Exploring the Relationships Between Physiological Signals During Automated Fatigue Detection
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909808420978688 |
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| author | Kakhi, Kourosh Khosravi, Abbas Alizadehsani, Roohallah Acharyab, U. Rajendra |
| author_facet | Kakhi, Kourosh Khosravi, Abbas Alizadehsani, Roohallah Acharyab, U. Rajendra |
| contents | Fatigue detection using physiological signals is critical in domains such as transportation, healthcare, and performance monitoring. While most studies focus on single modalities, this work examines statistical relationships between signal pairs to improve classification robustness. Using the DROZY dataset, we extracted features from ECG, EMG, EOG, and EEG across 15 signal combinations and evaluated them with Decision Tree, Random Forest, Logistic Regression, and XGBoost. Results show that XGBoost with the EMG EEG combination achieved the best performance. SHAP analysis highlighted ECG EOG correlation as a key feature, and multi signal models consistently outperformed single signal ones. These findings demonstrate that feature level fusion of physiological signals enhances accuracy, interpretability, and practical applicability of fatigue monitoring systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21794 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring the Relationships Between Physiological Signals During Automated Fatigue Detection Kakhi, Kourosh Khosravi, Abbas Alizadehsani, Roohallah Acharyab, U. Rajendra Machine Learning Fatigue detection using physiological signals is critical in domains such as transportation, healthcare, and performance monitoring. While most studies focus on single modalities, this work examines statistical relationships between signal pairs to improve classification robustness. Using the DROZY dataset, we extracted features from ECG, EMG, EOG, and EEG across 15 signal combinations and evaluated them with Decision Tree, Random Forest, Logistic Regression, and XGBoost. Results show that XGBoost with the EMG EEG combination achieved the best performance. SHAP analysis highlighted ECG EOG correlation as a key feature, and multi signal models consistently outperformed single signal ones. These findings demonstrate that feature level fusion of physiological signals enhances accuracy, interpretability, and practical applicability of fatigue monitoring systems. |
| title | Exploring the Relationships Between Physiological Signals During Automated Fatigue Detection |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.21794 |