Human factors and ergonomics

Dealing with the design of personalized medical devices, mass production is not an option that can be hypothesized. Indeed, a cumbersome production process must be considered in such cases, mainly to account for a delicate design phase that needs to take into consideration, as input, an anatomy that vary each time. This article discusses the development of a statistical tool able to support the design of patient-specific devices. By expanding the classical formulation of the Statistical Shape Model (SSM) with the introduction of multiple levels of information within the same model, the authors have experimented with the concept of an “enhanced SSM”. While the traditional SSM only provides information on the variations that a class of shapes can manifest, the eSSM may include more levels of information. The article discusses two possible mathematical formulations of such statistical tool. Its application to the design of custom-made pelvic implants is discussed. Such application scenario is described starting from the generation of the eSSM for the pelvis. The features of interest considered in this paper are the centers of the acetabular regions of the pelvis, the segmentation of the anatomy in a series of semantical regions that must be considered when developing a load-bearing implant. Finally, the conclusions of this research are drawn and discussed together with possible future development of eSSMs.

Keywords: Biomedical engineering , Custom implant design , Human modelling , Pelvis , SSM , Statistical shape analysis

The development and spread of human-centered products that are increasingly simple and affordable has seen their application areas increase over the years, as well as their effectiveness and reliability. The study of a model based on the interpretation of biosignals is able to provide real-time monitoring of physiological conditions and to offer useful support in both clinical and driving safety fields. Heart rate variability (HRV) analysis is useful in assessing the dynamics of the autonomic nervous system (ANS) and detecting the effects of numerous systemic diseases as well as changes related to the normal daily biological rhythm or to accidental situations of stress or fatigue, as well as comfort or vigilance. The conventional system for the acquisition of the electrocardiographic (ECG) signal uses a methodology that provides for the contact of electrodes with the human skin. This research proposes a detection platform without direct contact with the skin capable of acquiring cardiac signals in real time: through a digital signal processing algorithm able of filtering noise and identifying peaks, heart rate (HR), the interval between beats (RRI) and the characteristic indices of HRV in the time and frequency domain are determined. The parameters were compared with those of a conventional ECG using the Pearson correlation coefficient which produced an index ranging from 0.30 to 0.78 for the tachogram, managing to provide, in the cases less affected by noise, a correspondence in the results of the spectral analysis useful for the evaluation of sympatho-vagal balance.

Keywords: HRV , Human-centered-design , Non-invasive , RRI

In this work a new approach for the creation of Articulated Total Body (ATB) models for person-specific multi-body simulations is presented, with the main aim of overcoming limitations related to classical multi-ellipsoids ATB models, based on regression equations having only the weight and the height of the subject as input. The new methodology is based on a Statistical Shape Model (SSM), morphable according to up to 24 input parameters: the SSM was obtained from Principal Component Analysis (PCA), applied on a wide database of 3D human scans (CAESAR). The so obtained geometry can be segmented automatically to generate body segments with the respective inertial properties (mass, principal moments of inertia, and centres of mass location). The routine has been tested on a random set of 20 male subjects and the classical multi-ellipsoids models were compared to these in terms of inertial properties and 3D external geometry: the highest differences were registered at the abdomen and the thighs for what concerns the mass (60%), principal moments (75%) and centres of mass (50 mm) properties; the trunk, the shoulder and the calves are the most critical areas for the external geometry (average distance between the anthropomorphic and ellipsoids models equal to 50 mm). A contribution has been made to build person-specific multibody models. This is a valuable method since approximations made by multi-ellipsoidal models have resulted to be relevant at specific body areas, and personalised models can be a support to design and to forensic analyses.

Keywords: 3D parametric human model , Articulated total body , Forensic biomechanics , Multibody analysis , Principal component analysis (PCA)

Successful aircraft cabin design depends on how the different stakeholders are involved since the first phases of product development. To predict passenger satisfaction prior to the manufacturing phase, human response was investigated in a Virtual Reality (VR) environment simulating a cabin aircraft. Subjective assessments of virtual designs have been collected via questionnaires, while the underlying neural mechanisms have been captured through electroencephalographic (EEG) data. In particular, we focused on the modulation of EEG alpha rhythm as a valuable marker of the brain's internal state and investigated which changes in alpha power and connectivity can be related to a different visual comfort perception by comparing groups with higher and lower comfort rates. Results show that alpha-band power decreased in occipital regions during subjects' immersion in the virtual cabin compared with the relaxation state, reflecting attention to the environment. Moreover, alpha-band power was modulated by comfort perception: lower comfort was associated with a lower alpha power compared to higher comfort. Further, alpha-band Granger connectivity shows top-down mechanisms in higher comfort participants, modulating attention and restoring partial relaxation. Present results contribute to understanding the role of alpha rhythm in visual comfort perception and demonstrate that VR and EEG represent promising tools to quantify human–environment interactions.

In the last two decades a huge number of interactive and collaborative applications of Virtual Environments for designing products has been proposed. Such applications have been recommended as tools to implement Human Centered Design Approach in experiments where potential users are involved in participatory design sessions before going for production. In this kind of experiments, we observe that users are mainly involved in the validation of solutions previously elaborated by designers while in the last decade the most innovative approach in the creation of solutions seems to be moving from a user centered design to a co-design or co-creation process. Thus, it is essential to have a platform where the elements of co-creation can be fulfilled in building a successful project. The purpose of this paper is to outline the concept of co-creation and the significance of co-creation platforms alongside of proposing innovative tools for building the co-creative environments. The paper aims to layout a classification of the Extended Reality (XR) tools currently available and of their functionality as valuable means to actually embed co-design and co-creation concepts in Virtual Environments evaluating the advantages that this can bring to Industry through field studies. A review of innovative solutions like Virtual, Augmented and Mixed reality technologies is examined and drawn towards the requirements of the concept through a literature research. Additionally, a co-creative environment for designing aircraft cabin interiors is conceived and discussed a with company representative.

Keywords: Co-creation , Collaboration , Extended reality , Interaction platforms

Industry 4.0 is characterized by great potential for innovation impacting the operator's role, increasingly engaged in smart activities of a decision-making nature. In such a working scenario, operators' working conditions can be effectively improved by applying a user-centered collaborative design approach. To this end, we developed a Virtual Reality-based multiplayer tool exploiting low-cost body tracking technology to evaluate ergonomic postural risk. The tool allows evaluating both in real-time and off-line the ergonomic postural risk according to the Rapid Upper Limb Assessment metrics. By applying this approach, a twofold advantage can be achieved. On the one hand, ergonomic experts can have an immersive three-dimensional visualization of postures even in off-line observations. On the other hand, it is possible to evaluate the ergonomics of workstations in the design phase by having the operator work on virtual mock-ups of workstations, thus allowing a sustainable approach to user-centered collaborative design.

Keywords: Collaborative design , Ergonomics , Kinect V2 , RULA , User-centered , Virtual Reality

Nowadays, User eXperience (UX) is one of the main concerns in developing innovative products. The literature already offers several UX design methods and tools. Among them, the interaction related Mental Models—based UX evaluation method appeared some years ago. Recently, the modular evaluation of key Components of User Experience questionnaire, a tool for quantifying products' UX, integrated this method. The knowledge acquired thanks to this synergy suggested the way to define and exploit guidelines for interactive redesign of the UX of products. Because of their origin, these guidelines encompass all UX characteristics reputed as meaningful like usefulness, subjective feelings, motivational aspects, emotions, etc. Moreover, these guidelines are generic enough to be used in different situations and on different products; their structure and functioning make them independent from the methods and tools used to collect data and to implement them, widening in this way their applicability in almost any UX interactive redesign activity by almost any researcher/practitioner. The redesign suggestions offered by these guidelines, as well as the procedures to collect and analyze the data to define other guidelines and suggest the best ways to implement them, push different competencies (engineers, UX experts, psychologists, etc.) to work in synergy, just like an interactive design context requires. To date, this research defined ten guidelines; a first adoption in the field starts witnessing their goodness and effectiveness. The paper describes in detail how to define and exploit the guidelines, as well as the first adoption of them in a real redesign context.

Keywords: Components of User Experience (CUE) model , Interaction related mental models (IrMMs)—based UX evaluation method , Interactive User eXperience redesign guidelines , Mental models , Product User eXperience

This paper introduces a system that enable the collection of relevant data related to the emotional behavior and attention of both student and professor during exams. It exploits facial coding techniques to enable the collection of a large amount of data from the automatic analysis of students and professors faces using video analysis, advanced techniques for gaze tracking based on deep Learning, and technologies and the principles related to the Affective Computing branch derived from the research of Paul Ekman. It provides tools that facilitates the interpretation of the collected data by means of a dashboard. A preliminary experiment has been carried out to investigate whether such a system may help in assessing the evaluation setting and support reflection on the evaluation processes in the light of the different situations, so as to improve the adoption of inclusive approaches. Results suggest that information provided by the proposed system can be helpful in assessing the setting and the evaluation process.

Keywords: Affective computing , Deep learning , E-leaning , Emotion recognition , Gaze tracking

This article introduces at a conceptual level a system based on AI technologies, able to determine the customer profile, in order to support customer experience design and management accordingly to a customer-centered approach, by extracting information from video stream provided by the security cameras installed in a store. The system collects customer demographic and behavioral information (e.g., age, gender, time spent in determined areas of the store, time spent interacting with the salesperson, etc.) through Deep Learning algorithms, in a completely anonymous way, without saving bio-metric data. To predict the customer profile based on the collected data it exploits a Bayesian Belief Network (BBN). The paper describes the overall system architecture, details the method used to model the BBN and reports, through the description of a use case scenario, some examples of insights useful to guide the choice of possible actions to be taken to improve the customer experience strategy.

Keywords: Customer experience , Customer profiling , Machine learning , Predictive models , Video analysis

In the industry 4.0 era, pursuing social sustainability also means implementing a synergic collaboration between workers and robots. Indeed, robot behavior does not affect only worker safety, but it also influences his health and comfort. In this context, an important topic to be enhanced is the operator's physical monitoring aimed at reducing the risk of musculoskeletal disorders. Some research studies deal with the improvement of the worker's posture during human-robot collaboration; however, non-intrusive methods applicable in real industrial scenarios are lacking. To this ending, this paper proposes a system to avoid uncomfortable and unsafe postures based on workers' anthropometric characteristics, posture monitoring by inertial and visual systems, task requirements, and a real-time risk assessment by standard methodology. The system allows the optimization of the robot behavior in order to improve worker's well-being. Finally, the virtual simulation of a real case study is presented.

Keywords: human factors , human-robot collaboration , industry 4.0 , physical ergonomics

The work environment influences workers' well-being and contributes to the growth of personal experiences. In fact, working in an unhealthy workplace can cause stress, frustration, and anxiety. Therefore, companies have to deal with the workers' well-being in the work environment, making the management of human factors a crucial aspect. In this context, the introduction of Industry 4.0 technologies can support workplace monitoring and improvement. Some researchers propose structured methods that consider several ergonomic domains together; however, it is necessary to create platforms that support data collection, elaboration, and correlation in an integrated way. Accordingly, this paper presents a tool that supports the monitoring of operators' activities, the data analysis, and the implementation of corrective actions to make the workplace socially sustainable. Preliminary tests were conducted to assess the functionality of the tool architecture and two use cases are presented. They focus on posture analysis and stress detection by inertial sensors and unsupervised machine learning algorithms, respectively.

Keywords: Human factors , Human-centered manufacturing , Industry 4.0 , Stress detection , Unsupervised learning , Worker well-being

In the paradigm of Industry 4.0, innovative workplaces characterized by Human-Robot Collaboration represent an important topic to improve productivity and adaptability of manufacturing plants. In this context, the design of a collaborative workplace is a challenging issue because of the high level of complexity due to multidisciplinary and non-homogeneity of its features, as well as the presence of human very close to the robot. This work faces with the complexity of collaborative workplace and proposes a structured framework to support strategic decisions in designing. It suggests a clusterization of factors and effects, based on five domains involved in collaborative workplace, in order to better consider the human safety and working conditions. Consequently, the main elements of a collaborative workplace are highlighted in a matrix decomposed in relevant features and main incident factors, and a multi-level designing workflow is described to report collaborative performances. The proposed approach manages connections among the elements by means of the graph theory in the form of an adjacency matrix in order to show and manage the complexity of the problem. A user interface named Smart Graph Interface was developed to read and manipulate the contents of the adjacency matrix. Main results are reported on an assembly and sealing of a refrigerator, to spread out principal outcomes in terms of applicability and robustness.

Keywords: Collaborative workplace design , Graph theory , Human-robot collaboration (HRC) , Multi-level designing , User interface

The fourth industrial revolution is evolving the machines as well as the abilities of people working in the factories. Human roles and tasks are changing, moving from highly physical tasks to decision-making and high-precision activities, asking for different competencies and creating new types of interactions with machines. This paper reviews the design and engineering methods for the inclusion of human factors in modern companies. Human factor integration (HFI) can play a key role in the design of factories with a great impact on social aspects and global process sustainability. The paper proposes a systematic view of the main tools to design human-centered industrial processes, with a specific focus on manufacturing, and discusses trends to achieve and effective HFI.

Keywords: Engineering methods , Ergonomics , Human factors , Human-centered design

Designing highly usable and ergonomic dashboards is fundamental to support users in managing and properly setting complex vehicles, like trains, airplanes, trucks and tractors. Contrarily, control dashboards are usually intrusive, full of controls and not really intuitive or usable. This paper focuses on the design of ergonomic and usable dashboard for specific classes of vehicles, like tractors and trucks. Indeed, trucks and tractors are both vehicles and operating machines, and their control is particularly complex. Indeed, the driver contemporary drives and checks if the machine is working properly. The paper proposes an innovative methodology to design highly usable and compact dashboards inspired by human-centered design and ergonomics principles. The study started by shifting the attention from the machine performance, that is the conventional engineering approach, to the human-system interaction quality, according to a new, transdisciplinary approach. The methodology proposes to combine virtual simulations with human performance analysis to support the design at different stages, from concept generation to detailed design, until testing with users. The methodology uses virtual environments to create digital twins of both driver and controls, making users interact with virtual items and predict the type and nature of interaction. Within virtual scenarios, different configurations of dashboard controls can be easily compared and tested, checking the frequency of use of each control and measuring the achieved human performance related to postural comfort and mental workload. The study adopted the proposed methodology to two industrial use cases focusing on the design of ergonomic dashboards: the former is referred to tractor dashboard and armrest, the latter refers to truck dashboard and seat. Both cases demonstrated that the new methodology allowed improved comfort, higher usability, higher visibility and accessibility, better performance and reduced time for machine control. The study demonstrates how a multidisciplinary user information integration can drive design optimization.

Keywords: Ergonomics , Human factors , Human-centered design , Usability , Virtual simulation

Successful interaction with complex processes, like those in the modern factory, is based on the system's ability to satisfy the user needs during human tasks, mainly related to performances, physical comfort, usability, accessibility, visibility, and mental workload. However, the ‘real' user perception is hidden and usually difficult to detect. User eXperience (UX) is a useful concept related to subjective perceptions and responses that result from the interaction with a product, system or process, including users' emotions, beliefs, preferences, perceptions, physical and psychological responses, behaviors and accomplishments that occur before, during and after use. The paper proposes the creation of a User eXperience Index (UXI) to assess the quality of human-system interaction during job tasks and, consequently, evaluate both process and workstation. The proposed approach has been applied to improve the design of assembly human tasks, using a virtual simulated case study focusing on tractor assembly. Tests with users, with different levels of expertise, allowed us to validate the proposed approach and to optimize the assembly task sequence. Results showed how the proposed UXI can validly objectify the workers' experience and can be validly used to improve the design of human tasks.

Keywords: ergonomics , human factors engineering , human monitoring , Human-centered design , transdisciplinary engineering , user experience , virtual assembly

Background: With the spread of COVID-19, telemedicine solutions became crucial to release continuous and remote assistance to chronic patients. The rapid transition to telemedicine solutions did not allow a complete assessment of the user experience by both patients and medical personnel. Despite the well-known benefits in remote care, the lack of usability evaluation of already existing technologies for the vital signs measurement has emerged. A telemedicine platform must match video communication between patients and the medical staff with the possibility to measure vital parameters. Furthermore, technological assistance may overcome the unfamiliarity with telemedicine and drastically reduce the learning time for both patients and medical personnel. Objectives: The research work presents a method to improve the user experience of a telemedicine service based on the combination of televisits and telemonitoring with wearable sensors for heart failure patients. Skilled technological staff is proposed by the presented method to lead the learning process of both medical personnel and patients in order to reach a high level of usability in less than 6 months. Methods: The proposed method is composed of 5 steps: identification of technological staff and end users; selection of the telemedicine platform; design of questionnaires for usability analysis; training of clinicians and patients; final usability evaluation of the telemedicine platform by means of customized satisfaction questionnaires and Post-Study System Usability Questionnaires (PSSUQs). The methodological approach has been tested in collaboration with a heart failure clinic by involving 5 physicians, 5 nurses, 15 heart failure patients, a research engineer and 4 technicians. A platform with 3 wearable sensors has been chosen: a wireless thermometer, a finger pulse-oximeter and an undershirt have been used for the detection of ECG trace. While these devices allowed asynchronous measurements of physiological data, scheduled televisits have been used for direct communication between physicians and patients. Results: Satisfaction questionnaires of patients and clinicians reached respectively 65.18% and 65.83%, while PSSUQ scores were respectively 91.73% and 81.70%. Both groups of end users confirmed a good level of usability and their satisfaction about the ease of use and the perceived quality of the instrumentation. Moreover, 73% of patients did not require help from caregivers to use the kit of sensors. The results have been reached in 5.5 months according to the aim defined initially. Research engineers have played a crucial role in helping clinicians and patients to improve the user experience with the telemedicine platform. Conclusions: The high level of usability and satisfaction confirmed that the proposed methodological approach helps to learn the technological features of the telemedicine platforms, which are based on different types of technology, such as web applications, wearable sensors and virtual calls. The positive results with heart failure patients encouraged to plan further research studies by using the designed method with other categories of chronic patients.

Keywords: Health informatics , Heart failure patients , Telemedicine , Televisit , Usability analysis , Wearable sensors

This paper introduces a web-platform system that performs semi-automatic compute of several risk indexes, based on the considered evaluation method (e.g., RULA—Rapid Upper Limb Assessment, REBA—Rapid Entire Body Assessment, OCRA—OCcupational Repetitive Action) to support ergonomics risk estimation, and provides augmented analytics to proactively improve ergonomic risk monitoring based on the characteristics of workers (e.g., age, gender), working tasks, and environment. It implements a body detection system, marker-less and low cost, based on the use of RGB cameras, which exploits the open-source deep learning model CMU (Carnegie Mellon University), from the tf-pose-estimation project, assuring worker privacy and data protection, which has been already successfully assessed in standard laboratory conditions. The paper provides a full description of the proposed platform and reports the results of validation in a real industrial case study regarding a washing machine assembly line composed by 5 workstations. A total of 15 workers have been involved. Results suggest how the proposed system is able to significantly speed up the ergonomic assessment and to predict angles and perform a RULA and OCRA analysis, with an accuracy comparable to that obtainable from a manual analysis, even under the unpredictable conditions that can be found in a real working environment.

Keywords: Ergonomics risk assessment , Extended reality , Human-centered manufacturing , Machine learning , Motion capture

Low-back exoskeletons are a wide-spreading technology tackling low-back pain, the leading work-related musculoskeletal disorder in many work sectors. Currently, spring-based (i.e., passive) exoskeletons are the mostly adopted in the industry, being cheaper and generally less complex and more intuitive to use. We introduce a system of interconnected wireless sensing units to provide online ergonomics feedback to the wearer. We integrate the system into our passive low-back exoskeleton and evaluate its usability with healthy volunteers and potential end users. In this way, we provide the exoskeleton with a tool aimed both at monitoring the interaction of the system with the user, providing them with an ergonomics feedback during task execution. The sensor system can also be integrated with a custom-developed Unity3D application which can be used to interface with Augmented- or Virtual-Reality applications with higher potential for improved user feedback, ergonomics training, and offline ergonomics evaluation of the workplace. We believe that providing ergonomics feedback to exoskeleton users in the industrial sector could help further reduce the drastic impact of low-back pain and prevent its onset.

Keywords: Ergonomics , Exoskeleton , Industrial sector , Low-back pain , Usability , User feedback

Municipal waste management, especially in developed countries, has a significant impact on the environment and humans that need to be quantified and mitigated. This study aims to investigate and compare the environmental impact and occupational risks of the following three municipal waste collection methods in Italy: street bins, door-to-door collection, and a smart bins system. Six waste categories (paper, plastics, glass, metal, organic materials, and residual waste) are analysed. Environmental performances are calculated by the life cycle assessment (LCA) and material and flow analysis (MFA) methodologies. The ergonomic analysis is based on direct observation of municipal waste collectors and the application of standard assessment methods as RULA, NIOSH, and Snook Ciriello. Results: show that the smart bins collection method is environmentally more effective in all the impact categories considered, thanks to the better quality of collected waste. The residual waste flow directed to landfills has a lower value in the case of smart bins (36.63%) in comparison with door-to-door (52.90%) and street bins (89.56%). The use of smart bins allows higher environmental benefits, passing for the Climate Change impact category from −2.80E+01 kg of CO2eq. of the door-to-door system and −2.74E+01 kg of CO2eq. of street bins to −7.13E+01 kg of CO2eq. of smart bins. Regarding the transport phase, the smart bins system determines a reduction of the impact of about 60% if compared with the door-to-door system for all the impact categories considered. On the other hand, acceptable occupational risks result in street and smart waste collection methods, while poor ergonomics conditions are observed during the door-to-door collection. Containers lifting and emptying are the most critical tasks.

Keywords: Ergonomics , Life cycle assessment , Occupational risks , Sustainability , Waste collection

One of the enabling technologies of Industry 4.0 is represented by collaborative robotics. The basis of its success lies in the increase in safety, productivity and flexibility but above all in the improvement of the employees working conditions, as well as their well-being. Human Robot Collaboration (HRC) applications are increasing due to the ease of communication between robots and humans. To this aim, this paper presents a vision based hand gesture recognition to help humans manage the workstation operations. An application with three levels of access has been proposed to guarantee an appropriate level of user friendliness. The system provides both hardware and software support for real-time gesture recognition. The developed system was tested using a standard webcam and applied to a case study to control the tasks performed by UR10e. Thanks to its fast and on point recognition, it can be used as a real-time gesture image recognition system.