發(fā)布時間：2018-10-23 08:49

【摘要】：腦力疲勞會引起人機系統(tǒng)績效下降甚至引起安全事故,因此實時檢測疲勞狀態(tài)具有重要意義。雖然關于腦力疲勞檢測的研究較多,但仍未有統(tǒng)一生理標準。由于疲勞的復雜性,多生理檢測法已經成為一種趨勢,但是會增大設備復雜度。功能近紅外光譜能夠通過測量人大腦皮層的血氧活動而間接反映腦認知功能,近紅外信號中的心動和呼吸信號屬于生理活動的敏感信息,但是常被作為干擾去除,因此造成了信息丟失。為增強近紅外的生理信息含量并建立多生理疲勞檢測模型,從近紅外信號中提取出心動和呼吸作為新的敏感特征,并結合均值斜率等常規(guī)特征構建基于支持向量機的腦力疲勞檢測模型。研究采用60 min 2-back任務誘導疲勞狀態(tài),利用近紅外測量了15名被試包括前額(PFC)共計10個通道的腦皮層近紅外信號。研究結果證實了提取出的心動和呼吸特征對疲勞敏感,且增大了疲勞識別的準確性(84%→90%)。因此,所建立的模型能夠有效地檢測腦力疲勞并且降低了多生理腦力疲勞檢測設備的復雜度。
[Abstract]:Mental fatigue can cause deterioration of man-machine system performance and even cause safety accidents, so it is very important to detect fatigue state in real time. Although there are many researches on mental fatigue detection, there is still no unified physiological standard. Due to the complexity of fatigue, multi-physiological detection has become a trend, but it will increase the complexity of equipment. Functional near infrared spectroscopy (FNIR) can indirectly reflect the cognitive function of brain by measuring the blood oxygen activity of human cerebral cortex. The cardiac and respiratory signals in NIR signal are sensitive information of physiological activities, but they are often removed as interference. As a result, information is lost. In order to enhance the physiological information content of NIR and establish a multi-physiological fatigue detection model, cardiac and respiratory signals were extracted from NIR signals as a new sensitive feature. A mental fatigue detection model based on support vector machine (SVM) was constructed based on the conventional features such as mean slope. The 60 min 2-back task induced fatigue state was used to measure the cortical near infrared signals of 15 subjects, including 10 channels of prefrontal (PFC). The results show that the extracted cardiac and respiratory characteristics are sensitive to fatigue and increase the accuracy of fatigue identification (84% or 90%). Therefore, the established model can effectively detect mental fatigue and reduce the complexity of multiple physiological mental fatigue detection equipment.
【作者單位】： 中國航天員科研訓練中心;
【基金】：國家自然科學基金(81671861) 中國航天醫(yī)學工程預先研究項目(YJGF151204) 中國航天員科研訓練中心人因國家重點實驗室自主課題(SYFD150051805)項目資助
【分類號】：R318;TN219

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