CRNN-Based Modeling of Mental Fatigue from Multimodal Signals: A Systematic Review
Volume: 14 - Issue: 05 - Date: 01-05-2025
Approved ISSN: 2278-1412
Published Id: IJAECESTU456 | Page No.: 106-109
Author: Salunke Apurvaa Annasaheb
Co- Author: Mr. Jeetendra Singh Yadav,,,
Abstract:-Mental fatigue—characterized by reduced vigilance, slowed reaction time, and cognitive
inefficiency—poses safety and productivity risks in transportation, healthcare, and industrial settings. Recent
work leverages convolutional–recurrent neural networks (CRNNs), typically coupling convolutional layers
(for spatial/spectral feature extraction) with recurrent layers (LSTM/GRU for temporal dynamics), to model
mental fatigue from multimodal signals such as EEG, EOG, ECG/PPG, fNIRS, eye tracking (PERCLOS),
facial video, and behavioral/interaction traces. This systematic review synthesizes CRNN-based methods for
mental fatigue estimation from 2013–2025, covering sensing modalities, input encodings (e.g., spectrograms,
wavelets, topographical maps), fusion strategies (early/late/hybrid attention), learning paradigms
(supervised, transfer, self-supervised), evaluation protocols (within-subject vs. cross-subject), and
deployment aspects (real-time/edge). We summarize representative datasets (SEED-VIG, DROZY, NTHUDDD, FatigueSet, and others), identify typical performance regimes, and highlight open problems in label
quality, domain shift, personalization, interpretability, and ethics. We conclude with a set of practical
recommendations and a forward-looking agenda that integrates self-supervised pretraining, lightweight
architectures, and robust human-in-the-loop evaluation
Key Words:-Mental Fatigue, Vigilance, Drowsiness, CRNN, CNN–LSTM/GRU, Multimodal Fusion, EEG, EOG, ECG, Fnirs, PERCLOS, Eye Tracking, Wearable Sensing, Edge AI
Area:-Engineering
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