疲劳检测的研究进展

职业与健康 ›› 2025, Vol. 41 ›› Issue (15): 2149-2154.

疲劳检测的研究进展

欧阳安平¹, 张芫蓓¹, 毋琳¹, 李炎², 曹致远¹, 方鹏^1,3

1.空军军医大学军事医学心理学系,陕西西安 710032;
2.清华大学社会科学院心理学系,北京 100084;
3.陕西省生物电磁检测与智能感知重点实验室,陕西西安 710032

收稿日期:2024-07-15 修回日期:2024-11-11 出版日期:2025-08-15 发布日期:2025-12-12
通信作者: 方鹏,副教授,E-mail：fangpeng@fmmu.edu.cn
作者简介:欧阳安平,女,在读硕士研究生,研究方向为心理学与认知神经科学。
基金资助:
国家自然科学基金（32471081）; 无人飞行器技术全国重点实验室基金（WR202420-2）; “临床医学+X”研究中心科研课题（LHJJ24XL03）

Research progress in fatigue detection

OUYANG Anping¹, ZHANG Yuanbei¹, WU Lin¹, LI Yan², CAO Zhiyuan¹, FANG Peng^1,3

1. Department of Military Medical Psychology, Air Force Medical University, Xi'an, Shaanxi 710032, China;
2. Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China;
3. Shaanxi Key Laboratory of Bio-Electromagnetic Detection and Intelligent Sensing, Xi'an, Shaanxi 710032, China

Received:2024-07-15 Revised:2024-11-11 Online:2025-08-15 Published:2025-12-12
Contact: FANG Peng,Associate professor,E-mail：fangpeng@fmmu.edu.cn

摘要/Abstract

摘要： 疲劳感知已有全民化趋势,疲劳检测是安全领域中的热点研究方向,有效的疲劳检测对个人健康、社会安全和稳定具有重要意义,现有的疲劳检测对象以驾驶员、飞行员为主体。基于生理指标或主观报告的方式进行疲劳检测已经取得了良好的发展,但在疲劳检测的准确性、灵敏度以及实际应用上仍存在不足。本文从主观评定、客观检测、多模态数据融合3个角度总结了基于量表、脑电信号（electroencephalogram,EEG）、心电信号（electrocardiogram,ECG）、肌电信号（electromyography,EMG）、眼动信号、呼吸、脉搏信号、血氧信号、身体特征综述疲劳检测的研究进展。同时,分析现有疲劳检测方法的应用及不足,探讨未来疲劳检测的发展方向,为提高疲劳检测的准确性和应用价值提供思路和借鉴。

关键词: 疲劳检测, 技术方法, 生理信号, 多模态

Abstract: Fatigue perception has become a universal trend,and fatigue detection is a hot research direction in the field of safety. Effective fatigue detection is of great significance to personal health,social security and stability. The existing fatigue detection objects are mainly drivers and pilots. Fatigue detection based on physiological indicators or subjective reports has made good progress,but there are still shortcomings in the accuracy,sensitivity and practical application of fatigue detection. This paper summarizes the research progress of fatigue detection based on scale,electroencephalogram（EEG）,electrocardiogram（ECG）,electromyogram（EMG）,eye movement signal,respiration,pulse signal,blood oxygen signal and body characteristics from the perspectives of subjective evaluation,objective detection and multi-modal data fusion. At the same time,the application and shortcomings of existing fatigue detection methods are analyzed,and the development direction of fatigue detection in the future is discussed,which provides ideas and references for improving the accuracy and application value of fatigue detection.

Key words: Fatigue detection, Technology and methods, Physiological signal, Multimodality

中图分类号:

R181.3

欧阳安平, 张芫蓓, 毋琳, 李炎, 曹致远, 方鹏. 疲劳检测的研究进展. [J]职业与健康, 2025, 41(15): 2149-2154.

OUYANG Anping, ZHANG Yuanbei, WU Lin, LI Yan, CAO Zhiyuan, FANG Peng. Research progress in fatigue detection. [J]OCCUPATION AND HEALTH, 2025, 41(15): 2149-2154.

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