The human-centered design (HCD) approach places humans at the center of design in order to improve both products and processes, and to give users an effective, efficient and satisfying interactive experience. In industrial design and engineering, HCD is very useful in helping to achieve the novel Industry 5.0 concept, based on improving workers’ wellbeing by providing prosperity beyond jobs and growth, while respecting the production limits of the planet as recently promoted by the European Commission. In this context, the paper proposes an ergonomic assessment method based on the analysis of the workers’ workload to support the design of industrial products and processes. This allows the simultaneous analysis of the physical and cognitive workload of operators while performing their tasks during their shift. The method uses a minimum set of non-invasive wearable devices to monitor human activity and physiological parameters, in addition to questionnaires for subjective self-assessment. The method has been preliminarily tested on a real industrial case in order to demonstrate how it can help companies to support the design of optimized products and processes promoting the workers’ wellbeing.

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 introduces a low-cost and low computational marker-less motion capture system based on the acquisition of frame images through standard RGB cameras. It exploits the open-source deep learning model CMU, from the tf-pose-estimation project. Its numerical accuracy and its usefulness for ergonomic assessment are evaluated by a proper experiment, designed and performed to: (1) compare the data provided by it with those collected from a motion capture golden standard system; (2) compare the RULA scores obtained with data provided by it with those obtained with data provided by the Vicon Nexus system and those estimated through video analysis, by a team of three expert ergonomists. Tests have been conducted in standardized laboratory conditions and involved a total of six subjects. Results suggest that the proposed system can predict angles with good consistency and give evidence about the tool’s usefulness for ergonomist.

Product and process digitalization is pervading numerous areas in the industry to improve quality and reduce costs. In particular, digital models enable virtual simulations to predict product and process performances, as well as to generate digital contents to improve the general workflow. Digital models can also contain additional contents (e.g., model-based design (MBD)) to provide online and on-time information about process operations and management, as well as to support operator activities. The recent developments in augmented reality (AR) offer new specific interfaces to promote the great diffusion of digital contents into industrial processes, thanks to flexible and robust applications, as well as cost-effective devices. However, the impact of AR applications on sustainability is still poorly explored in research. In this direction, this paper proposed an innovative approach to exploit MBD and introduce AR interfaces in the industry to support human intensive processes. Indeed, in those processes, the human contribution is still crucial to guaranteeing the expected product quality (e.g., quality inspection). The paper also analyzed how this new concept can benefit sustainability and define a set of metrics to assess the positive impact on sustainability, focusing on social aspects.