Human Factors and Ergonomics in Manufacturing & Service Industries最新文献

Human error recognition and subsequent prioritization are the most important tasks in the human–robot reliability analysis. This study aims to address the issue of human error in human–robot collaboration (HRC) by developing a model for identifying and assessing risks. First, the key tasks performed by operators during HRC were identified using the hierarchical task analysis, and a cognitive model was built based on information processing theory. This model breaks down the collaboration process into stages and identifies potential human errors at each step. Next, failure modes and effects analysis and evidence reasoning were applied to quantify the risk levels of these errors. Finally, the risks associated with human errors were measured, ranked, and compared with existing studies, and recommendations were made. The findings showed that the leading causes of safety risks in HRC are fatigue, illegal operations, error operations, misjudgments, and misperception. The perception stage of the process was found to carry the highest risk level, which means operators are more likely to make errors during the perception stage than during decision or execution, largely due to factors such as fatigue, distraction, and misperception. These results provide important theoretical support for improving safety in HRC and offer practical suggestions for refining risk management strategies in HRC systems.

Acute pain from injury is a major concern in sport and public health. Athletes are known to have higher pain tolerance than the general population, suggesting that they may manage pain differently. This study aims to elicit the “how” and “why” of acute pain management strategies when coping with sport injuries. A thematic analysis of qualitative data involved 12 injured elite athletes (EAs) to ascertain their drug and nondrug acute pain management strategies. Results showed 14 strategies relating to two functions: problem-focused and emotion-focused. Six problem-focused strategies aimed to change the relationship between an individual and his/her environment: (a) modulating activity; (b) seeking instrumental support; (c) goal setting; (d) encouraging oneself; (e) taking painkillers; and (f) reframing the perception of pain. Eight emotion-focused strategies aimed to manage emotional distress: (a) diverting attention; (b) acceptance; (c) relativising pain; (d) repeating negative thoughts; (e) ignoring pain; (f) seeking emotional support; (g) hoping and praying; and (h) self-blame. EAs were shown to favor three strategies: modulating activity, diverting attention, and seeking instrumental support. Results showed a repertoire of acute pain management strategies and the function each strategy served. The variety of pain management strategies reinforces the coping functions (Lazarus and Folkman 1984) and suggests newly-identified flexibility in coping with acute pain extending beyond previous results on chronic pain. Results enriched the pain management approach concerning nondrug-strategies for patients in acute pain. They could offer new perspectives on human factors in health to improve acute pain management in the general population.

Pilots of long-haul aircraft face a variety of challenges, including unstable flight environments, confined and narrow cockpit spaces, complex human–machine system operations, multiple tasks, and long-haul flight times. This study analyzed the factors leading to pilot fatigue from four aspects (human, machine, environment, task) and predicted the fatigue risk of long-haul flights using a dynamic Bayesian networks method. First, we identified factors related to fatigue during long-haul flights from four aspects: human, machine, environment, and task, and established an index system containing 20 fatigue risk factors. Second, 10 experts in the field of aviation evaluated these factors within the fatigue risk system to derive the prior probabilities for the dynamic Bayesian networks on pilot fatigue on long-haul flights. Finally, we introduced the Noisy-OR model to derive the conditional probabilities and calculated the posterior probabilities using the dynamic Bayesian networks. We validated the proposed method with a real case study, and the results showed that this method can predict fatigue during long-haul flights.

Every year, health tourism organizations (HTOs) provide numerous patients worldwide with specialized or less costly medical services. Any crisis or uncertainty can have detrimental effects on the performance of these organizations, the behavior and emotions of staff, as well as the economy of the host country. Therefore, it is essential to evaluate the resilience of these organizations and the behavior of their staff in facing challenges and crises. In this study, we assessed the performance of an HTO in Tehran using resilience engineering and emotional intelligence indicators. The required data were collected using standard questionnaires, and data envelopment analysis was employed to study the performance of this HTO. Sensitivity analysis was applied to evaluate the performance of the HTO concerning each indicator. Finally, the strengths, weaknesses, opportunities, and threats matrix was applied to propose strategies aimed at enhancing the performance of the studied HTO.

In the high-risk environment of drilling operations, mind-wandering is a significant contributor to unsafe behavior and reduced safety performance. Despite its critical impact, few studies have focused on the cognitive characteristics of mind-wandering and its influence on situation awareness (SA). This study sought to bridge the research divide by utilizing electroencephalography (EEG) to explore the link between mind-wandering and brain activity, with the objective of establishing an objective means to monitor drillers’ mind-wandering. Utilizing the Situation Awareness Global Assessment Technique (SAGAT) in a simulated drilling monitoring task, we compared the states of 50 natural gas drillers before and after their shifts. Brainwave data were recorded and categorized into pre-shift and post-shift groups. Our analysis revealed a significant increase in mind-wandering post-shift, coupled with a notable decline in SA. This effect was especially pronounced during complex tasks, such as gas kick simulation videos, where response times increased and delta and alpha band power in the frontal lobes significantly rose. These findings suggest that mind-wandering adversely affects SA, particularly in complex task scenarios. The identified neural indicators offer objective measures of drillers’ cognitive states and can inform attention training and fatigue management strategies. By providing safety managers with real-time insights into workers’ attention, mind-wandering detection can help mitigate errors and enhance operational safety in high-risk environments.

Human factors methods, as a systems discipline, can be applied across various areas of working systems. Risk assessment methods are particularly useful for identifying risks that may impact the performance of overall working systems, groups, and individuals. The emergency evacuation process in hospitals involves multiple risks that can significantly affect its performance. This study applied a systems thinking-based risk assessment method to identify risks associated with hospital emergency evacuations. The Networked Hazard Analysis and Risk Management System (Net-HARMS) method was utilized to identify all credible risks in the hospital evacuation process during emergencies that could degrade optimal performance. Some of the key risks identified in the hospital emergency evacuation process included delays in assessing risks associated with evacuation procedures, failures or delays in forming and appointing an emergency evacuation and command team, and inadequate intra- and interorganizational coordination. Additionally, emerging risks were identified, such as delays in the evacuation process due to staff lacking sufficient information about the evacuation and incident command team members, as well as delays in receiving assistance from external organizations like the fire department and Red Crescent due to inadequate interorganizational coordination processes. These risks arose from the interactions between activities. The study concludes that the Net-HARMS method is effective in forecasting systemic and emergent risks in the hospital evacuation process, as well as identifying risks associated with specific activities and emergent risks in this critical process.

Despite the many studies investigated the impact of icon design on usability in the past, few have compared outline icons with solid icons. This study combines familiarity training, recognition tasks, and visual search tasks to explore how icon design style and internal cognitive characteristics jointly affect visual perception. A total of 120 pairs of solid icons and corresponding outline icons were collected and designed. Subsequently, participants were asked to rate icons based on familiarity and concreteness, excluding those that were either too familiar or too unfamiliar. After 27 participants were familiarized with all of the icons over two training sessions, they were required to complete the task of recalling icons with relevant semantic meanings. Finally, to further decompose the users' visual perception process, participants' ability to visually search for icons was additionally tested. The results indicated that participants performed significantly better at recognizing and visually searching for solid icons, especially when they were unfamiliar. However, the visual perception advantage decreased with an increase in familiarity. In addition, strong evidence was found indicating that concrete solid icons have the highest visual search performance. The findings in this study provide practical guidelines for user interface design.