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

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.

We aimed to provide causal evidence on the contradictory effects of projection-based intelligent assistance systems (IASs) for nine motivational work characteristics (MWCs). IASs are increasingly implemented in assembly to counteract rising cognitive workload due to individualized manufacturing processes. However, how IASs enhance or restrict MWCs is largely unknown. We conducted two studies with experimental vignette methodology. In Study 1 (N₁ = 169 German employees), we manipulated an assembly workplace (with IAS vs. without IAS) and tested whether findings indicating only positive effects of IASs in the support of a simple assembly process can be transferred to more complex assembly processes. In Study 2 (N₂ = 176 German employees), we manipulated again the assembly workplace (with IAS vs. without IAS) and in addition the dynamic of product changes (task rotation after 1 h vs. no task rotation). Analyzing the data with SPSS 27, we found increased feedback from job and information processing and decreased work scheduling, decision-making, and work methods autonomy when working with IAS. In Study 2, we did not find the main or interaction effects of task rotation on MWCs. Our experimental evidence suggests that working with IASs represents a double-edged sword regarding MWCs and that the effect of task rotation is limited. Hence, our results provide vital theoretical implications for a much-needed work design theory that delineates how new technologies shape work design and practical implications for modern assembly.

Artificial intelligence (AI) virtual assistants are rapidly growing, permeating people's daily lives and work. However, some trust and risk issues prevent the acceptance and use of AI virtual assistants by users. Thus, understanding the roles of trust and perceived risk in user acceptance of AI virtual assistants is crucial. This study develops a comprehensive research model based on unified theory of acceptance and use of technology (UTAUT) to explain user acceptance of AI virtual assistants. This model extends UTAUT by adding users' perception of trust and risk. The research model and hypotheses are validated through structural equation modeling with a sample of 926 AI virtual assistant users. Results show that gender is significantly related to behavioral intention to use, education is positively related to trust and behavioral intention to use, and usage experience is positively related to attitude toward using. UTAUT variables, including performance expectancy, effort expectancy, social influence, and facilitating conditions, are positively related to behavioral intention to use AI virtual assistant. Trust and perceived risk respectively have positive and negative effects on attitude toward using and behavioral intention to use AI virtual assistants. Trust and perceived risk play equally important roles in explaining user acceptance of AI virtual assistants. Theoretical and practical implications of the current AI virtual assistant acceptance model and directions for future research are discussed.

Aggressive driving significantly impacts traffic safety, and heavy-duty vehicle drivers are more liable for causing serious crashes. This paper analyzes drivers' aggressive driving behavior from the vehicle type perspective and identifies the influencing factors of aggressive driving behavior through artificial neural network (ANN) and Shapley additive explanations (SHAPs). Using Kaggle's open-source aggressive driving data, we establish an ANN model to identify driving styles, where road conditions, environmental conditions, and vehicle parameters are independent variables and driving style is a dependent variable. The following measurements, including accuracy, recall, precision, and F1 score, are used to evaluate the model's performance, and the neural network got 85.33%, 82.32%, 84.16%, and 0.8308, respectively. To illustrate the influence of independent variables, the SHAP algorithm is used to analyze the model's feature importance. It was found that illumination and weather conditions influenced the model's performance along with the vehicle length. The number of lanes relates to driving style, and there were more aggressive driving behaviors on two-lane roads than on single-lane roads. Besides, heavy-duty vehicle drivers were more likely to drive aggressively in wet road conditions and indulge in aggressive driving behaviors at night. Particularly, drivers of heavy-duty vehicles were more likely to drive aggressively, provided that the vehicle in front was also a heavy-duty vehicle. These findings inform heavy-duty vehicle drivers to reduce aggressive driving behavior. The information is suitable for inclusion in driver education programs, thus improving traffic safety.

Aerial refueling is an extender of air combat capability that has received widespread attention with the development of the military field, particularly the mental workload of the pilot performing the aerial refueling task, as it is the key to the aerial refueling success rate. Therefore, this study analyzes the behavior of receiver aircraft pilots from rendezvous to the separation phase and constructs a mental workload model to improve the aerial refueling success rate. First, the time-channel-action unit network was constructed based on Petri net and multiple resource theory (MRT). Second, the mental workload evaluation model was constructed from three dimensions: conventional resource occupation, additional resource occupation, and time occupation. Finally, a simulated experiment was conducted, and the results showed that the mental workload obtained by the model constructed in this study exhibits a high positive correlation with subjective mental workload, pupil diameter, and blink rate, surpassing the accuracy of the traditional MRT model.