رؤى - Neuroergonomics - # Performance Evaluation of Nuclear Power Plant Operators under Heat Stress

Evaluating Nuclear Power Plant Operators' Performance under Heat Stress using ECG Time-Frequency Spectrums and fNIRS Prefrontal Cortex Network

Q: How can the proposed neuroergonomics model be extended to evaluate the performance of operators in other high-risk industries beyond nuclear power plants?

The proposed neuroergonomics model, which combines ECG time-frequency spectrums and fNIRS prefrontal cortex networks, can be extended to evaluate the performance of operators in various high-risk industries beyond nuclear power plants by adapting the data acquisition and preprocessing techniques to suit the specific requirements of those industries. Here are some ways to extend the model: Customized Data Acquisition: Tailoring the data acquisition process to capture relevant physiological signals specific to the industry of interest. For example, in the aviation industry, additional sensors like eye-tracking devices could be incorporated to monitor eye movements and cognitive workload. Domain-Specific Handcrafted Features: Introducing industry-specific handcrafted features derived from physiological signals to enhance the model's ability to capture nuances in operator performance. For instance, in the healthcare sector, incorporating heart rate variability metrics could provide insights into stress levels and cognitive load. Multi-Modal Data Fusion: Integrating data from multiple sources such as EEG, EMG, or skin conductance alongside ECG and fNIRS to create a more comprehensive model that considers a broader range of physiological responses in high-stress environments. Industry-Specific Performance Metrics: Developing performance evaluation metrics tailored to the unique demands of different industries. For example, in emergency response scenarios, metrics related to decision-making under time pressure could be crucial. Validation in Diverse Settings: Testing the model in diverse high-risk industry settings to ensure its generalizability and effectiveness across different operational contexts.

Q: How can the potential limitations of using only ECG and fNIRS signals be addressed, and how could the model be further improved by incorporating additional physiological or neuroimaging modalities?

The limitations of using only ECG and fNIRS signals in the proposed model can be addressed by incorporating additional physiological or neuroimaging modalities to provide a more comprehensive assessment of operator performance. Here's how the model could be improved: Incorporating EEG Data: EEG signals can offer insights into brain activity patterns, cognitive workload, and mental states that complement the information provided by ECG and fNIRS. Integrating EEG data can enhance the model's ability to capture cognitive processes and mental workload. Adding EMG Signals: EMG signals can provide information about muscle activity and fatigue levels, offering a more holistic view of the operator's physiological responses during high-stress situations. Including Eye-Tracking Data: Eye-tracking technology can reveal visual attention patterns, gaze behavior, and task engagement, which are crucial factors in assessing operator performance in various industries. Utilizing Respiration Monitoring: Monitoring respiration patterns can offer insights into stress levels, emotional states, and physiological arousal, complementing the information obtained from ECG and fNIRS signals. Integrating Functional MRI (fMRI): Incorporating fMRI data can provide detailed information about brain activation patterns and neural connectivity, offering a deeper understanding of cognitive processes and decision-making in high-risk environments. By incorporating additional physiological and neuroimaging modalities, the model can capture a more comprehensive range of physiological responses and cognitive functions, leading to a more robust evaluation of operator performance in high-risk industries.

المفاهيم الأساسية

A neuroergonomics model is proposed to effectively evaluate the performance of nuclear power plant operators under heat stress by leveraging ECG time-frequency spectrums and fNIRS prefrontal cortex network.

الملخص

The study aimed to develop a neuroergonomics model to evaluate the performance of nuclear power plant (NPP) operators under heat stress. The model utilized electrocardiogram (ECG) time-frequency spectrums and functional near-infrared spectroscopy (fNIRS) prefrontal cortex (PFC) network to extract discriminative features.

Key highlights:

Operators' performance was categorized into three levels based on their performance in major tasks, situation awareness, workload, and working memory under different heat stress conditions.
ECG time-frequency spectrums were obtained using short-time Fourier transform (STFT) and fed into a 4-layer convolutional neural network (CNN) backbone.
fNIRS PFC network was constructed with activation betas as node features and functional connectivity as edge features, and then embedded using a 2-layer graph attention network (GAT) backbone.
The CNN-learned features from ECG spectrums, the GAT-learned features from fNIRS PFC network, and the handcrafted ECG and fNIRS features were fused to achieve the final classification.
The proposed CNN-GAT fusion model outperformed other variants, achieving 81.82% accuracy and 0.90 AUC in the three-class classification task.
The results demonstrate that the small-world nature of the brain network can be effectively captured by GAT, leading to better discriminative feature extraction compared to simple non-linear transformation.
The multi-physiological fusion model shows high ecological validity in detecting abnormal states of operators under extreme heat stress, providing a potential neuroergonomics application for industry 5.0 scenarios.

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arxiv.org

الإحصائيات

The ECG time-frequency spectrums were obtained using short-time Fourier transform (STFT) on the filtered ECG signals.
The fNIRS PFC network was constructed with activation betas in channels as node features and functional connectivity between channels as edge features.

اقتباسات

"The model fused a convolutional neural network (CNN) backbone and a graph attention network (GAT) backbone to extract discriminative features from ECG time-frequency spectrums and fNIRS prefrontal cortex (PFC) network respectively with deeper neuroscience domain knowledge, and eventually achieved 0.90 AUC."
"Inspired by the small-world nature of the brain network, the fNIRS PFC network was organized as an undirected graph and embedded by GAT. It is proven to perform better in information aggregation and delivery compared to a simple non-linear transformation."

الرؤى الأساسية المستخلصة من

A neuroergonomics model to evaluating nuclear power plants operators' performance under heat stress driven by ECG time-frequency spectrums and fNIRS prefrontal cortex network

by Yan Zhang,Mi... في arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02439.pdf

A neuroergonomics model to evaluating nuclear power plants operators' performance under heat stress driven by ECG time-frequency spectrums and fNIRS prefrontal cortex network

استفسارات أعمق

How can the proposed neuroergonomics model be extended to evaluate the performance of operators in other high-risk industries beyond nuclear power plants?

The proposed neuroergonomics model, which combines ECG time-frequency spectrums and fNIRS prefrontal cortex networks, can be extended to evaluate the performance of operators in various high-risk industries beyond nuclear power plants by adapting the data acquisition and preprocessing techniques to suit the specific requirements of those industries. Here are some ways to extend the model:

Customized Data Acquisition: Tailoring the data acquisition process to capture relevant physiological signals specific to the industry of interest. For example, in the aviation industry, additional sensors like eye-tracking devices could be incorporated to monitor eye movements and cognitive workload.

Domain-Specific Handcrafted Features: Introducing industry-specific handcrafted features derived from physiological signals to enhance the model's ability to capture nuances in operator performance. For instance, in the healthcare sector, incorporating heart rate variability metrics could provide insights into stress levels and cognitive load.

Multi-Modal Data Fusion: Integrating data from multiple sources such as EEG, EMG, or skin conductance alongside ECG and fNIRS to create a more comprehensive model that considers a broader range of physiological responses in high-stress environments.

Industry-Specific Performance Metrics: Developing performance evaluation metrics tailored to the unique demands of different industries. For example, in emergency response scenarios, metrics related to decision-making under time pressure could be crucial.

Validation in Diverse Settings: Testing the model in diverse high-risk industry settings to ensure its generalizability and effectiveness across different operational contexts.

How can the potential limitations of using only ECG and fNIRS signals be addressed, and how could the model be further improved by incorporating additional physiological or neuroimaging modalities?

The limitations of using only ECG and fNIRS signals in the proposed model can be addressed by incorporating additional physiological or neuroimaging modalities to provide a more comprehensive assessment of operator performance. Here's how the model could be improved:

Incorporating EEG Data: EEG signals can offer insights into brain activity patterns, cognitive workload, and mental states that complement the information provided by ECG and fNIRS. Integrating EEG data can enhance the model's ability to capture cognitive processes and mental workload.

Adding EMG Signals: EMG signals can provide information about muscle activity and fatigue levels, offering a more holistic view of the operator's physiological responses during high-stress situations.

Including Eye-Tracking Data: Eye-tracking technology can reveal visual attention patterns, gaze behavior, and task engagement, which are crucial factors in assessing operator performance in various industries.

Utilizing Respiration Monitoring: Monitoring respiration patterns can offer insights into stress levels, emotional states, and physiological arousal, complementing the information obtained from ECG and fNIRS signals.

Integrating Functional MRI (fMRI): Incorporating fMRI data can provide detailed information about brain activation patterns and neural connectivity, offering a deeper understanding of cognitive processes and decision-making in high-risk environments.

By incorporating additional physiological and neuroimaging modalities, the model can capture a more comprehensive range of physiological responses and cognitive functions, leading to a more robust evaluation of operator performance in high-risk industries.

Given the importance of human-cybernetics systems in Industry 5.0, how can the insights from this study contribute to the broader development of human-centered solutions in the industrial context?

The insights from this study can significantly contribute to the development of human-centered solutions in the industrial context, particularly in the context of Industry 5.0. Here's how these insights can be leveraged:

Enhanced Operator Monitoring: By utilizing advanced physiological monitoring techniques like ECG and fNIRS, industries can implement real-time monitoring systems to assess operator performance, cognitive workload, and stress levels. This can lead to proactive interventions to optimize human-machine interactions and prevent errors.

Personalized Training Programs: The model's ability to evaluate operator performance under extreme conditions can inform the development of personalized training programs tailored to individual physiological responses and cognitive capabilities. This can enhance skill development and decision-making in high-risk environments.

Optimized Human-Machine Interfaces: Insights from the study can guide the design of human-machine interfaces that adapt to operators' physiological states in real time. By integrating feedback from ECG and fNIRS signals, interfaces can adjust complexity, information flow, and task allocation to optimize performance and reduce cognitive load.

Safety and Emergency Response: The model's capability to detect abnormal states and cognitive impairments in operators can be instrumental in enhancing safety protocols and emergency response strategies in high-risk industries. Early identification of performance decrements can trigger automated safety measures or intervention protocols.

Continuous Improvement: By implementing the neuroergonomics model in Industry 5.0 settings, organizations can establish a culture of continuous improvement based on real-time physiological feedback. This iterative process can lead to the optimization of human-cybernetics systems, increased operational efficiency, and enhanced safety standards.

Overall, the insights from this study can pave the way for the development of human-centered solutions that prioritize operator well-being, performance optimization, and safety in the evolving landscape of Industry 5.0.