Effect of user interface layout on the operators’ mental workload in emergency operating procedures in nuclear power plants

Measuring cognitive workload in the nuclear control room: a review

Despite the substantial literature and human factors guidance, evaluators report challenges in selecting cognitive workload measures for the evaluation of complex human-technology systems. A review of 32 articles found that self-report measures and secondary tasks were systematically sensitive to human-system interface conditions and correlated with physiological measures. Therefore, including a self-report measure of cognitive workload is recommended when evaluating human-system interfaces. Physiological measures were mainly used in method studies, and future research must demonstrate the utility of these measures for human-system evaluation in complex work settings. However, indexes of physiological measures showed promise for cognitive workload assessment. The review revealed a limited focus on the measurement of excessive cognitive workload, although this is a key topic in nuclear process control. To support human-system evaluation of adequate cognitive workload, future research on behavioural measures may be useful in the identification and analysis of underload and overload.

Physiological measures of operators' mental state in supervisory process control tasks: a scoping review

Physiological measures are often used to assess the mental state of human operators in supervisory process control tasks. However, the diversity of research approaches creates a heterogeneous landscape of empirical evidence. To map existing evidence and provide guidance to researchers and practitioners, this paper systematically reviews 109 empirical studies that report relationships between peripheral nervous system measures and mental state dimensions (e.g. mental workload, mental fatigue, stress, and vigilance) of interest. Ocular and electrocardiac measures were the most prominent measures across application fields. Most studies sought to validate such measures for reliable assessments of cognitive task demands and time on task, with measures of pupil size receiving the most empirical support. In comparison, less research examined the utility of physiological measures in predicting human task performance. This approach is discussed as an opportunity to focus on operators' individual response to cognitive task demands and to advance the state of research.

Design guidelines for limiting and eliminating virtual reality-induced symptoms and effects at work: a comprehensive, factor-oriented review

Virtual reality (VR) can induce side effects known as virtual reality-induced symptoms and effects (VRISE). To address this concern, we identify a literature-based listing of these factors thought to influence VRISE with a focus on office work use. Using those, we recommend guidelines for VRISE amelioration intended for virtual environment creators and users. We identify five VRISE risks, focusing on short-term symptoms with their short-term effects. Three overall factor categories are considered: individual, hardware, and software. Over 90 factors may influence VRISE frequency and severity. We identify guidelines for each factor to help reduce VR side effects. To better reflect our confidence in those guidelines, we graded each with a level of evidence rating. Common factors occasionally influence different forms of VRISE. This can lead to confusion in the literature. General guidelines for using VR at work involve worker adaptation, such as limiting immersion times to between 20 and 30 min. These regimens involve taking regular breaks. Extra care is required for workers with special needs, neurodiversity, and gerontechnological concerns. In addition to following our guidelines, stakeholders should be aware that current head-mounted displays and virtual environments can continue to induce VRISE. While no single existing method fully alleviates VRISE, workers' health and safety must be monitored and safeguarded when VR is used at work.

Domain- and task-analytic workload (DTAW) method: a methodology for predicting mental workload during severe accidents in nuclear power plants

Excessive mental workload reduces operators' performance and threatens the safety of nuclear power plants (NPPs) in severe accident management (SAM). Given the lack of suitable mental workload measurement methods for SAM tasks, we proposed a Domain- and Task-Analytic Workload (DTAW) method to predict SAM workload. The DTAW method is developed in three stages: scenario construction based on work domain analysis, task analysis, and workload estimation with eight workload components scored through task-analytic and projective methods. To demonstrate its utility, we applied the method to construct two SAM scenarios and predict the mental workload demand of operators in these scenarios as compared to two design basis accident scenarios. With statistical analysis, the DTAW method can predict the overall subjective workload rated by NPP operators, be used to identify high-load tasks, cluster tasks with similar workload patterns, and provide direct implications for improving SAM strategies and supporting systems.Practitioner summary: To predict mental workload in severe accident management (SAM) scenarios in nuclear power plants, we proposed an analytic method and applied it to estimate mental workload in two SAM scenarios and two design basis accident (DBA) scenarios. We found that the workload pattern in SAM scenarios is different from that in DBA scenarios.

Effects of interface layout design on mobile learning efficiency: a comparison of interface layouts for mobile learning platform

Purpose

The purpose of this study is to explore the impact of mobile learning platforms on users' study efficiency and develop cognitive indicators to evaluate users' study efficiency on mobile learning platforms.

Design/methodology/approach

Layout style was the only independent factor that was investigated. A between-group experimental design was employed. Eye movement data were recorded during the experiment, following which participants were asked to complete an after-scenario questionnaire. This study evaluated the usability of the proposed new design using both subjective and objective data. The computer system usability questionnaire V3 (CSUQ) was used to measure subjective data. For the eye-tracking measure, gaze entropy, the proportion of fixation count and duration of each AOI were calculated. Gaze entropy reflects the complexity of information organization. Fixation counts and AOI duration represent the difficulty of information processing and attention distribution, respectively during the task.

Findings

The results indicated that interface layout presents significant effects on user's learning efficiency, usability and cognitive load. Sequential layout improved efficiency and satisfaction among participants and reduced information complexity. The results provided useful insights for designers whose goal is to improve user's learning efficiency under mobile learning scheme.

Originality/value

This study investigated the effects of interface layout on usability, user performance and cognitive load using subjective ratings and eye-tracking technology. Gaze entropy was used to measure the complexity of information organized by the interface design. Fixation count and duration proportion were used to identify the difficulty of information processing and distinguish users' distribution of cognitive resources. The results indicated that a vertical layout panel design was more efficient than a horizontal layout panel design. The design implications of the eye tracking indicators and research results were then summarized. This study is expected to encourage designers to optimize their design proposals using eye tracking testing.

Performance Index Based on Predicted Auditory Reaction Time Analysis for the Evaluation of Human-Machine Interface in Flight Control

With the rapid development of complex equipment, such as airplanes, the appropriate design of the human-machine interface is often upgraded, thus emerged many methods to evaluate whether such an upgrade is effective. Most researches focus on the time accumulation effect of the human state during the interaction to evaluate the interface. However, in the aviation application, the performance of the pilot's instantaneous reactions also reveals the design efficiency of the interface, since the difficulty level of obtaining the useful information would severely influence the reaction time in some voice command tasks or emergency situations. Besides, there are so many flight scenarios that are impossible to be simulated in experiments or in a laboratory environment. Also, voice commands are too numerous to be traversed simulated. This paper introduced predicted auditory reaction time as an index to evaluate human-machine interface design. The proposed method has two advantages. On the one hand, it effectively measures the pilot's auditory reaction time based on the eye movement tracking; thus, the data can be taken in flight task scenarios, and the experiment would not cause interference to the subjects. On the other hand, a prediction model is proposed, in which the pilot's reaction time under more generalized voice command can be estimated based on a small-size sample set.

Development of an Eye Responses-Based Mental Workload Evaluation Method

This study proposed an eye responses-based mental workload (E-MWL) evaluation method in nuclear power plants (NPPs) when performing the task via a user interface control. The fuzzy theory was used to combine four eye response indices using the entropy weight method. Then, the E-MWL method was validated through experiments by comparison with the NASA-TLX rating and performance measures indices in two different tasks of the State Oriented Procedure (SOP) in NPP. The correlation analysis results between the NASA-TLX and eye response indices showed that four eye response indices used in this study were correlated significantly with the NASA-TLX, indicating that these indices may develop the E-MWL method. The E-MWL score results indicated that it is highly correlated with NASA-TLX and performance measures indices in two different tasks of SOP in NPP. This has proved that E-MWL is an objective method suitable for evaluating and predicting human mental workload (MWL) for interface control task in NPPs.

The Validity of Physiological Measures to Identify Differences in Intrinsic Cognitive Load

A sample of 33 experiments was extracted from the Web-of-Science database over a 5-year period (2016-2020) that used physiological measures to measure intrinsic cognitive load. Only studies that required participants to solve tasks of varying complexities using a within-subjects design were included. The sample identified a number of different physiological measures obtained by recording signals from four main body categories (heart and lungs, eyes, skin, and brain), as well as subjective measures. The overall validity of the measures was assessed by examining construct validity and sensitivity. It was found that the vast majority of physiological measures had some level of validity, but varied considerably in sensitivity to detect subtle changes in intrinsic cognitive load. Validity was also influenced by the type of task. Eye-measures were found to be the most sensitive followed by the heart and lungs, skin, and brain. However, subjective measures had the highest levels of validity. It is concluded that a combination of physiological and subjective measures is most effective in detecting changes in intrinsic cognitive load.

Exploring the Role of Visual Design in Digital Public Health Safety Education

In this research, the positive role of interface visual design in digital safety education was verified taking COVID-19 prevention and control knowledge as the content of public health safety education, where interface emotion (positive, negative, and neutral) and interface layout (waterfall typed and juxtaposition typed) were regarded as independent variables, and readers’ understanding, course evaluation and system usability score were dependent variables. As revealed in the results of a 3 × 2 two-factor experiment in which 252 college students participated: first, different interface emotion can cause significantly different understanding, where negative emotion has the best learning transfer effect; second, due to the difference in interface emotion, participants may give certain courses significantly different evaluation scores, while positive emotional interface contributes to the obviously high scores of three course-evaluation items, “appeal of the lesson”, “enjoyment of the lesson” and “interface quality”; third, significantly different system usability can be caused by different interface layout, where waterfall-type layout enjoys higher appraisal from users; fourth, interface emotion and interface layout have a similar interactive effects in terms of “effort of the lesson” and “interface quality”, where waterfall-type layout is favored in terms of positive emotional interface, and juxtaposition-type layout is more advantageous in terms of negative emotional interface. These results are of vital significance for interface design and safety education. Further, the visual design method for interface emotion and interface layout were analyzed to determine the most suitable design principles so as to improve the effect of digital public health safety education and provide constructive ideas for fighting against COVID-19 at the educational level.

Reference values and subscale patterns for the task load index (TLX): a meta-analytic review

The Task Load Index (TLX) is the predominant instrument for self-reporting workload. On the basis of a meta-analytic review of 556 studies, this paper supplies reference values for TLX and its six subscales across domains, technologies, regions, and real-life/lab settings. Across domains, TLX spans mean values from 35 for leisure to 56 for manual labour. TLX tends to be driven upward by the subscales of mental demand and effort and downward by the subscales of physical demand and frustration. For technologies, handheld devices are associated with lower TLX, possibly because they are simpler and more task-specific. TLX also varies across regions in that it is higher for studies in Asia than in Europe and North America. This variation is only partly explained by co-variation in domains. Furthermore, TLX is higher and its subscales more inter-correlated when it is studied in real-life rather than lab settings. Practitioner summary: Practitioners can use the reference values supplied in this paper to benchmark their TLX measurements against those from the corpus of TLX research. Furthermore, the reported subscale patterns add to the diagnostic power of the TLX instrument. Abbreviations: TLX: task load index; MD: mental demand; PD: physical demand; TD: temporal demand; EF: effort; PE: performance; FR: frustration.

Optimal Design of Virtual Reality Visualization Interface Based on Kansei Engineering Image Space Research

To effectively organize design elements in virtual reality (VR) scene design and provide evaluation methods for the design process, we built a user image space cognitive model. This involved perceptual engineering methods and optimization of the VR interface. First, we studied the coupling of user cognition and design features in the VR system via the Kansei Engineering (KE) method. The quantitative theory I and KE model regression analysis were used to analyze the design elements of the VR system’s human–computer interaction interface. Combined with the complex network method, we summarized the relationship between design features and analyzed the important design features that affect users’ perceptual imagery. Then, based on the characteristics of machine learning, we used a convolutional neural network (CNN) to predict and analyze the user’s perceptual imagery in the VR system, to provide assistance for the design optimization of the VR system design. Finally, we verified the validity and feasibility of the solution by combining it with the human–machine interface design of the VR system. We conducted a feasibility analysis of the KE model, in which the similarity between the multivariate regression analysis of the VR intention space and the experimental test was approximately 97% and the error was very small; thus, the VR intention space model was well correlated. The Mean Square Error (MSE) of the convolutional neural network (CNN) prediction model was calculated with a measured value of 0.0074, and the MSE value was less than 0.01. The results show that this method can improve the effectiveness and feasibility of the design scheme. Designers use important design feature elements to assist in VR system optimization design and use CNN machine learning methods to predict user image values in VR systems and improve the design efficiency. Facing the same design task requirements in VR system interfaces, the traditional design scheme was compared with the scheme optimized by this method. The results showed that the design scheme optimized by this method better fits the user’s perceptual imagery index, and thus the user’s task operation experience was better.

Research on Optimization Method of VR Task Scenario Resources Driven by User Cognitive Needs

Research was performed in order to improve the efficiency of a user’s access to information and the interactive experience of task selection in a virtual reality (VR) system, reduce the level of a user’s cognitive load, and improve the efficiency of designers in building a VR system. On the basis of user behavior cognition-system resource mapping, a task scenario resource optimization method for VR system based on quality function deployment-convolution neural network (QFD-CNN) was proposed. Firstly, under the guidance of user behavior cognition, the characteristics of multi-channel information resources in a VR system were analyzed, and the correlation matrix of the VR system scenario resource characteristics was constructed based on the design criteria of human–computer interaction, cognition, and low-load demand. Secondly, analytic hierarchy process (AHP)-QFD combined with evaluation matrix is used to output the priority ranking of VR system resource characteristics. Then, the VR system task scenario cognitive load experiment is carried out on users, and the CNN input set and output set data are collected through the experiment, in order to build a CNN system and predict the user cognitive load and satisfaction in the human–computer interaction in the VR system. Finally, combined with the task information interface of a VR system in a smart city, the application research of the system resource feature optimization method under multi-channel cognition is carried out. The results show that the test coefficient CR value of the AHP-QFD model based on cognitive load is less than 0.1, and the MSE of CNN prediction model network is 0.004247, which proves the effectiveness of this model. According to the requirements of the same design task in a VR system, by comparing the scheme formed by the traditional design process with the scheme optimized by the method in this paper, the results show that the user has a lower cognitive load and better task operation experience when interacting with the latter scheme, so the optimization method studied in this paper can provide a reference for the system construction of virtual reality.

Evaluating the International Competitiveness of Vietnam Wood Processing Industry by Combining the Variation Coefficient and the Entropy Method

Indicators measuring industrial international competitiveness are being continuously improved. However, so far, there is no unified perfect indicator to measure the level of international competitiveness of the industry. Based on the market share index (MS), trade competitiveness index (TC), revealed comparative advantage index (RCA), and relative trade advantage index (RTA), we constructed a comprehensive international competitiveness index by combining the variation coefficient and the entropy method. This study aims to compare and evaluate the international competitiveness of the wood processing industry (ICWPI) in Vietnam using a comprehensive international competitiveness index. The data is collected from the top 22 countries and the total import and export volume of the wood processing industry from the repository of official international trade statistics (UN Comtrade) database for 2001–2017. The results found that it is more accurate to use the combined variation coefficient and the entropy method to evaluate the international competitiveness of the wood processing industry, compared to using only a single index. The growth rate of international competitiveness of Vietnam increased rapidly from 2001 to 2007 but slowed from 2008 to 2017. Vietnam has the advantages of natural resources, low labor costs and favorable geographical location. However, the low productivity gains and added industry value have led to a gradual decline in the international competitiveness growth rate of Vietnam's wood processing industry.

A Systematic Review of Physiological Measures of Mental Workload

Mental workload (MWL) can affect human performance and is considered critical in the design and evaluation of complex human-machine systems. While numerous physiological measures are used to assess MWL, there appears no consensus on their validity as effective agents of MWL. This study was conducted to provide a comprehensive understanding of the use of physiological measures of MWL and to synthesize empirical evidence on the validity of the measures to discriminate changes in MWL. A systematical literature search was conducted with four electronic databases for empirical studies measuring MWL with physiological measures. Ninety-one studies were included for analysis. We identified 78 physiological measures, which were distributed in cardiovascular, eye movement, electroencephalogram (EEG), respiration, electromyogram (EMG) and skin categories. Cardiovascular, eye movement and EEG measures were the most widely used across varied research domains, with 76%, 66%, and 71% of times reported a significant association with MWL, respectively. While most physiological measures were found to be able to discriminate changes in MWL, they were not universally valid in all task scenarios. The use of physiological measures and their validity for MWL assessment also varied across different research domains. Our study offers insights into the understanding and selection of appropriate physiological measures for MWL assessment in varied human-machine systems.