融合多生理信号关系提升疲劳检测准确率
Exploring the Relationships Between Physiological Signals During Automated Fatigue Detection
- 分析15种生理信号组合的统计关系，用集成模型融合特征
- EMG+EEG组合在XGBoost下达到最优，准确率显著提升
- 揭示ECG与EOG相关性关键作用，适合医疗与驾驶场景
使用生理信号进行疲劳检测在交通、医疗和表现监控领域至关重要。现有研究多集中于单一模态，本文通过分析信号对之间的统计关系，提升分类鲁棒性。基于DROZY数据集，从心电（ECG）、肌电（EMG）、眼电（EOG）和脑电（EEG）中提取15组信号组合特征，采用决策树、随机森林、逻辑回归和XGBoost进行评估。结果表明，使用XGBoost与EMG+EEG组合时性能最佳。SHAP分析显示ECG与EOG的相关性是关键特征，多信号模型始终优于单信号模型。研究证实，在特征层面融合生理信号可提升疲劳监测系统的准确性、可解释性与实际应用价值。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。