基于脑电信号的动车组司机疲劳状态评估技术研究  

Research on Fatigue State Assessment Technology of EMU Drivers Based on EEG

作　　者：马世伟 王泽敏 吕宝粮[2] MA Shiwei;WANG Zemin;LV Baoliang(Energy Saving&Environmental Protection&Occupational Safety and Health Research Institute,China Academy of Railway Sciences Co.,Ltd.,Beijing 100081,China;Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai 200444,China)

机构地区：[1]中国铁道科学研究院集团有限公司节能环保劳卫研究所,北京100081 [2]上海交通大学计算机与科学工程系,上海200444

出　　处：《铁路节能环保与安全卫生》2021年第4期43-49,共7页Railway Energy Saving & Environmental Protection & Occupational Safety and Health

基　　金：中国铁路总公司科技研究开发计划课题(2016Z003-B)。

摘　　要：通过脑电检测技术读取137名动车组司机值乘前10 min的脑电信号,通过人工智能训练的脑电数据模型评估出乘状态,结合自主设计的调查问卷,采用卡方检验和有序逻辑回归分析的统计方法对结果进行关联性分析。结果表明,通过脑电检测技术检测得到的司机出乘状态与其自评注意力集中困难存在显著相关性(p<0.05);驾驶年龄越大,司机在出乘前越容易疲劳(p<0.01);司机出乘前后身体状态存在显著相关性(p<0.01),提示脑电检测技术可用于评估动车组司机出乘时的身体状态。Through electroencephalogram detection technology,137 driver's electroencephalogram was read within 10 minutes before they drive the train of High-speed.The degree of fatigue was assessed by an electroencephalogram model trained in artificial intelligence.Collect subjective data using self-designed fatigue questionnaires.The correlation analysis of the results is carried out by the statistical methods of card-side testing and orderly logical regression analysis.The results showed that there was a significant correlation between the fatigue status of drivers before they were tested by electroencephalography technology and their difficulty in self-assessment concentration(p<0.05).The older the driver,the more severe the driver's fatigue before the ride(p<0.01)and the significant correlation between the driver's fatigue before and after the ride(p<0.01).It is suggested that EEG detection technology can be used to evaluate the physical state of EMU drivers when they get out.

关 键 词：动车组司机 脑电检测技术 睡眠质量 疲劳 

分 类 号：U268.48[机械工程—车辆工程]