of cycling and walking as modes of transportation, pedestrians and cyclists are often considered vulnerable road users due to their lack of safety and the risk of injuries in collisions with other road users or each other. Understanding pedestrian safety perceptions in mixed-use urban environments is essential for developing safer shared infrastructure. This study analyzes pedestrian risk perception in three cyclist–pedestrian interaction scenario: a blind corner, a crosswalk, and a shared path. Unlike prior studies that focus on pedestrian-vehicle interactions, this study examines perceived risk in cyclist-pedestrian environments using both virtual reality (VR) and matched real-world video conditions. A total of 42 participants experienced all scenarios and provided subjective risk ratings. Results from paired sample t-tests revealed no significant differences in perceived risk between VR and video conditions for all scenarios (p -value ' 0.05). However, shared path conditions showed the highest perceived risk, underlining the need for infrastructure designs that reduce user conflicts, such as physical separation. The study extends the validated use of VR to cyclist–pedestrian conflict scenarios within pedestrian safety research and underscores the importance of designing shared facilities that accommodate the needs of all non-motorized users.

Keywords: Pedestrian–cyclist interaction , Risk perception , Road safety , Virtual reality , Vulnerable users

Road safety is still a critical concern for all countries of the world since most of the reports state that traffic fatalities remain very high. Despite the reduction of fatal situations while driving, traffic collisions, especially due to unsafe behaviors, are the leading cause of death among young individuals. Governments use safety campaigns to promote safer driving, but traditional methods such as videos conveyed through 2D screens may have a limited impact. Cinematic Virtual Reality (CVR), with its immersive features, could offer a more effective way to deliver road safety messages. This study proposes a between-subject design to explore how CVR influences attitudes, behaviors, and physiological responses related to traffic safety. A sample of 95 young participants, split into two groups, watched either a fear-based or a positive-based 360° CVR video. They completed the Attitudes Towards Traffic Safety (ATTS) and Driving Behavior Questionnaire (DBQ) at different stages (before and after CVR experience and in follow-up condition), as well as a User Experience Questionnaire (UEQ) and a Virtual Reality Sickness Questionnaire (VRSQ). ECG data were collected during the CVR experience to assess physiological response. The results demonstrate that CVR significantly impacts safety attitudes and behavior, with a statistically stronger effect observed in the fear-based approach. Furthermore, CVR markedly impacts users' physiological responses, as evidenced by ECG analysis. Physiological data underscore CVR's effectiveness in engaging users and reveal that responses vary significantly based on the nature of the immersive experience, highlighting CVR's potential as a targeted intervention tool for traffic safety education.

Keywords: Cinematic virtual reality , Educational intervention , Physiological response , Risky driving , Road safety , Young drivers

Immersive Virtual Reality (IVR) has become increasingly important in cultural heritage applications. Recent advances, such as the integration of virtual agents and Artificial Intelligence (AI), have further reshaped the IVR landscape, paving the way for new applications with potential impact. Despite extensive research on their application, the user experience and usability of cultural content for end users remain largely overlooked. This study aims to conduct a preliminary analysis of an AI-powered virtual agent's impact on users in the context of cultural heritage. Participants could interact freely with the virtual agent, simulating a conversation about questions and curiosities as they would with an expert museum guide. A user study has been conducted, comparing two methods of information delivery in an IVR museum environment: a traditional explanatory panel and an AI virtual agent. Metrics collected were the time spent during each condition, the systems' overall user experience, and usability. The results suggest that virtual agents capture the user's attention more and encourage prolonged interaction due to novelty. However, the study also highlights the need to combine both methods to achieve optimal user engagement and usability.

Keywords: Artificial Intelligence , Cultural Heritage , Immersive Virtual Reality , User Experience , Virtual Agent

Physical ergonomics is critical for worker well-being, safety, and productivity, in Industry 4.0 and in the emerging Industry 5.0 paradigm, which emphasize human-machine collaboration. Advances in motion capture technologies, especially marker-less systems using RGB cameras, offer new possibilities for real-time ergonomic assessments. These systems effectively address the intrusiveness issues inherent to marker-based approaches and partially overcome challenges such as lighting variations, occlusions and calibration commonly associated with RGB-D camera-based systems. Nonetheless, their accuracy can be suboptimal due to limitations arising from perspective distortions. To overcome this issue, the present paper introduces an “inductive” RGB camera-based approach to ergonomic assessment, leveraging Deep Learning models trained on a synthetic dataset of 14,400 unique postures captured from 24 viewpoints in Unity 3D. Unlike traditional methods reliant on measured angles, this approach predicts ergonomic evaluations directly from posture observations. We implemented the proposed approach on a prototypal system that allowed us to experiment and collect quantitative results. We analysed the system's performance through comparative testing, juxtaposing predictions from our system with measurements obtained from an inertial system worn by individuals while performing tasks in a controlled laboratory environment. Experimentations highlighted the satisfactory performance of the proposed inductive approach and related technology, highlighting its potential for delivering more accurate real-time ergonomic assessments.

Keywords: Deep learning , Ergonomics , Human centred manufacturing , RGB motion capture , Synthetic datasets