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The transition to industry 4.0 has impacted factories, but it also affects the entire value chain. In this sense, human-centred factors play a core role in transitioning to sustainable manufacturing processes and consumption. The awareness of human roles in Industry 4.0 is increasing, as evidenced by active work in developing methods, exploring influencing factors, and proving the effectiveness of design oriented to humans. However, numerous studies have been brought into existence but then disconnected from other studies. As a consequence, these studies in industry and research alike are not regularly adopted, and the network of studies is seemingly broad and expands without forming a coherent structure. This study is a unique attempt to bridge the gap through the literature characteristics and lessons learnt derived from a collection of case studies regarding human-centred design (HCD) in the context of Industry 4.0. This objective is achieved by a well-rounded systematic literature review whose special unit of analysis is given to the case studies, delivering contributions in three ways: (1) providing an insight into how the literature has evolved through the cross-disciplinary lens; (2) identifying what research themes associated with design methods are emerging in the field; (3) and setting the research agenda in the context of HCD in Industry 4.0, taking into account the lessons learnt, as uncovered by the in-depth review of case studies.

In the absence of user experience evaluation tools for industrial human–machine interfaces (HMI), a specific tool called eXperience Capturer (XC) has been created. It is a multi-method user-centred tool that evaluates the pragmatic and experiential aspects of employees’ interaction with industrial HMIs during the three phases of experience. In this article, a case study is shown where the XC tool is used in an industrial HMI design process. The results show that evaluation using the XC tool facilitates the creation of a new design that improves the experience of employees during interaction, increasing their autonomy, competence, closeness to the system, safety and stimulation.

This article introduces a dataset of human-machine interactions collected in a controlled and structured manner. The aim of this dataset is to provide insights into user behavior and support the development of adaptive Human-Machine Interfaces (HMIs). The dataset was generated using a custom-built application that leverages formally defined User Interfaces (UIs). The resulting interactions underwent processing and analysis to create a suitable dataset for professionals and data analysts interested in user interface adaptations. The data processing stage involved cleaning the data, ensuring its consistency and completeness. A data profiling analysis was conducted for checking the consistency of elements in the interaction sequences. Furthermore, for the benefit of researchers, the code used for data collection, data profiling, and usage notes on creating adaptive user interfaces are made available. These resources offer valuable support to those interested in exploring and utilizing the dataset for their research and development efforts in the field of human-machine interfaces.

Industry 5.0, the latest evolution in industrial processes, builds upon the principles of Industry 4.0 by emphasizing human-centric approaches and the integration of virtual reality technologies. This paradigm shift underscores the importance of collaboration between humans and advanced technologies with a focus on optimizing efficiency, safety, and worker skill development. Based on the PRISMA 2020 guidelines, this study conducts a systematic literature review, identifying 328 papers from databases. After applying inclusion and exclusion criteria, 24 papers were selected for detailed analysis. The review provides valuable insights into the diverse evaluation methods employed in the literature, and a detailed classification of 29 human factors with their associated metrics. Despite the absence of a standardized method for assessing human factors in VR experiences, this comprehensive analysis of 240 different ways of measuring factors highlights the current state of evaluating human-centered VR experiences in Industry 5.0. While the review reveals some limitations such as potential bias in study selection and heterogeneity of methods, it also identifies significant research gaps and proposes future directions. This study contributes to the establishment of a coherent structure for future research and development in human-centered design within the rapidly evolving landscape of Industry 5.0, paving the way for more effective and standardized approaches in the future.

This article presents a dataset of service design skills which service design experts value as important requirements for design team members. Purposive sampling and a chain referral approach were used to recruit appropriate experts to conduct questionnaire-based research. Using the analytical hierarchy process (AHP), pairwise skills-rating questionnaires were designed to elicit the experts’ responses. The resulting dataset was processed using AHP algorithms programmed in R programming language. The transparent data and available codes of the research may be reused by design practitioners and researchers for replication and further analysis. This paper offers a reproduceable research process and associated dataset for conducting multiple-criteria decision analysis with expert purposive sampling.Measurement(s)perspective of experts on important service design skills for design team membersTechnology Type(s)pairwise skills-rating questionnairesFactor Type(s)service design skills

The role of humans in industry is changing due to the technological transformation of production processes, which represents a potential source of stress for workers. In this context, the NO-STRESS project aims to combine objective and subjective data collected during task execution to explore causes of work-related stress and propose solutions to mitigate them. This paper describes the within-subjects study design used in the project, where each participant was exposed to all experimental conditions, and outlines the workflow developed during the study. Moreover, it describes the behavioural, performance and perceptual indicators of participants’ stress collected during the execution of full-kitting, component quality control, assembly and product quality monitoring tasks. The data collection sessions were conducted in different manufacturing contexts. The collected data may be reused to compare stress intensity among different subjects considering individual factors and experimental conditions. In addition, the results of stress in manufacturing may be compared to the intensity of this phenomenon in other sectors. The consequent outcomes may have practical implications for work contexts and benefits for society as a whole.

Stress is a critical concern in manufacturing environments, as it impacts the well-being and performance of workers. Accurate measurement of stress is essential for effective intervention and mitigation strategies. This paper introduces a holistic and human-centered protocol to measure stress in manufacturing settings. The three-phased protocol integrates the analysis of physiological signals, performance indicators, and the human perception of stress. The protocol incorporates advanced techniques, such as electroencephalography (EEG), heart rate variability (HRV), galvanic skin response (GSR), and electromyography (EMG), to capture physiological responses associated with stress. Furthermore, the protocol considers performance indicators as an additional dimension of stress measurement. Indicators such as task execution time, errors, production rate, and other relevant performance metrics contribute to a comprehensive understanding of stress in manufacturing environments. The human perception of stress is also integrated into the protocol, recognizing the subjective experience of the individual. This component captures self-assessment and subjective reports, allowing for a more nuanced evaluation of stress levels. By adopting a holistic and human-centered approach, the proposed protocol aims to enhance our understanding of stress factors in manufacturing environments. The protocol was also applied in the automotive industry and plastic component manufacturing. The insights gained from this protocol can inform targeted interventions to improve worker well-being, productivity, and overall organizational performance.

Extended reality (XR) and collaborative robots are reshaping human–robot interaction (HRI) by introducing novel control methods that enhance user experience (UX). However, human factors such as cognitive workload, usability, trust, and task performance are often underexplored. This study evaluated UX during robotic manipulation tasks under three interaction modalities: manual control, XR-based control at real-time speed (RS), and XR-based control at reduced safety speed (SS). Twenty-one participants performed a series of tasks across three scenarios, where we measured usability, workload, flow state, trust, and agency using a subjective questionnaire adapted from SUS, NASA-TLX, FSS, SoAS, and Trust in Industrial Human–Robot Collaboration Questionnaire, and objective task metrics (completion time, errors, and attempts). Our results reveal that RS-based control modes significantly reduced physical workload and improved usability compared to manual control. RS control at real-time speed enhanced task efficiency but increased error rates during complex tasks, while SS mode mitigated errors at the cost of prolonged completion times. Trust and agency remained stable across all modalities, indicating extended reality technologies do not undermine user confidence. These findings contribute to the field of human–robot collaboration by offering insights regarding efficiency, accuracy, and UX. The results are particularly relevant for industries seeking to optimize safety, productivity, and human-centric robotic systems.

Industry 4.0 has ushered in a new era of process automation, thus redefining the role of people and altering existing workplaces into unknown formats. The number of robots in the manufacturing industry has been steadily increasing for several decades and in recent years the number and variety of industries using robots have also increased. For robots to become allies in the day-to-day lives of operators, they need to provide positive and fit-for-purpose experiences through smooth and satisfying interactions. In this sense, user experience (UX) serves as the greatest link between persons and robots. Essential to the study of UX is its evaluation. Therefore, the aim of this study is to identify methodologies that evaluate the human–robot interaction (HRI) from a human-centred approach. A systematic literature review has been carried out, in which 24 articles have been identified. Among these, are 15 experimental studies, in addition to theoretical frameworks and tools. The review has provided insight into how evaluations are conducted in HRI. The results show the most evaluated factors and how they are measured considering different types of measurements: qualitative and quantitative, objective and subjective. Research gaps and future directions are correspondingly identified.

Smart Product–Service Systems (S-PSS) represent an innovative business model that integrates intelligent products with advanced digital capabilities and corresponding e-services. The user experience (UX) within an S-PSS is heavily influenced by the customization of services and customer empowerment. However, conventional UX analysis primarily focuses on the design stage and may not adequately respond to the evolving user needs during the usage stage and how to exploit the data surrounding the use of S-PSS. To overcome these limitations, this article introduces a practical framework for developing Adaptive User Interfaces within S-PSS. This framework integrates ontologies and Context-aware recommendation systems, with user interactions serving as the primary data source, facilitating the development of adaptive user interfaces. One of the main contributions of this work lies on the integration of various components to achieve the creation of Adaptive User Interfaces for digital services. A case study of a smart device app is presented, to demonstrate the practical implementation of the framework, with a hands-on development approach, considering technological aspects and utilizing appropriate tools. The results of the evaluation of the recommendation engine show that using a context-aware approach improves the precision of recommendations. Furthermore, pragmatic aspects of UX, such as usefulness and system efficiency, are evaluated with participants with an overall positive impact on the use of the smart device.