Human factors and ergonomics

The human-centric approach of Industry 5.0 underscores the integration of advanced technologies and information systems to enhance worker well-being, productivity, and safety. Significant progress has been made in automation and digitalization; however, the high prevalence of work-related musculoskeletal disorders (WRMSDs) remains a critical challenge, translating into a significant socioeconomic burden. Nevertheless, industrial practice still predominantly relies on observational ergonomic assessment methods and reactive ergonomic strategies, creating an urgent need for flexible, proactive, individualized, and easy-to-implement risk mitigation approaches. This paper addresses this gap by proposing an intelligent decision-support system based on a multi-objective optimization model that integrates heterogeneous information - workers' anthropometric measures, task requirements, and personal habits - within an expert-system architecture for industrial applications. The method relies on a convex integer program that can be embedded in machine controllers to compute the relative position between the product and the operator, minimizing ergonomic risks. Key innovations include the adoption of ergonomic principles without complex inverse kinematics, the explicit involvement of workers to account for their preferences, and the joint consideration of tasks involving both visual and physical interaction with the product. Experimental validation was conducted in a virtual environment simulating typical manufacturing scenarios, with diverse users and products. Results showed significant reductions in ergonomic risks with optimized positions, especially for smaller products, whereas larger ones posed challenges due to their size and task distribution. Statistical analyses validated these findings, highlighting the model's potential to reduce the REBA (Rapid Entire Body Assessment) risk index and enhance operational efficiency. Overall, the proposed system provides actionable set-points for workstation configuration and practical guidance for implementation, thus supporting human-centric manufacturing in Industry 5.0.

Keywords: Ergonomics , Intelligent decision-support systems , Manufacturing , Mathematical programming , Multi-objective optimization , Work-related musculoskeletal disorders

The use of immersive virtual reality (VR) technologies for ergonomic assessment is gaining increasing relevance as digital tools become integral to human-centered design processes. However, questions remain regarding the reliability and representativeness of postural evaluations conducted in virtual environments compared to those performed in the real world. This study explores the effectiveness of virtual ergonomic analysis by applying a structured, sensor-based protocol to two manual assembly tasks executed in both real and virtual settings. Full-body motion capture and the RULA method were employed to objectively assess postural risk, with consistent task sequences and workstation setups ensuring comparability across conditions. By analyzing and comparing results from both environments, the study provides insights into the strengths and current limitations of VR-based ergonomic evaluations. Findings indicate that immersive VR is a promising tool for early-stage ergonomic analysis. Nevertheless, limitations emerged in tasks involving high physical demands or requiring precise manual alignment, where the absence of physical feedback in VR may lead to an underestimation of postural risk. These observations underscore the need to improve interaction fidelity to enhance the validity and applicability of VR-based ergonomic assessments in industrial contexts.

Keywords: Digital Human Model , Ergonomics , Virtual Reality

Recent studies show that most Work-Related Musculoskeletal Disorders (WMSDs) of the hand and wrist are the result of repeated stresses, caused by repetitive activities over a long time. Tools like ergonomics risk indexes, have been developed to assess upper limb overload in repetitive tasks: part of this evaluation is based on the observation of the number and frequency of Technical Hand Grips (THGs), differing by the number of fingers involved, the exerted forces and most affected regions. Ergonomists typically assess THGs manually via video review, which is time-consuming: a need arises for the automation of these procedures. The rapid and functional creation of AI models capable of classifying efficiently THGs presents some particular challenges: (1) labeling a huge amount of images at ergonomist-level, since, to the best of our knowledge, there are no useful datasets available; (2) considering object occlusions as an intrinsic requirement of realistic study of THGs; (3) moving towards the shared request of data anonymization. This work proposes an Active Learning system in which an initial model labels THGs from videos and consults an Oracle (ergonomist) only when uncertain. An implemented AI (GNN) model is trained on these carefully labeled data to replace the first rudimentary model. The process repeats until the desired model accuracy or data set size is reached. This has guaranteed: (1) a rapid labeling strategy and scaling of the THGs classification model that learns from selected “good” data; (2) implement an occlusion-robust model, by applying selective masking strategies; (3) anonymization through graph-structured data.

Keywords: Active Learning , Ergonomics , Graph Neural Network , OCRA Index , Technical Hand Grip Classification

The transition to Industry 5.0 emphasizes human-centric automation, prioritizing ergonomics, cognitive adaptability, and real-time collaboration between humans and machines. Traditional automation strategies have focused primarily on efficiency and standardization, often overlooking human operators' cognitive and physiological workload. This paper presents an AutomationML-based (AML) digital framework that integrates user experience (UX) approaches and human-in-the-loop methodologies to optimize human task design by harmonizing human cognitive states, physiological data, and real-time behavioral insights from industrial digital assets and the physical factory. The presented approach extends the recently defined AML standard by introducing a structured human taxonomy, a UX database architecture, and an enhanced task modeling and adaptation strategy based on the operators' feedback. An industrial use case about quality inspection is presented to validate the proposed framework, where an assisted inspection system integrates augmented reality visualization with human physiological monitoring using a sensorized wristband. This framework enables real-time task adaptation based on the estimate of the operator's workload, reducing cognitive overload and optimizing task allocation between humans and machines. Results on the use case suggest how UX-driven automation improves both process performance and workers' well-being, as well as the overall system flexibility. This research contributes to the development of ergonomic and cognitive-aware automation solutions that enhance collaboration between humans and machines.

Keywords: Augmented Reality , AutomationML , Human-Centered Design , Human-In-The-Loop (HITL) , Industry 5.0 , User Experience

The integration of emotional monitoring technologies in logistics represents a significant leap in enhancing operational efficiency and worker well-being. As the logistics evolves, understanding the acceptance and adoption of such technologies becomes crucial. This study employs the extended Technology Acceptance Model (TAM) as a framework to investigate the determinants influencing logistics workers' acceptance of emotional monitoring technologies, with a focus on facial expression recognition (FER), electroencephalography (EEG) and a bundle of physiological measures including Electromyography (EMG), Electrocardiography (ECG), and Galvanic Skin Conductance (GSC). We aim to explore how perceived usefulness (PU) and perceived ease of use (PEOU), core constructs of TAM, along with external variables belonging to social, individual and system levels, contribute to this acceptance. The questionnaire was administered to 45 warehouse operators from a logistics company in Italy. Results show that TAM models are able to explain the acceptance of these technologies in the examined working environment.

Keywords: facial expression recognition , human factors , industry 4.0 , logistics , physiological technologies , Technology acceptance model (TAM)

Objective: Joint replacement surgery, also known as arthroplasty, is a common procedure that restores mobility and relieves pain in patients with severe joint pathologies. Despite being considered routine, arthroplasties are complex interventions with potential complications and variable clinical outcomes. Accurate evaluation of replaced joint mobility to ensure implant stability within the patient's functional range of motion (ROM) is a major challenge in postoperative care. However, the reliability of current assessment methods is limited due to their lack of standardized and quantitative tools. This study presents a patient-specific Mixed Reality (MR) framework designed to enhance postoperative evaluation in joint replacement with a focus on total hip arthroplasty (THA). Methods: The proposed system enables objective quantification and MR visualization of prosthesis biomechanics by integrating ROM simulation and 3D modeling, promoting explainability and interpretability of surgery outcomes. A retrospective analysis of 67 THAs was performed to compare simulated ROM results with clinical assessments and literature benchmarks. Additionally, surgeons evaluated the system's clinical relevance and usability through a preliminary study, including completion of the System Usability Scale (SUS). Results: Simulated ROM measurements showed good agreement with both clinical assessments and established literature reference values across ten movements commonly examined in orthopedic practice. The MR tool demonstrated high accuracy, repeatability, and potential to support postoperative decision-making, with usability testing yielding a favorable median SUS score of 82.5, indicating strong acceptance among clinicians. Conclusion: The patient-specific MR framework provides a reliable, quantitative, and interpretable method for assessing prosthetic joint performance after replacement, supporting its integration into postoperative workflows for improved surgical outcome assessment.

Keywords: Computer-aided surgery , Human-computer interaction , Joint replacement , Mixed reality , Orthopedic surgery , Total Hip arthroplasty

Musculoskeletal disorders are frequent workplace injuries, especially during manual lifting activities. They are influenced by posture, lifting technique, and repetitive movements. Various ergonomic assessment methods exist, but each has limitations: observational methods can be slow and prone to error, while contact sensor-based methods, although more accurate, tend to be invasive and expensive. Recent developments have focused on non-contact sensors, such as RGB and RGB-D cameras, combined with Deep Learning algorithms and observational methods, to improve efficiency and reliability. This study proposes a solution combining a skeleton-based Deep Learning algorithm for Human Pose Estimation with an observational method for postural assessment. Using an RGB camera, four lifting techniques (stoop, squat, semi-squat, and weightlifter) were analyzed, evaluating their impact on worker posture through the REBA score. Among handle-assisted lifts, the stoop and weightlifter techniques showed the lowest average maximum REBA scores (5.375 and 6.125), while the squat and semi-squat techniques scored highest at 7. The semi-squat without handles showed the greatest postural risk (7.875). Future work will integrate 3D data and validate the approach with a larger, more diverse population.

Keywords: Deep learning , Ergonomics , Industry 5.0 , Postural analysis

Background: In recent years, the growing adoption of Virtual Reality (VR) and 3D printing technologies has revolutionized surgical training by providing innovative opportunities for hands-on education. This study investigates the combined use of VR and 3D printed personalized anatomical models and cutting guides within the field of oral and maxillofacial oncologic surgery. Materials and methods: A mandibular tumour case was developed using the proposed approach, integrating both virtual and physical tools. Feedback was gathered from twelve surgical residents regarding their understanding of the case, the effectiveness of the immersive and three-dimensional technologies, and their overall satisfaction with the training experience. Results: Participants reported enhanced comprehension of complex surgical scenarios and valued the practical utility of the VR simulator combined with 3D printed models. The immersive environment facilitated skill acquisition in a risk-free setting. Conclusion: The findings underscore the significant added value of integrating VR and 3D printing technologies in surgical training, preparation, and simulation. This approach offers a safe, effective training platform that improves readiness for complex procedures in oncologic surgery and has the potential to be extended to other branches of maxillofacial surgery.

Keywords: fibula free flap , mandibular reconstruction , maxillofacial surgery , surgical education , three-dimensional printing , user-centered design , virtual reality

Image-generation tools have been widely adopted in product conceptual design, showing promise in supporting cognitive activities and fostering creativity. However, existing studies have primarily focused on the aggregate-level impact, overlooking individual differences in designers' cognitive styles, making it difficult for AI to understand and adapt to the needs of different groups. To fill this gap, we aim to explore whether and how image-generation tools affect designers' cognitive styles and to analyze the relationship between cognitive style and design performance. Through a within-subject experiment, we invited 28 designers to complete conceptual design tasks under SK-condition (traditional sketching) and AI-condition (image-generation tools). We found that AI-condition significantly influenced designers' cognitive styles, with approximately 50% of designers exhibiting cognitive style shifts. Although changes in cognitive style did not have a significant impact on design performance, there was a significant association between cognitive style and design performance under AI-condition. Designers identified as Treasure Hunters performed best in novelty, variety, and fluency, while those exhibiting Focused Prober style, traditionally associated with experts, performed worse under AI-condition. These empirical findings provide valuable insights for customising tools and educational approaches that align with the needs of different cognitive styles.

Keywords: cognitive process , cognitive style , design performance , Generative artificial intelligence , product conceptual design

The rise of automated vehicles marks a major shift in the transportation landscape, yet widespread public scepticism remains a barrier to their acceptance. Among the key challenges is the issue of trust, which significantly influences users' willingness to adopt self-driving technologies. This study explores the potential of Biophilic design -a concept rooted in the human affinity for nature- to improve trust and comfort within automated vehicle interiors. By incorporating natural elements such as materials, lighting, and visual aesthetics, the vehicle environment can become more psychologically supportive. This human-centred approach may not only enhance user experience but also promote broader acceptance of autonomous vehicles.

Keywords: Automated vehicles , Comfort Driving , Human-Technology Interaction , Pleasant Design , User Experience

Background: Automated driving offers enjoyable and comfortable trips, though not everyone will find the experience pleasant. Some special populations such as elderly people, individuals with physical or cognitive impairments, and pregnant women face different difficulties in mobility services to conduct their daily activities and, consequently, also in using new technologies like autonomous vehicles. Objective: This study aims to review the specific needs, concerns, and difficulties of special populations when using autonomous vehicles and to highlight the primary issues affecting their acceptance of these technologies. Methods: A review of 48 papers was conducted to identify the special needs, concerns, and difficulties faced by various demographic groups, including elderly individuals, people with physical disabilities, and pregnant women, in using or approaching the use of autonomous vehicles. Results: The review revealed that aging individuals have received more attention by scientific community compared to other disabled populations. Acceptance of autonomous vehicles was identified as the primary issue across all surveyed groups. Safety, comfort, and dependability were also significant concerns, particularly among individuals with special needs. Conclusions: This study outlines the unique concerns of various demographic groups using autonomous vehicles, with acceptance highlighted as a common issue. Designing user-friendly interfaces tailored to diverse demographics could enhance acceptance and improve the overall travel experience for those needing constant support.

Keywords: aging , autonomous vehicles , cognitive impairment , comfort , design , disabled populations

Recent advancements in construction technology have led to the increasingly frequent design of flexible structures, which are consequently more susceptible to vibrations from various sources, potentially impacting human comfort. The vibration comfort level of occupants is influenced by several factors, including the type of vibration source, building classification, vibration transmission medium, and human perception, making its assessment a complex process. To ensure buildings comply with human comfort prescriptions, it is critical to conduct an accurate evaluation of vibrations and identification of their sources. In this context, this work introduces VibComfort, a software tool to assess the effects of low-frequency vibrations on human comfort in urban environments. VibComfort is a MATLAB-based software application that offers an intuitive platform for signal visualization and processing, event selection, statistical analysis, long-term comfort monitoring, and verification against limits provided by the UNI9614-2017 standard. The potential of the developed platform is demonstrated with a case study of an administrative building of the University of Granada in Spain, adjacent to the metro line. To facilitate the identification of the vibration events, a camera was connected to the data acquisition system, allowing the identification of those triggered by metro passages. The vibrations induced by the subway were found to be significantly minimal, confirming a satisfactory level of comfort for the occupants. The presented platform lays groundwork for developing a fully automated comfort assessment system, representing a substantial advancement in the field of human comfort evaluation.

Keywords: Comfort evaluation , Comfort monitoring , Human comfort , Sensitive buildings

This study investigates the development of workwear designed to withstand harsh conditions and support physically demanding tasks. Its central aim is to create garments that enhance workers' comfort and mobility by optimizing ergonomic and anthropometric factors. First of all, expert surveys were collected, and the importance of posture adaptability and material comfort was highlighted. To investigate realistic body–garment interactions, the 3D body scans of the upper body from 34 participants in common working poses were captured. These scans revealed the zones of high deformation, guiding the placement of elastic inserts to improve flexibility in targeted areas. The redesigned garments underwent a two-stage evaluation process. First, Clo3D virtual fittings provided qualitative insights into overall jacket fit and movement behavior. Next, stress and strain mapping offered quantitative validation, showing that fabric stress levels remained below 120 kPa, providing evidence that the added elasticity effectively reduced mechanical load and improved wearability. Expert reviewers confirmed the enhanced fit and functional performance. Overall, the study demonstrates an integrated design strategy that unites textile behavior, body dimensions and biomechanics. This approach not only improves workwear but also offers a transferable framework for developing specialized clothing across other physically intensive professions.

Keywords: anthropometry , strain map , stress map , virtual fitting , workwear optimization

Articulated total body models have proved to be a useful tool for multibody analyses focused on human body behaviour. Historically, they have found wide applications in vehicle crash analyses, while the respective use in forensic biomechanics is less common and is hampered by the use of generic, simplified geometries and mechanical properties. The article introduces one advancement in these models aimed to replicate the outer geometry more faithfully as this aspect is crucial whenever the expected scenario includes one or more impacts between the human body and the environment. This accurate replication of the outer geometry has been made possible by the development of statistical shape models of the human body which can be shaped according to a limited set of anthropometric measurements. The anthropomorphic model was the basis to create a subject-specific articulated total body android, whose behaviour was validated against experimental tests obtained on cadavers, simulating pedestrian-to-car impacts. The correspondence between numerical and experimental data was checked making use of metrics (EARTH and EEARTH) specifically developed for time histories comparison. This activity has led to the optimization of contact parameters, differentiating the behaviour of the head versus all other body segments. On the whole, a validated model for forensic applications has been set up. This model is characterised by a high geometric accuracy and can be used, for example, to analyse fall scenarios. Further effort will be dedicated to set up a methodology for subject-specific parametrization of joint properties in terms of range of movement and passive stiffness.

Keywords: Articulated Total Body (ATB) , Contact properties , Crash tests , Forensic biomechanics , Multibody analysis , Time histories

The conservation of submerged cultural heritage is increasingly prioritized, especially after the adoption of frameworks such as the 2001 UNESCO Convention on the Protection of the Underwater Cultural Heritage. The degradation of underwater archaeological sites, exacerbated by harsh marine conditions, requires effective in-situ restoration and environmental monitoring. Traditional approaches often rely on separate tools for mortar injection, water sampling, and sediment analysis, leading to logistical inefficiencies and operational challenges for divers. This study presents a novel battery-powered, multipurpose underwater tool designed to support and improve conservation work conducted by archaeologists and restorers. The device addresses multiple operational needs in a single solution, allowing underwater restorers to perform hydraulic mortar injection for structural consolidation, collect water samples for environmental monitoring, and extract sediment for archaeological analysis. The tool features a modular architecture, an ergonomic design for diver usability, a precision extrusion system for mortar delivery, and a rotating carousel with interchangeable cartridges. By combining these capabilities in one compact instrument, the device enhances the efficiency of underwater conservation and monitoring efforts. This integrated approach reduces the equipment burden, simplifies logistics, and enables faster, more flexible field interventions. Ultimately, the proposed system contributes to more sustainable and effective management of submerged cultural heritage, promoting more responsive and streamlined underwater conservation efforts.

Keywords: Product design , Underwater cultural heritage , Underwater injector device , Underwater sampling device

The concept of User Experience (UX) in design has evolved beyond usability and aesthetics to encompass broader dimensions of emotional, cognitive, and social wellbeing. This paper explores the intersection of UX and wellbeing, proposing a shift in design thinking toward experience-driven, human-centered approaches that support holistic wellbeing. Drawing on interdisciplinary research from psychology, environmental science, human–computer interaction, and architecture, the study highlights the diverse methods and tools used to assess wellbeing, including self-reports, physiological measures, and digital technologies. It also examines how design can influence wellbeing through user-centered, positive, and ethical frameworks, addressing key challenges in conceptual clarity, methodological rigor, and practical implementation. The paper concludes with a research agenda focused on integrating wellbeing into UX design through new assessment methods, cross-disciplinary collaboration, and development of culturally sensitive, sustainable design principles. By aligning UX with wellbeing, the study aims to guide future research and practice in creating more meaningful, engaging, and health-promoting user experiences.

Keywords: Design for wellbeing , Design methods , Hedonism , Pleasure , User experience

This study investigates the role of Smart Home technologies in promoting personal autonomy among elderly people, with a focus on Ambient Assisted Living (AAL) systems. Adopting a multidimensional view of autonomy that integrates physical, cognitive, emotional, and social domains, the paper synthesizes findings from systematic reviews and meta-analyses published between 2020 and 2025. The review explores five key research questions: the impact of home automation on daily functioning (ADL/IADL), methodologies for measuring autonomy, types of data collected, benefits and limitations of smart systems, and the extent to which research reflects a holistic understanding of autonomy. The primary contribution of this study lies in synthesizing current review literature and identifying patterns, consistencies, and methodological limitations. Findings indicate that while smart technologies effectively support basic daily activities and enhance safety and independence, their effectiveness depends on personalization, inclusivity, and alignment with users\' real-life contexts. However, current research often overlooks the emotional and relational dimensions of aging, and many studies reveal inconsistent assessment methods, due to a lack of standardized tools, and limited incorporation of usercentered, cultural, or experiential data.

Keywords: ADL/IADL , Ambient assisted living , Assistive technologies , Disability first section , Elderly , Home automation system , Personal autonomy , Sensor-based monitoring

This workshop explores the relationship between eXtended Reality (XR), Artifcial Intelligence, and Human Factors, focusing on the fundamental principles of Human-Computer Interaction within the Intelligent User Interfaces topic. With the increasing integration of large language models, conversational agents, and autonomous AI services into everyday interactions, XR ofers embodied environments that blend language, gesture, gaze, touch, and spatial context. The workshop examines how AI can enable real-time personalization of immersive interfaces, the design of intuitive multimodal interactions with AI agents, and methods for assessing and optimizing human factors such as cognitive load, emotional engagement, and trust. Additional topics include accessibility by design and the social dynamics of AI-enhanced multi-user XR spaces. Positioning XR as both an application domain and a gateway to AI, the workshop fosters research on intelligent, user-centric, and inclusive immersive interfaces.

Keywords: Artifcial Intelligence , Extended Reality , Human Factor , Human-Computer Interaction

The emergence of Industry 4.0 and 5.0 is reshaping the manufacturing landscape, including human-centric approaches that harmonize human intelligence with machine efficiency. In this context, evaluating cognitive workload is essential to ensure operator well-being and optimize productivity. The Human Digital Twin (HDT) framework presents a promising solution, enabling the virtual simulation of human behavior within industrial environments. When integrated with Immersive Virtual Reality (IVR) and physiological monitoring technologies, HDTs can support the ergonomic and cognitive optimization of workstation design. This paper presents the development of a custom IVR application designed to analyze how different cognitive support conditions impact user performance and psychophysiological responses during simulated industrial assembly tasks. The system incorporates multi-modal data collection, including task performance metrics, eye-tracking data, ECG, and electrodermal activity, complemented by validated subjective questionnaires. The experimental protocol is designed to compare traditional instruction-based interfaces with visually guided support systems within a highly immersive environment.

Keywords: Immersive Virtual Reality , Industrial Tasks , Physiological Parameters , Unity Application Development

In complex industrial environments, modelling human performance is fundamental to ensuring safety and efficiency, particularly in extraordinary maintenance activities. Consistent with these challenges, this study examines the predictive value of nine individual characteristics in determining the completion time of a cognitive-motor task. Twenty-two operators, divided between experts and non-experts, participated in a within-subjects laboratory experiment, performing a maintenance simulation on a vacuum-filling machine under two conditions: without time constraints and with a seven-minute time limit. The data collected, relating to time pressure and performance in carrying out the activities, were analyzed using linear regression, Lasso Regression, and Random Forest models. The analysis showed that traits such as manual dexterity and decision-making ability are significant predictors of performance, although their influence varies under time pressure. In particular, the inclusion of contextual variables improved the interpretability and accuracy of the models. These results underscore the importance of developing human-centred predictive models that integrate individual characteristics with environmental factors, thereby contributing to the design of adaptive systems in high-risk industrial contexts. The study also emphasizes the importance of incorporating contextual variables into predictive models. It offers insights for the development of adaptive workforce management and training systems in line with the principles of personalization and resilience promoted by Industry 5.0.

Keywords: Human Factors , Industry 5.0 , Operator 5.0 , Predictive modeling , Time pressure