张朕,姜劲,傅嘉豪,曹勇,焦学军.基于功能近红外光谱的多生理脑力疲劳检测[J].仪器仪表学报,2017,38(6):1345-1352
基于功能近红外光谱的多生理脑力疲劳检测
Multi physiological mental fatigue detection based on the functional near infrared spectroscopy
  
DOI:
中文关键词:  近红外光谱  脑力疲劳  呼吸  心动  支持向量机
英文关键词:functional near infrared spectroscopy  mental fatigue  respiration  cardiac signal  support vector machine (SVM)
基金项目:国家自然科学基金(81671861)、中国航天医学工程预先研究项目(YJGF151204)、 中国航天员科研训练中心人因国家重点实验室自主课题(SYFD150051805)项目资助
作者单位
张朕 中国航天员科研训练中心北京100092 
姜劲 中国航天员科研训练中心北京100092 
傅嘉豪 中国航天员科研训练中心北京100092 
曹勇 中国航天员科研训练中心北京100092 
焦学军 中国航天员科研训练中心北京100092 
AuthorInstitution
Zhang Zhen China Astronaut Research and Training Center, Beijing 100092,China 
Jiang Jin China Astronaut Research and Training Center, Beijing 100092,China 
Fu Jiahao China Astronaut Research and Training Center, Beijing 100092,China 
Cao Yong China Astronaut Research and Training Center, Beijing 100092,China 
Jiao Xuejun China Astronaut Research and Training Center, Beijing 100092,China 
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中文摘要:
      脑力疲劳会引起人机系统绩效下降甚至引起安全事故,因此实时检测疲劳状态具有重要意义。虽然关于脑力疲劳检测的研究较多,但仍未有统一生理标准。由于疲劳的复杂性,多生理检测法已经成为一种趋势,但是会增大设备复杂度。功能近红外光谱能够通过测量人大脑皮层的血氧活动而间接反映脑认知功能,近红外信号中的心动和呼吸信号属于生理活动的敏感信息,但是常被作为干扰去除,因此造成了信息丢失。为增强近红外的生理信息含量并建立多生理疲劳检测模型,从近红外信号中提取出心动和呼吸作为新的敏感特征,并结合均值斜率等常规特征构建基于支持向量机的脑力疲劳检测模型。研究采用60 min 2 back任务诱导疲劳状态,利用近红外测量了15名被试包括前额(PFC)共计10个通道的脑皮层近红外信号。研究结果证实了提取出的心动和呼吸特征对疲劳敏感,且增大了疲劳识别的准确性(84%→90%)。因此,所建立的模型能够有效地检测脑力疲劳并且降低了多生理脑力疲劳检测设备的复杂度。
英文摘要:
      Mental fatigue can reduce work performance and cause safety accidents in human machine systems. Therefore, it is important to detect fatigue in real time. A great deal of work has focused on this problem, but there are still no standards for the physiological index. Multi physiological measurement becomes a trend, at the same time, the increasing complexity of instruments for multi physiological measurement brings challenges due to the complicacy of mental fatigue. Functional Near Infrared Spectroscopy (fNIRS) can measure cerebral hemoglobin and reflect cognitive function indirectly. However, cardiac and respiratory signals in the fNIRS signal are sensitive to physiological activity, which have always been removed as interference in previous studies. To increase the information capacity and establish a multi physiological fatigue detection model using fNIRS, this paper extracts the cardiac and respiratory features from the fNIRS signal as new sensitive feature. A fatigue detection model is proposed based on the support vector machine (SVM) by combining cardiac and respiratory features with common features, such as the mean value and slope. We use a verbal 2 back task for a total of 60 minutes to induce mental fatigue. The fNIRS signals from 10 channels in the prefrontal cortex (PFC) are measured from 15 healthy subjects. The results show that the new cardiac and respiratory features are significantly sensitive to the fatigue state and increase the classification accuracy compared with a common fatigue model based on fNIRS (84%→90%). Our findings can detect mental fatigue effectively and reduce the complexity of equipment significantly for multi physiological fatigue detection.
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