A Review of Convolutional Recurrent Neural Network Approaches
Volume: 14 - Issue: 03 - Date: 01-03-2025
Approved ISSN: 2278-1412
Published Id: IJAECESTU442 | Page No.: 2278 -1412
Author: Kratika Saxena
Co- Author: Indu Shrivastava,Nitya Khare,Swati Khanve
Abstract:-Fatigue is a critical factor affecting cognitive performance, decision-making, and overall well-being,
particularly in high-risk domains such as transportation, healthcare, aviation, and industrial operations.
Accurately predicting fatigue-related mental states is essential for preventing accidents and optimizing
human efficiency. Recent advancements in deep learning, particularly Convolutional Recurrent Neural
Networks (CRNNs), have shown significant promise in analyzing EEG signals for fatigue detection.
CRNNs combine Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural
Networks (RNNs) for sequential pattern analysis, making them well-suited for identifying fatigue
transitions over time.
This review provides a comprehensive analysis of CRNN-based approaches for fatigue prediction,
examining feature extraction techniques, preprocessing methods, model architectures, and performance
evaluation metrics. We discuss various datasets used for fatigue detection, highlighting their advantages
and limitations. Additionally, we compare CRNN models with traditional machine learning methods, such
as Support Vector Machines (SVM), Random Forest (RF), and standard CNN-LSTM hybrids,
demonstrating the superiority of CRNNs in capturing both spatial and temporal dependencies in EEG
signals.
The paper also explores the impact of attention mechanisms, feature selection strategies, and multimodal
data integration in enhancing model accuracy. Finally, we address the challenges of real-time fatigue
prediction, including cross-subject variability, data imbalance, and computational efficiency. Future
research directions focus on integrating deep reinforcement learning, explainable AI (XAI), and edge
computing for developing robust, real-time fatigue monitoring systems. By reviewing the latest
advancements in CRNN-based fatigue prediction, this study aims to guide future research toward more
accurate, interpretable, and scalable mental state monitoring solutions
Key Words:-Fatigue Prediction, Electroencephalography (EEG), Convolutional Recurrent Neural Network (CRNN), Deep Learning, Machine Learning, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Cognitive State Monitoring, Mental Fatigue Detection, Real-Time Fatigue Analysis, Human-Machine Interaction, Temporal Pattern Recognition
Area:-Engineering
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