FatigueNet: A hybrid graph neural network and transformer framework for real-time multimodal fatigue detection

Seyyed Ali Zendehbad ¹, Jamal Ghasemi ², Farid Samsami Khodadad ³

⁰Faculty of Engineering & Technology, University of Mazandaran, Babolsar, Iran.

Scientific Reports +

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October 1, 2025

View abstract on PubMed

Summary

FatigueNet, a multimodal framework, accurately detects fatigue using biosignals like ECG, EDA, EMG, and eye blinks. This advanced system offers real-time monitoring with low latency, improving upon current fatigue classification models.

Area Of Science

Biomedical Engineering
Artificial Intelligence
Signal Processing

Background

Fatigue presents complex challenges impacting cognitive, physical, and emotional well-being.
Current fatigue classification models struggle with biosignal diversity and interdependence.
There is a need for advanced frameworks to accurately detect and monitor fatigue levels.

Purpose Of The Study

To introduce FatigueNet, a novel multimodal framework for fatigue classification.
To address limitations in current models by integrating diverse biosignals and complex signal interdependence.
To develop an end-to-end system capable of real-time fatigue monitoring.

Main Methods

Utilized a combination of Graph Neural Network (GNN) and Transformer architecture.
Extracted dynamic features from Electrocardiogram (ECG), Electrodermal Activity (EDA), Electromyography (EMG), and Eye-Blink signals.
Employed adaptive feature adjustment and meta-learned gate distribution to capture temporal, spatial, and contextual relationships.

Main Results

FatigueNet demonstrated superior performance compared to existing benchmarks on the MePhy dataset.
The model accurately detected fatigue levels across four distinct categories.
Achieved an end-to-end latency of 50 ms per 20s window, proving real-time capability.

Conclusions

FatigueNet offers an improved approach to fatigue classification by effectively handling signal diversity and interdependence.
The framework's real-time performance makes it suitable for practical fatigue monitoring applications.
The multimodal approach surpasses traditional methods relying on manual feature engineering or single signal sources.

Keywords:

Biological signals processing Fatigue recognition Graph neural networks (GNNs) Multimodality Transformers

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