Human factors and ergonomics

The diversification of work as well as the challenges of modern industrial tasks make manual ergonomic assessment tools (i.e., checklist, excel-based worksheet) time consuming and strongly related to the ergonomist's experience. Recent advancements in wearable sensors technology offer new perspectives in terms of integrating human-monitoring solutions with traditional ergonomics methods by movements' digitization. Furthermore, digital posture assessment plays a critical role in the context of Industry 5.0, promoting worker well-being and productivity by identifying ergonomic risks and optimizing work environments. Also, leveraging advanced technologies for posture assessment enables proactive intervention strategies to mitigate musculoskeletal disorders and enhance overall workplace safety and efficiency. The present study proposes an innovative hardware and software solution which allows even non-expert designers or ergonomists to carry out a reliable postural ergonomic assessment according to well-known ergonomic methods, speeding up the analysis and providing accurate information. The setup consists of a wearable suit and its proprietary software tool specifically programmed to carry out the ergonomic assessment according to the Time-Based Assessment COmputerized Strategy (TACOs) method. The setup has been preliminarily tested in a controlled environment simulating a real industrial scenario and a comparison with standard ergonomic practices has been performed. The Mann-Whitney U test returned a p-value of [2.198e-11] < α [0.05] demonstrating how the solution proposes results which are numerically and qualitatively enhanced while showing the practical utility of the suggested technical setup and the validity of the suggested digital technique in retrieving and recognizing the workers' posture.

Keywords: Digital Postural Assessment , Ergonomics , Motion Capture , TACOs

Nowadays, the increasing integration of artificial intelligence (AI) technologies in manufacturing processes is raising the need of users to understand and interpret the decision-making processes of complex AI systems. Traditional black-box AI models often lack transparency, making it challenging for users to comprehend the reasoning behind their outputs. In contrast, Explainable Artificial Intelligence (XAI) techniques provide interpretability by revealing the internal mechanisms of AI models, making them more trustworthy and facilitating human-AI collaboration. In order to promote XAI models' dissemination, this paper proposes a matrix-based methodology to design XAI-driven user interfaces in manufacturing contexts. It helps in mapping the users' needs and identifying the “explainability visualization types” that best fits the end users' requirements for the specific context of use. The proposed methodology was applied in the XMANAI European Project (https://ai4manufacturing.eu), aimed at creating a novel AI platform to support XAI-supported decision making in manufacturing plants. Results showed that the proposed methodology is able to guide companies in the correct implementation of XAI models, realizing the full potential of AI while ensuring human oversight and control. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.

Keywords: artificial intelligence (AI) , explainable AI (XAI) , Human-Machine Interaction , manufacturing , user interface

Background: Heart failure (HF) is responsible for a high number of hospitalizations, caused by a progressive worsening quality of life. Telemedicine allows for better management of patients' complex conditions, improving the care released. However, the risk of remaining at a testing stage often limits the integration of remote care in daily pathways for HF patients. The aim of this study is to outline the steps needed to integrate telemedicine activities into ordinary HF clinic practices. This methodology is applied to observe activities and trend improvements over a 12-month routine phase. Method: Three steps have been defined for an efficient introduction of remote care services in ordinary activities, integrating them with traditional in-person care: (i) introduction of temporary telemedicine projects, (ii) systematization of telemedicine pathways, and (iii) evaluation of monitoring phase. Observational data have been collected from structured interviews to show the rate of telemedicine activities achieved in clinical practice over the last year. Results: The methodology has been proposed in the HF clinic of the Italian hospital ASST Bergamo Est. After an initial testing phase, in which usability and user experience have been tested, four different remote activities were added: (i) telemonitoring for patients with an implantable device, (ii) follow-up televisits, (iii) nursing telephone support, and (iv) high-intensity telesurveillance pathways for patients after an HF acute event. During the last year, 218 telemonitoring pathways, 75 televisits, 500 telephone calls, and nine telesurveillance pathways have been performed. Success rates were high, and patients gave positive feedback. Conclusion: By integrating multiple telemedicine activities, it has been possible to better manage complex patients, keep track of disease progression, and improve their participation in care.

Keywords: daily practice , heart failure patients , remote care organization , telemedicine , telemonitoring , televisit , user experience

Industries are increasingly integrating innovative technologies to enhance productivity, ensure high-quality products, and reduce costs and time. Augmented reality (AR) plays a key role in this transition, especially in supporting assembly procedures, which are major contributors to production costs and time constraints. Despite advancements, existing practices often lack effective, user-centric applications that fully address usability, user acceptance, and operator experience, particularly in complex industrial environments. This highlights the urgent need for adaptable assembly guidance solutions that offer intuitive, real-time support throughout the assembly process, from sequence definition to deployment of AR systems. This paper introduces an innovative AR assembly guidance tool (ARAGT) designed for industrial assembly in smart factories. ARAGT integrates virtual information into the physical workspace, providing real-time 3D models and assembly instructions to guide operators. Developed through a User-Centered Design (UCD) approach and refined based on user feedback, ARAGT includes functionalities such as assembly sequence definition, customization of graphical elements for clarity, and virtual annotations for issue reporting. Implemented using Unity® and the AR Foundation library, it is compatible with mobile devices, offering flexibility in diverse industrial settings. The paper presents results from a user evaluation conducted in a real facility, assessing usability and acceptance through SUS and TAM standard questionnaires. The findings suggest that ARAGT effectively addresses the challenges of increasing complexity and efficiency demands in manufacturing assembly, providing a user-centric solution aligned with the Industry 5.0 paradigm.

Keywords: Assembly guidance , Augmented Reality , Industry 5.0 , Usability , User acceptance , User-centered design

Industry 5.0 marks a further shift towards human-centered industrial processes, driven by increased automation and innovative technologies. Understanding operator risks in this context is crucial for ensuring worker safety and well-being. This paper introduces an innovative framework for the comprehensive and objective analysis of industrial operators' physical ergonomics. Conducted within a company that produces refrigeration systems, the study focuses on tasks such as hooks insertion and brazing on heat exchangers. Combining subjective and objective methods, the analysis includes self-assessment questionnaires, the User Experience Questionnaire (UEQ), the Body Part Discomfort Scale (BPDS), and the Borg CR10 questionnaire. Utilizing the Xsens MVN Awinda motion capture system, the study captures operators' movements and employs algorithmic analysis to assess ergonomics objectively. RULA analysis primarily concentrates on joint angles and hazardous postures, while the inclusion of Strain Index and NIOSH analyses provides further insight into the risks associated with hand and wrist movements and lifting tasks. Preliminary findings indicate significant time reduction in high-risk postures with semi-automated processes. Moreover, there's a noticeable decrease in medium-risk activity time for brazing. The study recommends guidelines for workstation redesign based on physical ergonomic analyses and human-centered design principles. Future work will involve cognitive ergonomics analysis and will include human factors in product and process redesign efforts.

Keywords: Ergonomics , Human Factors , Human-centred Design , Industry 5.0 , Manufacturing Processes

In the original publication [1], there was a mistake in Table 9 as published. Table 9 appeared the same as Table 7. The corrected Table 9 appears below. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

The integration of automated vehicle (AV) technology in public transportation systems offers promising opportunities to improve the flexibility and safety of the traffic environment. However, user acceptance remains a critical challenge in the field of human-machine interaction for the effective deployment of shared autonomous vehicles (SAVs). This study presents a design framework aimed at enhancing user acceptance through human-machine interface (HMI) design tailored to SAVs. The framework is developed in adherence to relevant interaction design principles, following a systematic approach encompassing three key steps: analysis, synthesis, and evaluation. It integrates user acceptance factors into the design process, providing a structured method for designers. The framework was iteratively refined through interviews with three international domain experts; a focus group discussion with 10 researchers and professionals specializing in automotive interaction designers; and a workshop with 30 students and designers. The results demonstrate the framework's ability to guide the development of user-acceptable HMI solutions. The paper concludes by emphasizing the need for further exploration into how user acceptance factors evolve over time and how real-world testing can validate the framework's effectiveness in promoting user acceptance and satisfaction.

Keywords: design framework , human-machine interfaces , shared autonomous vehicles , user acceptance

Functional evaluation plays an important role in determining suitable training programs for elite athletes. For athletes with Intellectual Impairment, it may represent an even more powerful tool as it allows to obtain quantitative feedbacks (when qualitative ones are hard to collect). This study presents practical examples in the context of strength assessment tests carried out in the functional evaluation project of the Italian Sport Federation for athletes with Intellectual Impairment (FISDIR). In particular, a useful customized tool for the assessment of explosive strength in sprinters, based on biomechanical indices, is presented. The proposed approach starts with the definition of key biomechanical parameters in terms of spatiotemporal, kinematic, kinetics, energetic and joint angles related to each main phase of the test. According to the literature and technical recommendations, reference target values for each parameter are defined and normalized. In addition, a quick graphical visualization was designed. Finally, two synthetic indices related to the performance and execution are developed. Practical implications in squat jump test are presented. The proposed methodology represents a useful customized tool for improving training in athletes with Intellectual Impairment.

Keywords: biomechanics , Functional evaluation , intellectual impairment , strength assessment

TOTTA outlines the spatial position and rotation guidance of a real/virtual tool (TO) towards a real/virtual target (TA), which is a key task in Mixed reality applications. The task error can have critical consequences regarding safety, performance, and quality, such as surgical implantology or industrial maintenance scenarios. The TOTTA problem lacks a dedicated study and it is scattered in different domains with isolated designs. This work contributes to a systematic review of the TOTTA visual widgets, studying 70 unique designs from 24 papers. TOTTA is commonly guided by the visual overlap -an intuitive, pre-attentive "collimation"feedback- of simple shaped widgets: Box, 3D Axes, 3D Model, 2D Crosshair, Globe, Tetrahedron, Line, Plane. Our research discovers that TO and TA are often represented with the same shape. They are distinguished by topological elements (e.g. edges/vertices/faces), colors, transparency levels, and added. shapes, widget quantity, and size. Meanwhile some designs provide continuous "during manipulation feedback"relative to the distance between TO and TA by text, dynamic color, sonification, and amplified graphical visualization. Some approaches trigger discrete "TA reached feedback"such as color alteration, added sound, TA shape change, and added text. We found the lack of golden standards, including in testing procedures, as current ones are limited to partial sets with different and incomparable setups (different target configurations, avatar, background, etc.). We also found a bias in participants: right-handed, young male, non-color impaired.

Keywords: 3D positioning , 3D user interface , tool to target manipulation , Virtual environments , widgets

—Mixed Reality (MR) is proven in the literature to support precise spatial dental drill positioning by superimposing 3D widgets. Despite this, the related knowledge about widget's visual design and interactive user feedback is still limited. Therefore, this study is contributed to by co-designed MR drill tool positioning widgets with two expert dentists and three MR experts. The results of co-design are two static widgets (SWs): a simple entry point, a target axis, and two dynamic widgets (DWs), variants of dynamic error visualization with and without a target axis (DWTA and DWEP). We evaluated the co-designed widgets in a virtual reality simulation supported by a realistic setup with a tracked phantom patient, a virtual magnifying loupe, and a dentist's foot pedal. The user study involved 35 dentists with various backgrounds and years of experience. The findings demonstrated significant results; DWs outperform SWs in positional and rotational precision, especially with younger generations and subjects with gaming experiences. The user preference remains for DWs (19) instead of SWs (16). However, findings indicated that the precision positively correlates with the time trade-off. The post-experience questionnaire (NASA-TLX) showed that DWs increase mental and physical demand, effort, and frustration more than SWs. Comparisons between DWEP and DWTA show that the DW's complexity level influences time, physical and mental demands. The DWs are extensible to diverse medical and industrial scenarios that demand precision.

Keywords: co-design , dentistry , Dynamic widgets , mixed reality , precise tool positioning , usability testing

This work investigates the application of Augmented Reality (AR) in assisting industrial companies in facilitating the adoption of vertical-axis wind turbines for domestic use. Despite its growth, high upfront costs limit broader wind turbine adoption, especially in urban areas. AR is an emerging technology that can enhance the spread of renewable energy. In the wind energy sector, it is frequently integrated with data monitoring to support the decision-making process. In the literature, many studies address this topic, focusing primarily on improving performance in industry rather than evaluating application usability and user experience. Therefore, our study intends to explore an AR application's usability and user experience integrated with data monitoring to create customer service in the wind energy sector. We developed an application targeted to potential customers planning to adopt wind power to produce electricity within their homes. The aim is to encourage the spread of this renewable energy in an urban setting by facilitating the choice of the most suitable wind turbine based on the local weather conditions and the needs of the individual. To achieve this goal, specifically, we paid significant attention to data visualization, including the estimated power output of the chosen turbine, to better engage the user through customizable graphs rather than traditional textual information. We evaluated the usability and user experience of the implemented application through a user study involving 10 potential buyers of a wind turbine to be installed in their property. The results reveal that the usability is 'excellent' with a positive user experience evaluation, especially in attractiveness and novelty. Thus, our proposal turns out to be promising in creating customer service in the wind energy sector and other branches of renewable energy, such as solar-powered systems.

Keywords: augmented reality , data monitoring , data visualization , user acceptance , wind turbine

This study contributes to understanding semi-automated ergonomic risk assessments in industrial manufacturing environments, proposing a practical tool for enhancing worker safety and operational efficiency. In the Industry 5.0 era, the human-centric approach in manufacturing is crucial, especially considering the aging workforce and the dynamic nature of the entire modern industrial sector, today integrating digital technology, automation, and sustainable practices to enhance productivity and environmental responsibility. This approach aims to adapt work conditions to individual capabilities, addressing the high incidence of work-related musculoskeletal disorders (MSDs). The traditional, subjective methods of ergonomic assessment are inadequate for dynamic settings, highlighting the need for affordable, automatic tools for continuous monitoring of workers' postures to evaluate ergonomic risks effectively during tasks. To enable this perspective, 2D RGB Motion Capture (MoCap) systems based on computer vision currently seem the technologies of choice, given their low intrusiveness, cost, and implementation effort. However, the reliability and applicability of these systems in the dynamic and varied manufacturing environment remain uncertain. This research benchmarks various literature proposed MoCap tools and examines the viability of MoCap systems for ergonomic risk assessments in Industry 5.0 by exploiting one of the benchmarked semi-automated, low-cost and non-intrusive 2D RGB MoCap system, capable of continuously monitoring and analysing workers' postures. By conducting experiments across varied manufacturing environments, this research evaluates the system's effectiveness in assessing ergonomic risks and its adaptability to different production lines. Results reveal that the accuracy of risk assessments varies by specific environmental conditions and workstation setups. Although these systems are not yet optimized for expert-level risk certification, they offer significant potential for enhancing workplace safety and efficiency by providing continuous posture monitoring. Future improvements could explore advanced computational techniques like machine learning to refine ergonomic assessments further.

Keywords: Computer vision , Deep learning , Ergonomic monitoring , Ergonomic Risk Assessment , Motion capture , Work-related MSDs

This chapter explores the challenging topic of emotion recognition by affective computing. The importance of considering and understanding people's emotions in interaction design is discussed, focusing on the role of human emotions in the entire life cycle of human–system interaction as a means to innovate products and services. The measurement of emotions is also analyzed, including the classification of human emotions and recognition methods, as well as current techniques for measuring emotional responses. An emotional-based approach and related technologies are considered in managing the entire life cycle of human–system interaction as an innovation driver. This chapter also presents how to use affective computing in cross-transversal applications, concentrating on potential applications and different case studies. This chapter concludes with a look towards a world of emotional intelligence, where affective computing plays a crucial role in collecting and analyzing emotional data to support innovative product and service experiences.

An important aspect of Advanced Driver-Assistance Systems is the real-time monitoring of the driver and the interaction with him/her. In this scenario, the proposed work is focused on the development of an innovative system capable of analyzing the driver's state and interact with him/her in a innovative way. In particular, the driver monitoring is obtained through the implementation of a multimodal approach that exploits deep learning and data fusion techniques while the interaction is achieved through sound signals elaborated with digital signal processing algorithm for the creation of an immersive scenario.

Keywords: driver monitoring , vehicle interaction

Human-robot interaction (HRI) is one of the most promising technologies in academic, social, and industrial contexts. Many industries consider HRI as an opportunity to combine the precision, repeatability, and speed of robotic automation with the human's flexibility, ability, and inclination to problem-solving. Nevertheless, human-centred factors and their impact on performance are still underestimated in this research area resulting in an important research and application gap. This paper addresses this gap by developing a conceptual framework that identifies and combines several key elements from the human-centred and system-centred design paradigms. The aim is to support the design and implementation of human-centred working environments that consider human roles and well-being while maintaining high production performances. This can improve the digital transition to Industry 5.0 enabling the combination of two visions and avoiding the risk of implementing HRI without attention to human factors. This study will contribute to assist practitioners and researchers in identifying the relevant elements to evaluate performance in HRI systems.

Keywords: collaborative robots , human factors , human robot interaction , human-centred design , industry 5.0 , performance , robot attributes

Work-related musculoskeletal disorders (WMSDs) pose a significant occupational health risk, particularly in industries requiring manual material handling tasks. Despite advancements in automation, manual labor persists, leading to strains on workers' lumbar region and increased susceptibility to WMSDs, particularly low back pain. This paper explores the potential of passive exoskeletons as preventive measures against WMSDs associated with load manipulation. Exoskeletons offer ergonomic support, augmenting human capabilities without hindering mobility. The study focuses on evaluating a passive back-support exoskeleton, PAEXO BACK, through a preliminary experimentation in a real industrial setting, specifically a warehouse. The research assesses various aspects, including muscular activity, posture, metabolic cost and physiological parameters, and perceived fatigue during lifting tasks. The case study involved workers in a warehouse lifting heavy boxes weighing up to 25 kg, both with and without the exoskeleton. Data collection utilized electromyographic signals, inertial motion capture, and physiological parameters monitoring. The results reveal a notable decrease in muscle activation, suggesting potential benefits in reducing the risk of musculoskeletal injuries among workers. Additionally, motion analysis indicates improved posture with exoskeleton usage. Despite some unexpected findings in metabolic cost, perceived exertion scores reflect reduced fatigue and enhanced operator confidence. This study underscores the promising role of exoskeletons in promoting worker health, safety, and productivity while acknowledging the need for further research to address limitations and uncertainties, paving the way for comprehensive integration into industrial settings.

Keywords: manufacturing , musculoskeletal disorders , on-field experimentation , passive exoskeletons

In complex engineering design environments, the demand for high quality products and high functionality are critical, the end-user requirements have to be integrated into the design process since the earliest design stages. In such contexts, strict design requirements related to quality, safety, and usability must be taken into account concurrently. To this aim, in this work, an integrated user-centered decision-based design method is proposed in the medical design environment context. This work is aimed at proposing a design method to be applied to complex design environments. The proposed case study is the design of an intercom device, aimed at improving patient-doctor communication in the case of bedridden patients on with helmet for Continuous Positive Airway Pressure (CPAP) therapy during SARS- CoV2 pandemic emergency. Patients undergoing helmet-assisted ventilation are often immersed in a highly noisy environment, unable to fully communicate their needs to the doctors. A three-steps decision based integrated product design and process simulation are used in the early design stages, to gain optimal product designs in a user-centered design viewpoint, for novel industrial products. The first step, modular design, is aimed at defining relations between the components of the assembly in a user centered approach. Design alternatives are generated in a Design for Manufacturing and Assembly DFMA viewpoint. In the second step, the group decision making step, in a multiple criteria context, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is applied together with the fuzzy set theory, to overcome uncertainties in the verbal judgments of the decision makers. The third step is related to the simulation of the assembly and manufacturing process, with the aim of defining a final optimal design. The proposed group multicriteria decision making based design method proved to be efficient in complex user centered engineering design contexts.

Keywords: Fuzzy-TOPSIS , group multi-criteria decision making , Integrated Design Method , User-Centered Design

The recent trend in the textile setting highlights the need for more effective and affordable processes to make the garment market more sustainable. Secondhand garment reuse and fabric recycling are two virtuous pathways toward material saving and sustainable transition. Sorting processes are fundamental enablers of this transition. The main operations involved are identification, classification, and quality assessment. This work proposes a collaborative robotic solution for the first identification and classification of garments to support human experts, along with the following assessment operations for garment and fabric quality and the identification of textile fibers. The proposed approach is based on a collaborative system integrating robots, machine vision models, and dedicated gripper systems to perform the picking actions required to identify and classify garment categories. The first experiments demonstrate the feasibility of the proposed approach as well as the self-learning capability of the system using the detection model continuous training.

Keywords: Garment detection models , Garment sorting , Gripper for garment picking , Human-robot collaborative system

CAD-to-XR is the workflow to generate interactive Photorealistic Virtual Prototypes (iPVPs) for Extended Reality (XR) apps from Computer-Aided Design (CAD) models. This process entails modelling, texturing, and XR programming. In the literature, no automatic CAD-to-XR frameworks simultaneously manage CAD simplification and texturing. There are no examples of their adoption for User-Centered Design (UCD). Moreover, such CAD-to-XR workflows do not seize the potentialities of generative algorithms to produce synthetic images (textures). The paper presents a framework for implementing the CAD-to-XR workflow. The solution consists of a module for texture generation based on Generative Adversarial Networks (GANs). The generated texture is then managed by another module (based on Shrinkwrap modelling) to develop the iPVP by simplifying the 3D model and UV mapping the generated texture. The geometric and material data is integrated into a graphic engine, which allows for programming an interactive experience with the iPVP in XR. The CAD-to-XR framework was validated on two components (rifle stock and forend) of a sporting rifle. The solution can automate the texturing process of different product versions in shorter times (compared to a manual procedure). After each product revision, it avoids tedious and manual activities required to generate a new iPVP. The image quality metrics highlight that images are generated in a “realistic” manner (the perceived quality of generated textures is highly comparable to real images). The quality of the iPVPs, generated through the proposed framework and visualised by users through a mixed reality head-mounted display, is equivalent to traditionally designed prototypes.

Keywords: Artificial Intelligence , CAD-to-XR , Extended Reality , Generative Adversarial Networks , User-Centered Design , Virtual Prototyping

This study investigates the use of electroencephalography (EEG) to characterize emotions and provides insights into the consistency between self-reported and machine learning outcomes. Thirty participants engaged in five virtual reality environments designed to elicit specific emotions, while their brain activity was recorded. The participants self-assessed their ground truth emotional state in terms of Arousal and Valence through a Self-Assessment Manikin. Gradient Boosted Decision Tree was adopted as a classification algorithm to test the EEG feasibility in the characterization of emotional states. Distinctive patterns of neural activation corresponding to different levels of Valence and Arousal emerged, and a noteworthy correspondence between the outcomes of the self-assessments and the classifier suggested that EEG-based affective indicators can be successfully applied in emotional characterization, shedding light on the possibility of using them as ground truth measurements. These findings provide compelling evidence for the validity of EEG as a tool for emotion characterization and its contribution to a better understanding of emotional activation.

Keywords: Affective elicitation , EEG , Emotion assessment , VR