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.

Humans and robots will increasingly have to work together in the new industrial context. Therefore, it is necessary to improve the User Experience, Technology Acceptance, and overall wellbeing to achieve a smoother and more satisfying interaction while obtaining the maximum performance possible out of it. For this reason, it is essential to analyze these interactions to enhance User Experience. The heuristic evaluation is an easy-to-use, low-cost method that can be applied at different stages of a design process in an iterative manner. Despite these advantages, there is rarely a list of heuristics in the current literature that evaluates Human-Robot interactions both from a User Experience, Technology Acceptance, and Human-Centered approach. Such an approach should integrate key aspects like safety, trust, and perceived safety, ergonomics and workload, inclusivity, and multimodality, as well as robot characteristics and functionalities. Therefore, a new set of heuristics, namely, the HEUROBOX tool, is presented in this work in the form of the HEUROBOX tool to help practitioners and researchers in the assessment of human-robot systems in industrial environments. The HEUROBOX tool clusters design guidelines and methodologies as a logic list of heuristics for human-robot interaction and comprises four categories: Safety, Ergonomics, Functionality, and Interfaces. They include 84 heuristics in the basic evaluation, while the advanced evaluation lists a total of 228 heuristics in order to adapt the tool to the evaluation of different industrial requirements. Finally, the set of new heuristics has been validated by experts using the System Usability Scale (SUS) questionnaire and the categories has been prioritized in order of their importance in the evaluation of Human-Robot Interaction through the Analytic Hierarchy Process (AHP).

In the new industrial contexts, the workers’ well-being is the central pillar. Therefore, research on methods and technics to improve the workers’ user experience in a human–robot collaborative environment is necessary. While the effects of kinematic variables, such as speed and acceleration, on human safety have been extensively studied, their impact on human perception has not been fully explored. This study investigates the effects of the robot’s speed and acceleration on humans. An experimental research approach was used, where 20 participants (10 women and 10 men) performed an assembly task collaborating with a robot. An experiment was defined with two procedures, and the participants were evenly distributed: in the first experiment, the participants started by performing the task at a slow robot speed and then performed the same task at a faster speed. In the second experiment, the other half followed the opposite procedure. Key Performance Indicators (KPIs), physiological values (via EEG and EDA), and perceptual values (using the standardised UEQ-S questionnaire) were collected. The results showed that the robot’s speed and acceleration impact the task’s completion time and participants’ emotional responses. Our results lead to a new concept, “HRI speed bell”, which indicates that it is necessary to investigate the optimal speed and acceleration to ensure good trust and perceived safety. Furthermore, the task sequence also influences participants’ expectations and performance. Finally, the results are examined according to gender perspective.

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.