Human Factors Engineering Research Articles

The role of human factors engineering in patient safety.

To explore the collaboration between human factors (HFs) experts and clinicians in order to improve perioperative patient safety. Recent recommendations to integrate human factors into anesthesia in the United Kingdom emphasizes the value of applying disciplines outside of medicine to optimize the patient experience. Human factors engineering is underutilized worldwide. Patient safety would benefit from collaboration with HF experts to design resilient systems. Healthcare organizations must consider HF to develop and implement user-centered solutions to improve safety for patients and professionals.

Research on the Visual Design Elements of the Living Street from the Perspective of Human Factors Engineering

Research on the Visual Design Elements of the Living Street from the Perspective of Human Factors Engineering

The Missing Engineering Discipline in Cybersecurity: Human Factors Engineering

Human factors remain the most critical vulnerability in cybersecurity, with many breaches involving human errors or social engineering. Integrating human factors engineering (HFE) into cybersecurity strategies is crucial for addressing these vulnerabilities and enhancing security resilience. Despite its importance, HFE is not widely recognized as an engineering discipline in cybersecurity, and many educational programs lack dedicated courses on human factors. Academic institutions, cybersecurity professionals, and federal agencies must advocate for incorporating HFE into cybersecurity curricula to prepare professionals with a comprehensive understanding of human behavior and its impact on security, ultimately leading to more robust and resilient cybersecurity practices.

Promoting human-centered manufacturing through Lean Ergonomics – a structural equation model for ergonomics and management data

ABSTRACT The Stress-Strain Concept (SSC) describes central mechanisms in Lean Ergonomics (LE) and Human Factors Engineering (HFE). The SSC can create additional value through content and methodological enrichment based on current economic, technological, and demographic constraints. This article uses the SSC to explore mechanisms of interactions of productivity and ergonomics in empirical shop floor data. Ergonomic (Borg, NASA-Task Load Index (N-TLX), Ergonomic Assessment Worksheet (EAWS), Health, Age) and business data (normalized and absolute standard deviations of work process times) were collected for 24 manual work processes operating large-scale chemical reactors. Data is analyzed using several Structural Equation Models (SEM). Stress (EAWS) and – depending on Health – Physical Strain with a model quality of Adjusted Goodness-of-Fit ≥0.9 were identified as statistically significant regressors for time data. In addition, carry-over effects from ergonomics to productivity were identified in an autoregressive model. Fit indices are in the good to acceptable range.

Application of Human Factors Engineering in the Design of Logistics Systems

Application of Human Factors Engineering in the Design of Logistics Systems

Human Factors Engineering and Diabetes Technology: A Close Relationship.

Human Factors Engineering and Diabetes Technology: A Close Relationship.

Systematic Improvement of a Bolus Calculator That Is on the Market: A User-Centric Approach.

State-of-the-art diabetes self-management includes the usage of (software) tools, such as Bolus Calculators, to support patients with their therapeutic decisions. The development of such medical devices comes with strict obligations to ensure the safety and performance for the user; however, it is also necessary to continue to evaluate such aspects after the products are introduced into the market. In addition, such aspects cannot always be sufficiently validated by clinical trials; they need real-world evaluation to systematically improve such tools while they are on the market. The approach described here uses innovative ways of generating user-centric evidence to improve the bolus calculator, including (1) human factor engineering, (2) analysis of glycemic real-world data, (3) patient-reported outcomes, and (4) machine-generated behavioral measurements. The combination of the diverse techniques to optimize the bolus calculator triggered changes in the user experience: a significant reduction in hypoglycemic events, -0.52% (±0.05), P < .01, n=3480, an increased diabetes treatment satisfaction (Diabetes Treatment Satisfaction Questionnaire [DTSQ] +9.90, P < .01, n=217), as well as an increased acceptance rate of bolus calculations, +15.73 (±0.89), P < .01, n=3436, were observed. Altogether, human factor engineering and different forms of real-world data support fast and direct adaptations and improvements in products used for diabetes therapy.

Task reliability index for operator performance and failure probability assessment in control room simulators

Main control room simulators are an important information source for improving the empirical basis of human reliability analysis (HRA). Both quantitative and qualitative observations provide valuable insight on the operators’ ability to successfully perform their tasks. However, relevant portions of this evidence are not used in typical approaches for estimation of human error probabilities (HEP), which are based on failure counts. With small sample sizes and the operator performance levels representative for nuclear power plants, very little information enters the probability estimates. This paper presents a novel approach to formalize the collection and use of reliability-relevant evidence from simulators. The methodology treats task reliability with the plant-centred orientation characteristic of the systems or Probabilistic Safety Assessment (PSA) perspective as well as a human-centred view of task performance based in human factors engineering. These are combined to form a Task reliability index (TRI) able to distinguish finer performance variations and consider indications of potential failures. Bayesian updating of HEP distributions is suggested by translating the TRI data into pseudo-failures and trials, preserving the convenient coupling of Beta-Binomial distributions in the Bayesian analysis. The approach is demonstrated using data from the International HRA Empirical Study and is shown to produce results that are comparable to those obtained in the empirical study, which were based on comprehensive expert analyses.

Development and Validation of the Hospital-to-Home-Health Transition Quality (H3TQ) Index: A Novel Measure to Engage Patients and Home Health Providers in Evaluating Hospital-to-Home Care Transition Quality: A Novel Measure to Engage Patients and Home Health Providers in Evaluating Hospital-to-Home Care Transition Quality.

Patients requiring skilled home health care (HH) after hospitalization are at high risk of adverse events. Human factors engineering (HFE) approaches can be useful for measure development to optimize hospital-to-home transitions. To describe the development, initial psychometric validation, and feasibility of the Hospital-to-Home-Health-Transition Quality (H3TQ) Index to identify patient safety risks. Development : A multisite, mixed-methods study at 5 HH agencies in rural and urban sites across the United States. Testing : Prospective H3TQ implementation on older adults' hospital-to-HH transitions. Populations Studied : Older adults and caregivers receiving HH services after hospital discharge, and their HH providers (nurses and rehabilitation therapists). The H3TQ is a 12-item count of hospital-to-HH transitions best practices for safety that we developed through more than 180 hours of observations and more than 80 hours of interviews. The H3TQ demonstrated feasibility of use, stability, construct validity, and concurrent validity when tested on 75 transitions. The vast majority (70%) of hospital-to-HH transitions had at least one safety issue, and HH providers identified more patient safety threats than did patients/caregivers. The most frequently identified issues were unsafe home environments (32%), medication issues (29%), incomplete information (27%), and patients' lack of general understanding of care plans (27%). The H3TQ is a novel measure to assess the quality of hospital-to-HH transitions and proactively identify transitions issues. Patients, caregivers, and HH providers offered valuable perspectives and should be included in safety reporting. Study findings can guide the design of interventions to optimize quality during the high-risk hospital-to-HH transition.

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection.

Persuasive technologies, in connection with human factor engineering requirements for healthy workplaces, have played a significant role in ensuring a change in human behavior. Healthy workplaces suggest different best practices applicable to body posture, proximity to the computer system, movement, lighting conditions, computer system layout, and other significant psychological and cognitive aspects. Most importantly, body posture suggests how users should sit or stand in workplaces in line with best and healthy practices. In this study, we developed two study phases (pilot and main) using two deep learning models: convolutional neural networks (CNN) and Yolo-V3. To train the two models, we collected posture datasets from creative common license YouTube videos and Kaggle. We classified the dataset into comfortable and uncomfortable postures. Results show that our YOLO-V3 model outperformed CNN model with a mean average precision of 92%. Based on this finding, we recommend that YOLO-V3 model be integrated in the design of persuasive technologies for a healthy workplace. Additionally, we provide future implications for integrating proximity detection taking into consideration the ideal number of centimeters users should maintain in a healthy workplace.

Developing, Implementing, Evaluating Electronic Apparent Cause Analysis Across a Health Care System

An interdisciplinary team developed, implemented, and evaluated a standardized structure and process for an electronic apparent cause analysis (eACA) tool that includes principles of high reliability, human factors engineering, and Just Culture. Steps include assembling a team, describing what happened, determining why the event happened, determining how defects might be fixed, and deciding which defects will be fixed. The eACA is an intuitive tool for identifying defects, apparent causes of those defects, and the strongest corrective actions. Moreover, the eACA facilitates system learning by aggregating apparent causes and corrective action trends to prioritize and implement system change(s).

Sustainable Development Methods of Urban Slow-traffic System Based on Human Factors Engineering

At present, China's urban transportation is in a period of development and transformation, and there are heated discussions about transportation sustainability. With China's new urban planning strategy and the future development trend of roads, the construction and development of the slow-traffic system have become a new direction of exploration. Through the analysis of human psychology and physiology, this article further explores the sustainable development of slow-traffic systems without sacrificing human welfare. Through the impact on the psychological safety of users based on distance in the street space, and through the three physiological stimulations of color and light on vision, noise on hearing, and ground materials on touch, it strengthens the sense of security for users in road space. Controllability and increased frequency of use. Integrate slow traffic into existing urban arterial roads and public transportation in more possible ways, reactivating street vitality and enriching street life. While achieving the "double carbon" goal, urban roads should be constructed meticulously to improve the quality of life of residents. Nowadays, there are many studies on the use of new technologies for road management, but few on road planning methods that focus on people themselves. After research and discussion, this article can fill the gaps in related research and contribute to the sustainable development of the slow-traffic system.

Sustainable Development Methods of Urban Slow-traffic System Based on Human Factors Engineering

At present, China's urban transportation is in a period of development and transformation, and there are heated discussions about transportation sustainability. With China's new urban planning strategy and the future development trend of roads, the construction and development of the slow-traffic system have become a new direction of exploration. Through the analysis of human psychology and physiology, this article further explores the sustainable development of slow-traffic systems without sacrificing human welfare. Through the impact on the psychological safety of users based on distance in the street space, and through the three physiological stimulations of color and light on vision, noise on hearing, and ground materials on touch, it strengthens the sense of security for users in road space. Controllability and increased frequency of use. Integrate slow traffic into existing urban arterial roads and public transportation in more possible ways, reactivating street vitality and enriching street life. While achieving the "double carbon" goal, urban roads should be constructed meticulously to improve the quality of life of residents. Nowadays, there are many studies on the use of new technologies for road management, but few on road planning methods that focus on people themselves. After research and discussion, this article can fill the gaps in related research and contribute to the sustainable development of the slow-traffic system.

Enabling reliable usability assessment and comparative analysis of medical software: a comprehensive framework for multimodal biomedical imaging platforms

PurposeA literature review reveals that, at the moment, all usability tests for Software as a Medical Device (SaMD) are designed in compliance with international standards but it also reveals a lack of formalization in the implementation and administration of such usability tests, which prevents the comparison of results from different tests for the same class of SaMD. This study aims to provide a reproducible usability testing framework for SaMD to establish a standardized protocol which can ensure repeatability and comparisons of similar SaMD for the visualization of medical images and data.MethodsThe devised protocol aligns with international standards and literature recommendations for usability and human factors engineering. It encompasses participant selection, testing environments, equipment setup for various testing methods (HDMI vs. wireless), and hardware interfaces (keyboard/mouse vs. touchscreen), as well as the roles of the required testers. The protocol consists of two distinct sections: exploratory tasks and specific scenarios, to assess software functions and real-life tasks, respectively. Effectiveness and efficiency are evaluated using video analysis and a custom Stopwatch software, while user satisfaction is measured through post-test questionnaires.ResultsThe usability testing protocol was applied to a Multimodal Biomedical Imaging Platform All-in-One software developed by Imaginalis S.r.l. (Sesto Fiorentino, Italy) for validation. The results of the usability testing protocol applied to the case-study software demonstrate good values of software’s effectiveness and efficiency, along with user satisfaction supporting the prior heuristic evaluation. The outcomes confirm the robustness, applicability, and reproducibility of the usability testing protocol, aligning with best practices.ConclusionsThe proposed usability testing framework enables reliable usability assessment and comparative analysis of medical software. Furthermore, the obtained results can serve as a reference for assessing other biomedical imaging platforms under development or ready for release.

Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. The artificial intelligence algorithms demonstrate the correlation between the number of vacant staff jobs and both the frequency of license event reports each year and the ratio of contract employees to regular employees in the administrative domain. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective machine learning neural network models are derived from generative adversarial network (GAN) algorithms and proposed for administrative, operational and maintenance loops of nuclear reactor control unit.

Aircraft maintainability verification evaluation based on fuzzy analytic hierarchy process

This paper uses the fuzzy analytic hierarchy process to establish the mathematical model of aircraft maintainability verification in the flight test stage. The maintainability evaluation index system is constructed from the factors that affect the aircraft maintainability level, such as equipment layout and accessibility, cover design, marking and error prevention, standardization and interchangeability, and human factor engineering. Through case analysis, the application of this method in the maintainability verification of the military aircraft flight test phase is introduced.

A Regulatory Perspective: Have We Done Enough on Grasping Automation Failure?

This paper responds to Skraaning and Jamieson’s target paper “The Failure to Grasp Automation Failure.” We acknowledge that the target paper made important contributions to automation research in the human factors community. It analyzed automation failure events in complex operational systems in contrast to the vast majority of laboratory research on human-automation interaction. The paper presented a taxonomy of automation failure. The analysis and taxonomy demonstrate the integration of approaches to grasping automation failures from system instrumentation and controls, human factors engineering, and human reliability analysis. We reviewed the regulatory framework related to use of automation in nuclear power plants and examined whether the framework elements adequately address “Failure to Grasp Automation Failure” using the taxonomy in the target paper. Overall, we believe that the target paper could enhance the consideration for potential automation failures in the design and regulatory review process of automation technologies.

The Airport and the Zoo

This article focuses on the shared history of Frankfurt Zoological Garden and Airport, developing an infrastructural perspective on the rise of behavioral thinking in postwar environmental sciences. It shows that technical and logistical environments, such as seaports, airports, train stations, and highways, shaped the way environmental scientists and a broader public conceptualized animal behavior. Similar to ergonomics and human factor engineering in the 1950s and ’60s, human-made spaces and interiors became central reference points for developing and testing new ideas on the interaction between animals and “the environment.” Further, airports were sites of knowledge transfer between practitioners, including animal traders, care attendants, veterinarians, logistics experts, and scientists. The first part of the article draws on the works of biologist and Frankfurt’s Zoo director Bernhard Grzimek, who made excessive use of air cargo to ship wild animals to Frankfurt. The second part traces the emergence of new spaces for animals at Frankfurt Airport in the 1950s—such as the “animal room” in the cargo area. The third part of the article uncovers the hidden networks of animal traders and shipping companies that made Frankfurt Airport a major international hub in animal air cargo. Lastly, the article argues that hands-on experience of animal behavior, such as that conducted in airports like Frankfurt, is part of a broader pattern of environmental knowledge production in in the twentieth century—in close proximity to infrastructure projects.

XR and digital twins, and their role in human factor studies

Digital twins (DT) are synchronized clones that mirror their physical counterparts at all times, enabling real-time monitoring, analysis, decision-making, and planning for optimized operations. Digital twins can transcend traditional two-dimensional interfaces by incorporating VR/AR/MR (XR) technology, providing immersive, intuitive, and interactive representations of systems, assets, and processes. Furthermore, real-time data from sensors, simulations, and other sources can be integrated into the XR-enabled digital twins, leading to better and more intuitive understanding and to more efficiently monitor, analyze, and maintain complex systems such as nuclear assets. Immersive, interactive, multi-player XR capabilities embedded in DTs will allow spatially accurate and more realistic representation, leading to improved risk assessment, optimized and predictive maintenance scheduling, enhanced situational awareness, and more effective communication among interdisciplinary teams. By combining the strengths of XR and digital twins, nuclear facilities can achieve heightened safety, operational efficiency, and decision-making accuracy. Marrying XR technology with digital twins is also likely to extend the utilization of digital twins to optimize those aspects of the design and operation of nuclear assets that involve human beings–specifically in human factors engineering. Training can also be significantly enhanced if DTs are linked with XR technology. These systems may also be used to assess human performance through human factors engineering for the safety analysis of nuclear assets. A specific example is assessing human performance in semi-autonomous nuclear assets or operating multiple nuclear assets. After briefly reviewing digital twins of nuclear systems from the perspective of XR technology, this paper summarises our work in the nuclear energy space on VR/AR/MR and how these can be integrated into the newly evolving DTs of nuclear assets. The paper also describes the potential use of such systems in optimizing the design and operations of nuclear systems. As XR technology advances, its symbiotic relationship with digital twins can significantly reshape the landscape of nuclear operations and asset management.

An effective cross-scenario remote heart rate estimation network based on global-local information and video transformer.

Remote photoplethysmography (rPPG) technology is a non-contact physiological signal measurement method, characterized by non-invasiveness and ease of use. It has broad application potential in medical health, human factors engineering, and other fields. However, current rPPG technology is highly susceptible to variations in lighting conditions, head pose changes, and partial occlusions, posing significant challenges for its widespread application. In order to improve the accuracy of remote heart rate estimation and enhance model generalization, we propose PulseFormer, a dual-path network based on transformer. By integrating local and global information and utilizing fast and slow paths, PulseFormer effectively captures the temporal variations of key regions and spatial variations of the global area, facilitating the extraction of rPPG feature information while mitigating the impact of background noise variations. Heart rate estimation results on the popular rPPG dataset show that PulseFormer achieves state-of-the-art performance on public datasets. Additionally, we establish a dataset containing facial expressions and synchronized physiological signals in driving scenarios and test the pre-trained model from the public dataset on this collected dataset. The results indicate that PulseFormer exhibits strong generalization capabilities across different data distributions in cross-scenario settings. Therefore, this model is applicable for heart rate estimation of individuals in various scenarios.