The fourth industrial revolution is promoting the Operator 4.0 paradigm, originating from a renovated attention towards human factors, growingly involved in the design of modern, human-centered processes. New technologies, such as augmented reality or collaborative robotics are thus increasingly studied and progressively applied to solve the modern operators’ needs. Human-centered design approaches can help to identify user’s needs and functional requirements, solving usability issues, or reducing cognitive or physical stress. The paper reviews the recent literature on augmented reality-supported collaborative robotics from a human-centered perspective. To this end, the study analyzed 21 papers selected after a quality assessment procedure and remarks the poor adoption of user-centered approaches and methodologies to drive the development of human-centered augmented reality applications to promote an efficient collaboration between humans and robots. To remedy this deficiency, the paper ultimately proposes a structured framework driven by User eXperience approaches to design augmented reality interfaces by encompassing previous research works. Future developments are discussed, stimulating fruitful reflections and a decisive standardization process.

This paper presents an integrated approach for the design of human-robot collaborative workstations in industrial shop floors. In particular, the paper presents how to use virtual reality (VR) technologies to support designers in the creation of interactive workstation prototypes and in early validation of design outcomes. VR allows designers to consider and evaluate in advance the overall user experience, adopting a user-centered perspective. The proposed approach relies on two levels: the first allows designers to have an automatic generation and organization of the workstation physical layout in VR, starting from a conceptual description of its functionalities and required tools; the second aims at supporting designers during the design of Human-Machine Interfaces (HMIs) by interaction mapping, HMI prototyping and testing in VR. The proposed approach has been applied on two realistic industrial case studies related to the design of an intensive warehouse and a collaborative assembly workstation for automotive industry, respectively. The two case studies demonstrate how the approach is suited for early prototyping of complex environments and human-machine interactions by taking into account the user experience from the early phases of design.

Chatbot-based tools are becoming pervasive in multiple domains from commercial websites to rehabilitation applications. Only recently, an eleven-item satisfaction inventory was developed (the ChatBot Usability Scale, BUS-11) to help designers in the assessment process of their systems. The BUS-11 has been validated in multiple contexts and languages, i.e., English, German, Dutch, and Spanish. This scale forms a solid platform enabling designers to rapidly assess chatbots both during and after the design process. The present work aims to adapt and validate the BUS-11 inventory in Italian. A total of 1360 questionnaires were collected which related to a total of 10 Italian chatbot-based systems using the BUS-11 inventory and also using the lite version of the Usability Metrics for User eXperience for convergent validity purposes. The Italian version of the BUS-11 was adapted in terms of the wording of one item, and a Multi-Group Confirmatory Factorial Analysis was performed to establish the factorial structure of the scale and compare the effects of the wording adaptation. Results indicate that the adapted Italian version of the scale matches the expected factorial structure of the original scale. The Italian BUS-11 is highly reliable (Cronbach alpha: 0.921), and it correlates to other measures of satisfaction (e.g., UMUX-Lite, τb = 0.67; p < .001) by also offering specific insights regarding the chatbots’ characteristics. The Italian BUS-11 can be confidently used by chatbot designers to assess the satisfaction of their users during formative or summative tests.

In the era of Industry 4.0, the importance of human-robot collaboration (HRC) in the advancement of modern manufacturing and automation is paramount. Understanding the intricate physiological responses of the operator when they interact with a cobot is essential, especially during programming tasks. To this aim, wearable sensors have become vital for real-time monitoring of worker well-being, stress, and cognitive load. This article presents an innovative dataset (SenseCobot) of physiological signals recorded during several collaborative robotics programming tasks. This dataset includes various measures like ElectroCardioGram (ECG), Galvanic Skin Response (GSR), ElectroDermal Activity (EDA), body temperature, accelerometer, ElectroEncephaloGram (EEG), Blood Volume Pulse (BVP), emotions and subjective responses from NASA-TLX questionnaires for a total of 21 participants. By sharing dataset details, collection methods, and task designs, this article aims to drive research in HRC advancing understanding of the User eXperience (UX) and fostering efficient, intuitive robotic systems. This could promote safer and more productive HRC amid technological shifts and help decipher intricate physiological signals in different scenarios.