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

In the digital interface of multimodal audio–visual presentation, the appearance of irrelevant information often brings cognitive interference or even confusion, leading to decision-making errors when users focus on or manipulate the interface target. However, few studies have explored the brain's inhibition effect and cognitive law evoked by audio–visual interference from the perspective of interface information design. On the basis of Stroop's classic interference task, an experimental paradigm of multimodal audio–visual stimuli to induce event-related potential (ERP) components was designed for digital interfaces in this study. Combining behavioral measurement and ERP technology, this study discussed the differences in the induced inhibition effects between the two carriers under various audio–visual interferences. The findings demonstrated that all five interference stimuli, based on functional icons and Chinese characters, elicited significant N250 and N400, with a similar time course. Compared with the Chinese character group, the functional icon group elicited more negative activity in the frontal and some parietal-occipital regions, indicating that the functional icon required more cognitive inhibitory resources to resist interference stimuli. Moreover, the inhibition effect induced by audio–visual interference with the same semantics was significantly lower than that of opposite semantics and even lower than that of single-sensory interference. The findings offered physiological evidence for the inhibition effect induced by audio–visual semantic interference in digital interfaces and proposed design principles for the interface information of human–machine systems.

Shoe manufacturing companies often use overtime work but neglect the impacts and importance of physical recovery time. Ergonomic methods aim to analyze this, but they focus on deterministic aspects, which limits their ability to evaluate working conditions amid variations over time. This research explores how a simulation model can mitigate these limitations and enhance analysis of overtime and physical recovery on worker absenteeism. The objective was developed a simulation model using System Dynamics (SD) to represent working conditions and assess the influence of overtime and recovery time in Brazil's footwear industry. An Ergonomic Analysis of Work was conducted in a large company's production cell. Using SD, were constructed a causal and simulation model to analyze three scenarios. An additional hour of work increased physical overload by 44%, leading to 5, 4 leave requests, and 48 days of absenteeism per year. Increasing recovery time by 15 min reduced overload to 38,96%, resulting in 4, 9 leave requests and 13,68 days of absenteeism. The SD simulation model mitigated the limitations of ergonomic methods in understanding the dynamic relationships over time, emphasizing the importance of actively managing overtime and physical recovery time.

Due to the physical nature of their work, sonographers are exposed to many musculoskeletal disorder risk factors, including awkward posture, repetitive movements, forceful manual exertion, and static muscle contractions, especially in the upper limbs. The current study is an investigation of musculoskeletal disorders among sonographers, caused by various occupational risk factors via different sonographic scan types. During the first phase of this study, the musculoskeletal symptoms and work postures of 29 subjects were investigated. During the second phase, muscle activity was quantified, and grip/push forces were estimated using the data obtained from 10 volunteer sonographers. 82% of sonographers experienced musculoskeletal symptoms. Based on the final scores and action levels obtained via rapid upper limb assessment, while performing scans of left regions; ergonomic changes and interventions were found necessary, to relieve stress on the sonographer's body. The results of muscular activity per muscle and scan type, showed that the mean muscle activity of the middle deltoid muscle was significantly higher during the right abdominal scan (17.64% maximum voluntary contraction [MVC]), compared to those of thyroid (12.54% MVC) and left abdominal (7.32% MVC) scans. Additionally, mean grip and push forces during both abdominal scans were significantly higher than those during the thyroid scan. Despite an injury risk during all scans, risk factor impact was different among scan types. This groundbreaking study represents the first that captures and measures both grip and push forces simultaneously, which may prove helpful while investigating corrective interventions or optimizing design of sonography robots and ergonomic probes in future studies.

Driver distraction is intricately linked to human behavior and cognitive ergonomics, as it explores how human engagement with various stimuli influences attention and decision-making processes while driving. The main purpose of this study is to comprehensively explore whether using Human–Machine Interface infotainment systems in automated vehicles can affect driver distraction. To this end, driver distraction was measured by driving performance features (speed, lane position, and reaction time), behavioral features (fixation time and pupil dilation), physiological features (changes in oxyhemoglobin), and subjective assessment (NASA-TLX workload). Twenty-one participants equipped with an eye tracker and functional near-infrared spectroscopy drove a driving simulator in the current investigation. The results revealed that interacting with the infotainment systems significantly affects the drivers' average speed (F_2,40 = 13.60, p < .0001), reaction time (F_2,40 = 4.74, p = .0142), fixation time (F_2,40 = 88.61, p < .0001), pupil dilation (F_2,28 = 3.63, p = .0356), and workload (F_2,40 = 14.40, p < .0001). Moreover, driving mode significantly affects drivers' speed deviation (F_2,40 = 6.12, p = .0048), standard deviation of lane position (F_2,40 = 10.57, p = .0002), fixation time (F_2,40 = 36.71, p < .0001), and workload (F_2,40 = 28.08, p < .0001). Drawing from the findings of this article and emphasizing human-centric design principles, researchers and engineers can craft automotive technologies that are intuitive, effective, and safer. This is vital for mitigating driver distraction and guaranteeing the beneficial influence of automated vehicles on both road safety and the overall driving experience.

Functional resonance analysis method (FRAM) is extensively employed in analyzing and managing performance variabilities. Additionally, semi-quantitative and quantitative methods have been increasingly integrated with the FRAM to analyze complex socio-technical systems to improve safety levels. This review article presents a comprehensive and updated survey of current literature focused on semi-quantitative and quantitative methods employed for quantifying performance variabilities and exploring aggregation/propagation rules. A total of 1659 studies published between 2012 and March 2024 from various scientific databases were systematically examined using preferred reporting items for systematic review and meta-analysis, identifying 29 studies that met inclusion criteria. The identified studies were categorized into four groups based on the quantitative methods employed: Monte Carlo simulation, fuzzy logic, cognitive reliability and error analysis method, and miscellaneous approaches. While different methodologies had unique strengths, they commonly relied on expert judgment for data collection, whether for defining probability distributions in Monte Carlo simulations, membership functions, and fuzzy rule bases in fuzzy inference systems, or selecting common performance conditions, determining their interrelationships, and assigning scores. Addressing bias from expert judgment in assessing performance variabilities can be achieved by using suitable experts' opinions integration techniques, and leading safety indicators in the analysis.

An important question in teamwork research is how to maximize performance and the aspects of the team's dynamics and collaboration process that underpin it. Prior research has shown that when team members who are collaborating towards a common purpose experience flow together (team flow; optimal experiences that occur simultaneously at the individual and team levels, entailing deep focus and intrinsic motivation to perform an activity), the team significantly improves its performance and team members experience many positive results at both the individual and team levels. Further advances have built a model of team flow and a means for measuring the construct, as well as qualitative results in business teams to confirm how the elements of team flow interact to generate the positive experiences and higher performance. This study adds practical value to the research by providing proof-of-concept for an intervention that promotes team flow in business teams. This cross-case-study of 15 teams across five different organizations uses the Team Flow Monitor as a barometer of team health and dynamics, which in turn serves as the centerpiece of an iterative intervention protocol for leading/guiding teams in targeted self-reflection that can generate virtuous cycles of improving dynamics and performance. In addition to a significant amount of qualitative data confirming the efficacy of the intervention in enabling teams to overcome obstacles and experience more team flow, quantitative analysis of Team Flow Monitor scores showed an increase on average team flow scores across the teams over the course of the intervention (Cohen's d = 0.6). Implications for translating team flow research to field situations are discussed, along with further potential uses of the Team Flow Monitor.

Manual operations remain crucial in critical human-machine interactions due to limitations in automatic control algorithms and technologies. While ergonomic analysis and evaluation techniques for interactive interfaces are advancing, recent approaches emphasize integrating graphical interface experiences with intuitive controls. Conventional methods often lack precision or overlook the interaction effect of different influencing factors, leading to inadequate assessment of essential manual operations for intricate interfaces, such as multifunctional operation panels. To address these challenges, this study aimed to investigate the interaction effects of various factors on the manual operation performance of operators when using a multifunctional operation panel and aims to develop a more comprehensive and broadly applicable evaluation model for such panels. An experiment was designed to consider the type, size, layout of controls, and operational task type as the main factors affecting manual operation performance. Multivariate analysis of variance was used to identify significant interaction effects among the operation factors. The findings underscored the importance of these interactions in evaluating manual operation performance. Multivariate linear regression further examined the influence of these factors, enhancing the evaluation methodology. The study emphasizes the critical role of understanding interaction effects in assessing the manual operation performance of multifunctional operation panels, particularly in improving the design of the panel or operation tasks.

The COVID-19 pandemic challenged health care organizations to cope with major disruptions, especially in the first waves. Several investigations were undertaken to understand how to support resilience during similar unexpected events. In this study, we attempted to unveil the resilient performance of health care organizations during the first waves of the COVID-19 pandemic from the viewpoint of resources for action. Thus, the research objectives are twofold: (i) to evaluate organizational resilience in facing COVID-19 by hospitals in Brazil and (ii) to evaluate the relationship between resources for action and resilient performance. Firstly, an online survey was sent to front-line health care workers, resulting in 111 responses. Then, a questerview was undertaken through online interviews with some participants of the previous phase. Resources for action were interpreted as five aspects supporting decision-making in health care organizations: information and communication; team, equipment, and tools; standard operating procedure (SOP); training; and built environment. Each resource was then unfolded based on the four potentials for resilient performance (i.e., anticipate, monitor, respond and learn). Respondents strongly agreed that their institutions are resilient (M = 4.15; standard deviation [SD] = 0.91). The potentials to learn (M = 4.23; SD = 0.96) and respond (M = 4.08; SD = 1.02) stood out, followed by monitoring (M = 3.85; SD = 1.07) and anticipating (M = 3.70; SD = 1.11). Although some differences stand out, findings corroborate with the joint performance of the resources for action to support resilience performance. Information and communication were the most present among the resources for action (M = 4.20). Making resources for action visible is a strategy for designing resilient systems, as it can be considered a bridge linking different resilience levels (micro, meso, and macro). Suggestions for future studies point out the need to promote the development and evaluation of resources for action in health care institutions.

Incorporating ergonomic considerations into an assembly-line balancing problem (ALBP) enhances productivity and minimizes ergonomic concerns. The assembly process, characterized by repetitive motions and handling numerous components, can lead to worker overload. Consequently, the inclusion of ergonomic aspects results in an appropriate distribution of assembly operations and relative workloads. This study investigates a multi-objective ALBP aimed at minimizing the number of workstations, overall skill level required, and variance in workers' energy expenditure across workstations. To address the ALBP while considering the ergonomic aspects, this study proposes an approach based on the non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective simulated annealing (MOSA) using Pareto optimality. A comparative analysis of the NSGA-II and MOSA is conducted in single- and multiproduct production scenarios, and a computational study involving various factors is performed to identify the dominant algorithm. The computational analysis indicates that the runtime performance of MOSA is 73.287% better than that of NSGA-II; therefore, MOSA outperforms NSGA-II. This study aims at applying scientific knowledge concerning manufacturing ergonomics to assist manufacturing industries in enhancing their productivity.

In this paper, we determined the adjustment levels of the human–machine layout under the preferred driving posture for individuals with different body sizes. We also comparatively analyzed the maximum activation levels of various muscles under straight-line driving and steering conditions. To increase the accuracy of the results, AnyBody biomechanics software was used to establish a human skeletal muscle mechanics model, which we analyzed for consistency with rig test results. The results showed that people with larger body sizes preferred a driving position with the seat reclined back. Steered driving was associated with a significant (p < .05) increase in the maximum activation of the wrist extensors, serratus anterior, deltoid, and triceps brachii, which are the main force-generating muscle groups for steered driving, compared with straight driving. Moreover, the measured and simulated results of maximum muscle activation were relatively consistent, with the error between them within a 15% margin. In summary, this study explored the relationship between different driving conditions and preferred driving postures from a biomechanical perspective. A combined experimental and simulation approach was adopted to ensure the reliability of the findings. The insights from this study can inform ergonomic considerations for the comfort and health of Chinese drivers with varying physical characteristics.