探索自动疲劳检测过程中生理信号之间的关系
Exploring the Relationships Between Physiological Signals During Automated Fatigue Detection
AI总结：
本研究利用DROZY数据集，通过融合多种生理信号特征，采用XGBoost等方法提升疲劳检测的准确性和可解释性，验证了多信号模型优于单信号模型。
摘要：
利用生理信号进行疲劳检测在交通、医疗保健和性能监测等领域至关重要。虽然大多数研究集中于单一模态，但本研究考察了信号对之间的统计关系，以提高分类的鲁棒性。使用DROZY数据集，我们从ECG、EMG、EOG和EEG中提取特征，涵盖15种信号组合，并使用决策树、随机森林、逻辑回归和XGBoost进行评估。结果表明，采用EMG-EEG组合的XGBoost取得了最佳性能。SHAP分析突出显示ECG-EOG相关性是关键特征，且多信号模型始终优于单信号模型。这些发现表明，生理信号的特征级融合增强了疲劳监测系统的准确性、可解释性和实际适用性。
英文摘要：
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.
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