Comprehensive evaluation method for user interface design in nuclear power plant based on mental workload

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.

Soft computing applications in the field of human factors and ergonomics: A review of the past decade of research

The main objectives of this study were to 1) review the literature on the applications of soft computing concepts to the field of human factors and ergonomics (HFE) between 2013 and 2022 and 2) highlight future developments and trends. Multiple soft computing methods and techniques have been investigated for their ability to address various applications in HFE effectively. These techniques include fuzzy logic, artificial neural networks, genetic algorithms, and their combinations. Applications of these methods in HFE have been highlighted in one hundred and four articles selected from 406 papers. The results of this study help address the challenges of complexity, vagueness, and imprecision in human factors and ergonomics research through the application of soft computing methodologies.

Image Entropy-Based Interface Evaluation Method for Nuclear Power Plants

The digital interface is crucial for nuclear plant operators, influencing their decision-making significantly. However, evaluations of these interfaces often overlook users' decision-making performance; lack established standards, typically occurring after the design phase; and are unsuitable for large-scale assessments. Recognizing the vital role of interface information, this paper built on our previous research and proposed a method tailored for nuclear power plant interfaces, utilizing image entropy to evaluate the impact of information on decision-making. A comparative analysis with an experimental evaluation method empirically validated the effectiveness of the proposed method. This research offers a unique decision-making-centric method to interface evaluation, providing a standardized, adaptable framework for various design phases and enabling extensive and rapid evaluations.

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.

Mental workload assessments of aerial photography missions performed by novice unmanned aerial vehicle operators

BACKGROUND

Mental workload is one of the important variables in understanding human performance in drone operation.

OBJECTIVE

To test the effects of gender, age group, flight route, and altitude on the flight performance and mental workload of the novice drone operators.

METHODS

Ten male and 10 female participants without prior drone operating experience joined. They were split into two age groups. After attending a training, the participants operated a drone to perform photo taking missions under flight route and altitude conditions. The weighted NASA task-load-index (TLX), modified Cooper-Harper (MCH) scale, heart rate, and interbeat interval were measured to assess the mental workload of the participants. Flight time to complete the mission was adopted to indicate flight performance.

RESULTS

The effect of age group was significant (p <  0.05) on flight time, weighted TLX score, and MCH score. Flight route and altitude were not significant on the two subjective ratings and two cardiac measures.

CONCLUSION

The flight performance of younger participants was significantly better than that of their older counterpart. The effects of both the flight route and altitude on the perceived mental workload of the drone operators were insignificant. Both the weighted NASA TLX and MCH scales were appropriate in measuring the mental workload of the novice drone operators.

Effects of Noise Exposure and Mental Workload on Physiological Responses during Task Execution

Twelve healthy male students were recruited to investigate the physiological response to different noise exposure and mental workload (MW) conditions, while performing multi-attribute task battery (MATB) tasks. The experiments were conducted under three noise exposure conditions, with different sound pressure levels and sharpness. After adaptation to each noise condition, the participants were required to perform the resting test and the MATB task tests with low, medium, and high MW. The electroencephalogram (EEG), electrocardiogram (ECG), and eye movement data were obtained, during the periods when participants were in the resting and task taking state. The results showed that subjects' physiological responses at rest were unaffected by noise exposure conditions. However, during the execution of MATB tasks, the elevated sound pressure level and increased sharpness were significantly correlated with increased mean pupil diameter and heart rate variability (HRV). These responses suggested that the human body defends itself through physiological regulation when noise causes adverse effects. If the negative effects of noise were more severe, this could damage the body's health and result in a significant drop in task performance. The elevated mental demands led to increased stress on the subjects, which was reflected in a considerable increase in theta relative power. Either high or low MW was related with reduced saccade amplitude and a decrease in weighted task performance, indicating an inverted U-shaped relationship between workload level and work performance.

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 Ergonomic User Interface Design of Calcium Imaging Processing System

An optical brain-machine interface (O-BMI) system using calcium imaging has various advantages such as high resolution, a comprehensive view of large neural populations, abilities such as long-term stable recording, and applicability to freely behaving animals in neuroscience research. The present study developed an ergonomic user interface (UI) design, based on a use scenario for an O-BMI system that can be used for the acquisition and processing of calcium imaging in freely behaving rodents. The UI design was developed in three steps: (1) identification of design and function requirements of users, (2) establishment of a use scenario, and (3) development of a UI prototype. The UI design requirements were identified by a literature review, a benchmark of existing systems, and a focus group interview with five neuroscience researchers. Then, the use scenario was developed for tasks of data acquisition, feature extraction, and neural decoding for offline and online processing by considering the sequences of operations and needs of users. Lastly, a digital prototype incorporating an information architecture, graphic user interfaces, and simulated functions was fabricated. A usability test was conducted with five neuroscientists (work experience = 3.4 ± 1.1 years) and five ergonomic experts (work experience = 3.6 ± 2.7 years) to compare the digital prototypes with four existing systems (Miniscope, nVista, Mosaic, and Suite2p). The usability testing results showed that the ergonomic UI design was significantly preferred to the UI designs of the existing systems by reducing the task completion time by 10.1% to 70.2% on average, the scan path length by 14.4% to 88.7%, and perceived workload by 12.2% to 37.9%, increasing satisfaction by 11.3% to 74.3% in data acquisition and signal-extraction tasks. The present study demonstrates the significance of the user-centered design approach in the development of a system for neuroscience research. Further research is needed to validate the usability test results of the UI prototype as a corresponding real system is implemented.

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.

Enhanced Accuracy for Multiclass Mental Workload Detection Using Long Short-Term Memory for Brain-Computer Interface

Cognitive workload is one of the widely invoked human factors in the areas of human-machine interaction (HMI) and neuroergonomics. The precise assessment of cognitive and mental workload (MWL) is vital and requires accurate neuroimaging to monitor and evaluate the cognitive states of the brain. In this study, we have decoded four classes of MWL using long short-term memory (LSTM) with 89.31% average accuracy for brain-computer interface (BCI). The brain activity signals are acquired using functional near-infrared spectroscopy (fNIRS) from the prefrontal cortex (PFC) region of the brain. We performed a supervised MWL experimentation with four varying MWL levels on 15 participants (both male and female) and 10 trials of each MWL per participant. Real-time four-level MWL states are assessed using fNIRS system, and initial classification is performed using three strong machine learning (ML) techniques, support vector machine (SVM), k-nearest neighbor (k-NN), and artificial neural network (ANN) with obtained average accuracies of 54.33, 54.31, and 69.36%, respectively. In this study, novel deep learning (DL) frameworks are proposed, which utilizes convolutional neural network (CNN) and LSTM with 87.45 and 89.31% average accuracies, respectively, to solve high-dimensional four-level cognitive states classification problem. Statistical analysis, t-test, and one-way F-test (ANOVA) are also performed on accuracies obtained through ML and DL algorithms. Results show that the proposed DL (LSTM and CNN) algorithms significantly improve classification performance as compared with ML (SVM, ANN, and k-NN) algorithms.

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.