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

Studies have focused on the negative effects of anger on driving performance, but insufficient research has addressed intervention methods to reduce these emotional effects. This research investigated how music mitigates the deterioration of driving performance associated with angry emotions in a simulated car-following task. Forty-three licensed drivers participated in this study, and they were randomly separated into two groups: an intervention group and a control group. First, all the participants completed a car-following task involving neutral arousal. Then, both groups completed the car-following task after the anger arousal task. The intervention group drove while listening to relaxing music, but the control group did not. Driving performance and electrocardiographic data were recorded. The results showed that participants who listened to relaxing music had significantly shorter braking reaction times and greater heart rate variability (HRV) than did those who did not listen to music. Relaxing music can reduce driving anger and improve driving behavior.

In today's interconnected global economy, maritime trade is a pillar of prosperity, yet maritime accidents loom as a formidable challenge. The intricate nature of these accidents, coupled with rapid technological advancements, necessitates the evolution of systematic analysis methods. Conventional systemic approaches, while valuable, struggle to encapsulate the intricate web of mutual and dynamic dependencies inherent in these incidents. Furthermore, the call for more quantitative support in decision-making and the ability to account for emergent factors has become increasingly imperative. This study aims to analyze maritime accidents by introducing a quantitative and dynamic model. The endeavour begins with establishing an extended Accident Map-based model, a robust framework that unveils a sophisticated accident causation model. This preliminary action establishes the groundwork for integrating an innovative Spherical Fuzzy Set, navigating the complex landscape of knowledge acquisition. The subsequent phase charts a transformative course by mapping the model onto a dynamic Bayesian Network to conduct a forward and backward analysis. The essence of the model lies in its dynamic nature, allowing for real-time updates that reflect the evolving maritime accidents risk factors. The approach is validated through a partial benchmark exercise, a reality check, an independent peer review, and a sensitivity analysis. The model can explore emerging contributing factors, reduce uncertainty, and consider relationships between factors that yield designing more effective safety measures.

To improve the interaction between drivers and the in-vehicle information system (IVIS), various intelligent agents, such as robot agents, virtual agents, and voice-only agents, have been integrated into vehicles. However, it is not yet clear which type of in-vehicle agent is best suited to the driving context. This study aims to investigate the effect of in-vehicle agent embodiment on drivers' perceived usability and cognitive workload. In a within-subject simulated driving experiment, 22 participants interacted with three different in-vehicle agents (smartphone agent, robot agent, and virtual agent). Functional near-infrared spectroscopy and electrocardiogram (ECG) were used to record prefrontal cortex activation and electrical changes associated with cardiac activity during simulated driving, respectively. The results show that the smartphone agent had the lowest perceived usability scores, oxygenated hemoglobin concentration variation (ΔHbO), and maximum ECG signal variation compared to baseline. There were no statistical differences in cognitive workload, perceived usability scores, brain area activation, and ECG signals between the robot agent and the virtual agent. The research findings demonstrate the positive effects of the anthropomorphic appearance of in-vehicle agents on perceived usability and contribute to improving the design of in-vehicle intelligent agents.

Gas accidents represent a crucial domain of coal mine safety research, as they result in substantial property damage, environmental pollution, and even loss of life compared to other types of accidents. Particularly, human factors play a significant role in the majority of mining accidents. The objective of this paper is to enhance the quality of coal mine safety management, minimize the occurrence of adverse human factors in gas accidents, and analyze the factors influencing coal mine gas accidents using the Human Factors Analysis and Classification System (HFACS). To commence, this paper has devised a human factor influence index system based on the enhanced HFACS for coal mine gas accidents. Subsequently, the Decision-making Trial and Evaluation Laboratory (DEMATEL) method has been employed to quantitatively delineate the causal relationships among these factors. Lastly, this paper utilized the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) comprehensive evaluation method to evaluate the importance of factors influencing coal mine gas accidents. The research findings indicate that through the utilization of the DEMATEL methodology for centrality and causal relationship calculations, the centrality and causality values associated with poor organizational management emerge as the foremost among all factors. This underscores the pivotal role that poor organizational management plays in the human factors influencing coal mine gas accidents. Furthermore, a meticulous examination using TOPSIS identified the top five indicators of influence capability: cognitive errors > habitual violations > operational management > management process > resource management. The analysis of human factors in coal mine gas accidents can provide enhanced theoretical support for the management of production safety in coal mines, as well as the prevention of gas accidents.

The past two decades have witnessed dramatic advancement in computer-aided design (CAD). However, development of human–computer interfaces (HCI) for CAD have not kept up with these advances. Windows, Icons, Menus, Pointer (WIMP) is still the mainly used interface for CAD applications which limits the naturalness and intuitiveness of the CAD modeling process. As a novel interface, Brain–computer interfaces (BCIs) have great potential in the application of CAD modeling. Utilizing BCIs, the user can create CAD models just by thinking about it in principle, because BCIs provide an end-to-end interaction channel between users and CAD models. However, current related studies are mainly limited to the existing BCIs paradigms, while ignoring the relationship between electroencephalogram (EEG) signals and CAD models, which largely increases the cognitive load on the users. In this study, we aimed to explore the potential of using BCI to create CAD models directly independent of the classical BCIs paradigms. For this purpose, EEG signals evoked by six basic CAD models (i.e., point, square, trapezoid, line, triangle, and circle) were collected from 28 participants. After preprocessing and sub-trial principal components analysis (st-PCA) of recorded data, the peak, mean and time-frequency energy features were extracted from EEG signals. By applying the one-way repeated measures analysis of variance, we demonstrated that there were significant differences among these EEG features evoked by different CAD models. These features from EEG electrode channels ranked by mutual information were then used to train a discriminant classifier of genetic algorithm-based support vector machine. The empirical result showed that this classifier can discriminate the CAD models with an average accuracy of about 72%, which turns out that EEG based model generation is feasible, and provides the technical and theoretical basis for building a novel BCI for CAD modeling.

Owing to the increasing amount of information presented in the cockpit, the visual and hearing channels are unable to adequately transmit information, which may increase the mental load on pilots. This study explores the benefits of multimodal alarms under high and low residual capacities during take-off in civil aircrafts in a quasi-experimental study. The performance of two modes of multimodal (visual and auditory [VA], and visual, auditory, and tactile [VAT]) alarms were tested. The results showed that the VAT alarms were superior to the VA alarms in terms of choice response times (CRTs) when the participants were exposed to low residual capacities of vision and hearing. However, this effect was not observed when the participants had high residual capacities for vision and hearing. Thus, we considered that an additional tactile alarm could play a significant role in the CRTs when VA resources were consumed. There was no significant difference in the number of response errors between the three multimodal alarm modes. This study provides a key comparison of the two modes of multimodal alarms, indicating that VAT alarms are ideal for use in alarm design strategies for next-generation civil cockpits.

Human–robot collaboration has been widely used in postdisaster investigation and rescue. Human–robot team training is a good way to improve the team rescue efficiency and safety; two common training methods, namely, procedural training and cross-training, are explored in this study. Currently, relatively few studies have explored the impact of cross-training on human–robot collaboration in rescue tasks. Cross-training will be novel to most rescuers and as such, an evaluation of cross-training in comparison with more conventional procedural training is warranted. This study investigated the effects of these two training methods on rescue performance, situation awareness and workload. Forty-two participants completed a path-planning and a photo-taking task in an unfamiliar simulated postdisaster environment. The rescue performance results showed that cross-training method had significant advantages over procedural training for human–robot collaborative rescue tasks. During the training process, compared with procedural training, participants were more likely to achieve excellent photo-taking performance after cross-training; after training, the length of the route planned by the cross-training group was significantly shorter than that of the procedural-training group. In addition, procedural-training marginal significantly increased the emotion demand, which proves that cross-training can well control the emotions of the operators and make them more involved in the rescue task. The study also found that arousal level increased significantly after the first cross-training session, and decreased to the same level as procedural training after multiple sessions. These results contribute to the application of cross-training in human–robot collaborative rescue teams.

Collaborative robots (cobots) are an essential component of intelligent manufacturing. However, employees working alongside them have negative attitudes toward cobots that assist humans' work. To address this industrial human–robot interaction problem, this study adopted the idea of cognitive ergonomics research, invited 323 participants, and conducted an empirical study using an experimental vignette methodology. This study found that (1) perceived intelligence plays a mediating role in the relationship between cobots anthropomorphism and negative attitudes toward cobots; (2) perceived intelligence and perceived threat play a serial mediating role in the relationship between cobots anthropomorphism and negative attitudes toward cobots; (3) robot use self-efficacy plays a moderating role in the relationship between perceived threat and negative attitudes toward cobots. The results provide a mechanistic explanation and related measures to eliminate the negative attitudes toward cobots.

This study aimed to examine the influence of elbow and forearm postures, as well as sex, on the perception of grip force in a sample of individuals without any known health conditions. A total of 21 healthy participants (10 women and 11 men) from college were included and completed a force reproducibility assignment with four elbow and forearm positions (full pronation, supination, and extension, and at 90° of flexion) at three force levels (10%, 30%, and 50% of maximal voluntary isometric contraction [MVIC]). Our results show that participants were more sensitive in detecting variations in their grip force when their elbow was in full supination (14.1 ± 8.5% MVIC, p < .05) and full extension (13.8 ± 10.1% MVIC, p < .01) than when it was at 90° of flexion (19.9 ± 20.1% MVIC). The normalized absolute error exhibited comparable patterns among both male and female participants. Specifically, when the working range of the muscles increased (as indicated by higher maximum voluntary isometric contraction values in males), the accuracy decreased (as reflected by the more significant absolute error in men). Moreover, men exhibited a greater degree of both constant and variable error than women. Recent research indicates that the prevalence of musculoskeletal disorders is higher in women than in males. The results we obtained may contribute to developing strategies to reduce injury risk.