Human factors and ergonomics

The following document outlines the design process of a haptic feedback glove. The aim of this project is to give the user a haptic feedback once they are in contact with an object in a virtual interactive 3D space. In order to induce a haptic feedback, Unity3D is used to simulate the 3D virtual space, Leap motion is used in order to track the hands of the user and map them on the 3D virtual space. Finally, the signal from Unity is sent to Arduino which is able to produce the signal to initiate the vibration motors and led lights. The purpose of this technology is to help people with reduced motor ability, to perform physiotherapy exercises while playing an interactive game that involves haptic feedback.

Keywords: Arduino , Haptic feedback , Leap motion and rehabilitation , Sharebot , Unity3D

Work-related musculoskeletal disorders are a very impactful problem, both socially and economically, in the manufacturing sector. To control their effect, standardised methods and technologies for ergonomic assessment have been developed. The main technologies used are inertial sensors and vision-based systems. The former are accurate and reliable, but invasive and not affordable for many companies. The latter use machine learning algorithms to detect human pose and assess ergonomic risks. In this paper, using data collecting by reproducing the working environment in LUBE, the major Italian kitchen manufacturer, we propose SPECTRE (Sensor-independent Parallel dEep ConvoluTional leaRning nEtwork): a fully sensor-independent learning model based on convolutional networks to classify postures in the workplace. This system assesses ergonomic risks in major body segments through Deep Learning with a minimal impact. SPECTRE's performance is evaluated using established metrics for imbalanced data (precision, recall, F1-score and area under the precision-recall curve). Overall, SPECTRE shows good performance and, thanks to an agnostic explainable machine learning method, is able to extrapolate which patterns are significant in the input.

Keywords: Computer vision , Deep learning , Ergonomic risks , Human-centered manufacturing , Posture recognition , Work-related musculoskeletal disorders

This chapter presents the ergonomic assessment of a typical shelf filling task performed by the store clerk. The proposed methodology is based on a robust design approach, which considers all the main factors that have influence on the ergonomic assessment of a typical refilling operation. The ergonomic assessment is based on two ergonomic indices, one specific for establishing the ergonomically optimal working height for lifting, and one specific for selecting the refilling process modality which minimises the clerks' effort. The research work has been performed using both virtual simulations and real laboratory experiments. The goal is to provide input to a suitably designed robotic handling unit encapsulating a standard supermarket trolley. The handling unit consists in a suitable SCARA-like arm and two actuated trays, which allow to serve the cases with the products contained in the trolley at an ergonomic height for the clerks, with the aim of reducing refilling-related musculoskeletal disorders and thus improve clerks' health and wellbeing.

Keywords: Biomechanical risk , Collaborative robotics , Ergonomic assessment , Ergonomic indices

This study introduces a new operational tool based on the AEIOU observational framework to support the design of adaptive human machine interfaces (HMIs) that aim to modify people's behavior and support people's choices, to improve safety using emotional regulation techniques, through the management of environmental characteristics (e.g., temperature and illumination), according to an approach based on the nudging concept within a design thinking process. The proposed approach focuses on research in the field of behavioral psychology that has studied the correlations between human emotions and driving behavior, pushing towards the elicitation of those emotions judged to be most suitable for safe driving. The main objective is to support the ideation of scenarios and/or design features for adaptive HMIs to implement a nudging strategy to increase driving safety. At the end, the results from a collaborative workshop, organized as a case study to collect concept ideas in the context of sports cars, will be shown and evaluated to highlight the validity of the proposed methodology, but also the limitations due to the requirement of prototypes to evaluate the actual effectiveness of the presented nudging strategies.

Keywords: adaptive HMI , affective computing , automotive , driving safety , emotion regulation , nudge

Industry nowadays must deal with the so called “fourth industrial revolution”, i.e. Industry 4.0. This revolution is based on the introduction of new paradigms in the manufacturing industry such as flexibility, efficiency, safety, digitization, big data analysis and interconnection. However, human factors' integration is usually not considered, although included as one of the paradigms. Some of these human factors' most overlooked aspects are the customization of the worker's user experience and on-board safety. Moreover, the issue of integrating state of the art technologies on legacy machines is also of utmost importance, as it can make a considerable difference on the economic and environmental aspects of their management, by extending the machine's life cycle. In response to this issue, the Retrofitting paradigm, the addition of new technologies to legacy machines, has been considered. In this paper we propose a novel modular system architecture for secure authentication and worker's log-in/log-out traceability based on face recognition and on state-of-the-art Deep Learning and Computer Vision techniques, as Convolutional Neural Networks. Starting from the proposed architecture, we developed and tested a device designed to retrofit legacy machines with such capabilities, keeping particular attention to the interface usability in the design phase, little considered in retrofitting applications along with other Human Factors, despite being one of the pillars of Industry 4.0. This research work's results showed a dramatic improvement regarding machines on-board access safety.

Keywords: Face recognition , Industrial Internet of Things , Industry 4.0 , Safety , Smart retrofitting , Usability

Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, − monitoring or–control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator–ErgoTakt–derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers' ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown.

Keywords: assembly , gesture-based control , Gesture-Based monitoring , manufacturing

The potential of collaborative robotics often does not materialize in an efficient design of the human-robot collaboration. Technology-oriented approaches are no longer enough in the Industry 4.0 era. This work proposes a set of methods to support manufacturing engineers in the human-oriented design process of integrated production systems to obtain satisfactory performance in the mass customization paradigm, without impacting the safety and health of workers. It founds the design criteria definition on five main pillars (safety, ergonomics, effectiveness, flexibility, and costs), favors the consideration of different design alternatives, and leads their selection. The dynamic impact of the design choices on the various elements of the system prevails over the static design constraints. The method has been experimented in collaboration with the major kitchen manufacturer in Italy, which introduced a collaborative robotics cell in the drawers' assembly line. It resulted in a more balanced production line (10% more), a verified risk minimization (RULA score reduced from 5 to 3 and OCRA score from 13.30 to 5.70), and a greater allocation of operators to high added value activities.

Keywords: collaborative robots , ergonomics , human-centered manufacturing , Human-robot collaboration , manufacturing systems design

Sailing navigation is an activity that requires acquiring and processing information from the surrounding environment. The advancement of technology has enabled sailboats to have an increasing number of onboard sensors that make sailing more user-friendly. However, data provided by these sensors are still visualized on 2D digital displays that imitate traditional analog interfaces. Although these displays are strategically placed on the sailboat, the user needs to divert attention from the primary navigation task to look at them, thus spending a significant amount of cognitive resources. AR-based technologies have the potential to overcome these limitations by displaying information registered in the real environment, but there are no studies in the literature for validating the effectiveness of this technology in the field of sailing. Thus, we designed a head-mounted display AR-based interface to assist users in monitoring wind data to avoid user diversion from the primary task of sailing. We conducted a user study involving 45 participants in an Immersive Virtual Reality simulated environment. We collected objective and subjective measures to compare the AR-based interface with a traditional data visualization system. The AR-based interface outperformed the traditional data visualization system regarding reaction time, cognitive load, system usability, and user experience.

Keywords: Augmented reality , Cognitive load , Human–computer interaction , Nautical , Sailing , User study

The key concept of collaborative robotics is represented by the presence of a strict interaction between a human user and the robotic system. As such, the study of the interaction is of paramount importance for a successful implementation of the system. In this article, we propose a novel approach to address the problem of designing a collaborative robotic system for industrial applications, focusing on the characteristics of the interaction. In particular, we will propose a set of methodologies focused on interaction design, inspired by those used for the design of user interfaces. These methodologies will allow the design of collaborative robotic systems following a user-centered approach, thus putting emphasis not only on safety and adaptability of the robotic systems (which have been widely addressed in the literature), but also on the interaction experience. While the proposed methodology was developed considering general collaborative robotics applications, two real industrial case studies were considered, to instantiate the considered framework and showcase its applicability to the real-world domain. Note to Practitioners-This article aims at bridging the gap between interaction design and collaborative robotics. In particular, the proposed methodology will represent a toolset for robotic experts (researchers and system integrators), for understanding the user experience and designing the robotic system ensuring an effective interaction. In fact, while robotics experts are typically well aware of issues and methodologies related to technological and application aspects, they often tend to ignore the principles of interaction. Such principles are commonly adopted in the design of computer-based human-machine interfaces or web applications, but, to the best of the authors' knowledge, have never been applied to the design of collaborative robotic systems for industrial applications. Hence, this article will serve as a fundamental step to bring interaction design principles into the robot integration domain.

Keywords: Collaborative robotics , interaction design , user interface human factors , user-centered design

Industry 4.0 is driving the revolution of manufacturing processes by combining innovative technologies and new interaction paradigms among systems and operators. In particular, the layout, tasks and work sequences of assembly lines are designed according to several transdisciplinary Design Principles (DPs), such as process efficiency, product quality, ergonomics, safety and operators' workload. A large variety of simulation software can be employed for evaluations. However, the related ability to assess multidisciplinary factors must be evaluated. The paper aims to provide a framework for guiding the assessment of simulation software in the context of Industry 4.0 assembly lines. Process requirements are first analyzed and mapped to select DPs, prioritized according to design goals by an analytical hierarchy process. Then, suitable simulation software is determined accordingly, and the virtual model is realized. Finally, the possibility of the software to provide meaningful elaborations for the selected DPs is assessed. The framework has been tested on a prototypal Industry 4.0 assembly line composed of automated logistic systems, cobots and systems to guide the execution of tasks. The line has been modeled in Siemens Process Simulate, analyzing the completeness and appropriateness of the functionalities of this software according to the defined DPs.

Keywords: Decision Support Tools , Design Principles , Industry 4.0 , Interactive Simulation for Engineering , Transdisciplinary Engineering

Nowadays, web designers are forced to have an even deeper perception of how users approach their products in terms of user experience and usability. Remote Usability Testing (RUT) is the most appropriate tool to assess the usability of web platforms by measuring the level of user attention, satisfaction, and productivity. RUT does not require the physical presence of users and evaluators, but for this very reason makes data collection more difficult. To simplify data collection and analysis and help RUT moderators collect and analyze user's data in a non-intrusive manner, this research work proposes a low-cost comprehensive framework based on Deep Learning algorithms. The proposed framework, called Miora, employs facial expression recognition, gaze recognition, and analytics algorithms to capture data about other information of interest for in-depth usability analysis, such as interactions with the analyzed software. It uses a comprehensive evaluation methodology to elicit information about usability metrics and presents the results in a series of graphs and statistics so that the moderator can intuitively analyze the different trends related to the KPI used as usability indicators. To demonstrate how the proposed framework could facilitate the collection of large amounts of data and enable moderators to conduct both remote formative and summative tests in a more efficient way than traditional lab-based usability testing, two case studies have been presented: the analysis of an online shop and of a management platform. Obtained results suggest that this framework can be employed in remote usability testing to conduct both formative and summative tests.

Keywords: affective computing , deep learning , gaze detection , remote usability testing , usability , usability assessment

Building great products or services is not easy; users want products and services that exceed their expectations and evolve with their needs; it is not just about building the right features. Knowing the user engagement (UE) towards a physical, virtual product or service can give valuable information that could be used as feedback for the design, enhancing its chances of success. In the context of user-centered design, UE is the assessment of the user experience characterized by the study of the individual's cognitive, affective, and behavioral response to some stimulus, such as a product, a service, or a website. UE considers not only the users' requirements and wishes but also their perceptions and reactions during and after an interaction with a product, system, or service. Many studies looking to quantify the UE are available. Still, a framework that provides a generic view of the most commonly used methods and metrics to measure UE does not yet exist in the literature. Aiming to understand the UE better, in this research, we developed a conceptual framework summarizing the available metrics and techniques used across different contexts, including good practices of self-report methods and physiological approaches. We expect this study will allow future researchers, developers, and designers to consider the UE as one of the most prominent product/service success indicators and use this guideline to find the more appropriate method, technique, and metric for its measurement.

Keywords: Consumer engagement , Physiological , UCD , User engagement measurement

Understanding user experience (UX) is essential to design engaging and attractive products, so nowadays has emerged an increasingly interest in user-centred design approach; in this perspective, digital technologies such as Virtual Reality (VR) and Mixed Reality (MR) could help designers and engineers to create a digital prototype through which the user feedback can be considered during the product design stage. This research aims at creating an interactive Digital Twin (DT) using MR to enable a tractor driving simulation and involve real users to carry out an early UX evaluation, with the scope to validate the design of the control dashboard through a transdisciplinary approach. MR combines virtual simulation with real physical hardware devices which the user can interact with and have control through both visual and tactile feedback. The result is a MR simulator that combines virtual contents and physical controls, capable of reproducing a plowing activity close to reality. The principles of UX design was applied to this research for a continuous and dynamic UX evaluation during the project development.

Keywords: Digital Engineering , Digital Twin , Human-centered Design , Mixed Reality , User experience design

Human factors integration is definitely a transdisciplinary and urgent matter in modern factories. Despite the great surge in factory automation in recent years, human-machine interaction is still a crucial aspect and companies need to take care of the workers' wellbeing and performance to enhance the overall system quality and productivity. Nevertheless, ergonomics is poorly considered during the design of complex industrial systems, such as automatic machinery, especially for the lack of practical methodologies and guidelines to promote human factors from the early stages of design or redesign. To overcome this issue, this work proposes a transdisciplinary approach to redesign automatic machinery in compliance with factory ergonomics, using a combination of digital technologies (e.g., digital human simulation, human physiological data monitoring). The paper defines a structure method and related tools to apply a human-centric approach to industrial cases and their validation of a real case, concerning the redesign of a packaging automatic machine. Results show how the proposed approach is useful to detect possible ergonomic issues at the shop floor, identifying in advance risky situations for the operators during operating or maintenance tasks, and leading to an optimized machine able to enhance the workers' wellbeing and factory productivity at the same time.

Keywords: digital human simulation , ergonomics , human factors integration , human monitoring , human-centered design

In the last decade, the field of Human-Robot Collaboration (HRC) has received much attention from both research institutions and industries. Robot technologies are in fact deployed in many different areas (e.g., industrial processes, people assistance) to support an effective collaboration between humans and robots. In this transdisciplinary context, User eXperience (UX) has inevitably to be considered to achieve an effective HRC, namely to allow the robots to better respond to the users' needs and thus improve the interaction quality. The present paper reviews the evaluation scales used in HRC scenarios, focusing on the application context and evaluated aspects. In particular, a systematic review was conducted based on the following questions: (RQ1) which evaluation scales are adopted within the HRI scenario with collaborative tasks?, and (RQ2) how the UX and user satisfaction are assessed?. The records analysis highlighted that the UX aspects are not sufficiently examined in the current HRC design practice, particularly in the industrial field. This is most likely due to a lack of standardized scales. To respond to this recognized need, a set of dimensions to be considered in a new UX evaluation scale were proposed.

Keywords: Human-Robot Collaboration , Review , Transdisciplinary engineering , User experience evaluation , User experience scale

The collaboration among humans and machines is one of the most relevant topics in the Industry 4.0 paradigm. Collaborative robotics owes part of the enormous impact it has had in small and medium size enterprises to its innate vocation for close cooperation between human operators and robots. The i-Labs laboratory, which is introduced in this paper, developed some case studies in this sense involving different technologies at different abstraction levels to analyse the feasibility of human-robot interaction in common, yet challenging, application scenarios. The ergonomics of the processes, safety of operators, as well as effectiveness of the cooperation are some of the aspects under investigation with the main objective of drawing to these issues the attention from industries who could benefit from them.

Keywords: human-in-the-loop , human-robot collaboration , obstacle avoidance , virtual reality

The fourth industrial revolution emphasizes the need for an approach centered on human factors as a cornerstone of the production cycle. The link between ergonomics and enabling technologies of industry 4.0 becomes increasingly stronger, incorporating both the cultural and the technical aspects. The design of the workplace must pursue its adaptability to the various elements of the work system to guarantee the psychophysical well-being of man, safety, and performance. This paper proposes a model to optimize the operator's posture naturally and dynamically. The model considers the characteristics of the operator, the product, and the task and by applying the ergonomic principles and the technological constraints it determines the optimal position of the product. It aims to minimize both the ergonomic risk and the distance with the postural attitude of the operator. The case study demonstrates the implementation of the model in a smart machine and the benefits that can be obtained in terms of the prevention of musculoskeletal disorders.

Keywords: adaptive manufacturing systems , ergonomics , human factors , human-centered manufacturing , musculoskeletal disorders , workplace design

The driving behaviour of Connected and Automated Vehicles (CAVs) may influence the final acceptance of this technology. Developing a driving style suitable for most people implies the evaluation of alternatives that must be validated. Intelligent Virtual Drivers (IVDs), whose behaviour is controlled by a program, can test different driving styles along a specific route. However, multiple combinations of IVD settings may lead to similar outcomes due to their high variability. The paper proposes a method to identify the IVD settings that can be used as a reference for a given route. The method is based on the cluster analysis of vehicular data produced by a group of IVDs with different settings driving along a virtual road scenario. Vehicular data are clustered to find IVDs representing a driving style to classify human drivers who previously drove on the same route with a driving simulator. The classification is based on the distances between the different vehicular signals calculated for the IVD and recorded for human drivers. The paper includes a case study showing the practical use of the method applied on an actual road circuit. The case study demonstrated that the proposed method allowed identifying three IVDs, among 29 simulated, which have been subsequently used as a reference to cluster 26 human driving styles. These representative IVDs, which ideally replicate the driving style of human drivers, can be used to support the development of CAVs control logic that better fits human expectations. A closing discussion about the flexibility of the method in terms of the different natures of data collection, allowed for depicting future applications and perspectives.

Keywords: automotive engineering , autonomous driving , driving behaviour , driving simulator , intelligent agents , pattern clustering

In the future, many activities will be carried out in the Metaverse: hybrid offices and video-based education are just some examples. The way research is carried out could change, too. In this context, this work investigates the possibility of simulating Augmented Reality (AR) user studies on information presentation in a virtual environment. Organizing an industrial setup is complex; thus, most studies are executed in laboratories. However, lab experiments present limitations, e.g., the number and variety of participants and the availability of facilities. User studies may also be carried out by exploiting simulated AR, as an initial step for the Metaverse, where people are connected regardless of their location. This alternative could be used to carry out experiments on AR information presentation to solve common issues, such as the lack of physical equipment to perform component location tasks and the long time required to collect a large sample of users. Indeed, researchers could propose CAD models with information that simulates the same visual realism achieved with true AR. Moreover, multiple tests could be conducted in parallel by not relying on a limited amount of physical equipment per user. In this work, we developed and evaluated a desktop-simulated testing environment (DSTE) to conduct AR information presentation experiments remotely. We applied it in a pilot user study, revealing that the proposed DSTE was effective for the related research goals. Furthermore, 40 participants reported a positive user experience. The evaluation confirms that using a DSTE is promising for collecting and analyzing data from a wide range of people.

Keywords: AR simulation , industrial metaverse , information presentation , user interaction , user study

Objectives: This study aims to evaluate the acceptability of Cinematic VR technology as a novel therapeutic approach supporting Social Skills Training (SST) rehabilitation interventions among patients with schizophrenia. Materials and Methods: We developed an innovative cinematic VR-based platform as a support system for SST rehabilitation of independent living skills and evaluated its acceptance among psychiatric patients in terms of usability, user experience, and use performance. Ten voluntary participants were enrolled in the study. The study inclusion criteria consisted of age 18-65 years, lack of moderate and severe intellectual disability, no substance use disorder, and schizophrenia spectrum disorder pathology according to DSM V. We administered post treatment questionnaires and developed the platform to capture relevant data automatically. Results: Patients rated usability and user experience from good to excellent. We also observed an improvement in the use performance. Conclusions: Cinematic Virtual Reality based applications showed good acceptability among patients with schizophrenia. This result supports further efforts in evaluating its effectiveness as a novel therapeutic approach supporting SST rehabilitation interventions.

Keywords: Cinematic virtual reality , Schizophrenia rehabilitation , Social skills training , Task performance , Usability , User experience