【摘要】：腦力疲勞會(huì)引起人機(jī)系統(tǒng)績(jī)效下降甚至引起安全事故,因此實(shí)時(shí)檢測(cè)疲勞狀態(tài)具有重要意義。雖然關(guān)于腦力疲勞檢測(cè)的研究較多,但仍未有統(tǒng)一生理標(biāo)準(zhǔn)。由于疲勞的復(fù)雜性,多生理檢測(cè)法已經(jīng)成為一種趨勢(shì),但是會(huì)增大設(shè)備復(fù)雜度。功能近紅外光譜能夠通過(guò)測(cè)量人大腦皮層的血氧活動(dòng)而間接反映腦認(rèn)知功能,近紅外信號(hào)中的心動(dòng)和呼吸信號(hào)屬于生理活動(dòng)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(qiáng)近紅外的生理信息含量并建立多生理疲勞檢測(cè)模型,從近紅外信號(hào)中提取出心動(dòng)和呼吸作為新的敏感特征,并結(jié)合均值斜率等常規(guī)特征構(gòu)建基于支持向量機(jī)的腦力疲勞檢測(cè)模型。研究采用60 min 2-back任務(wù)誘導(dǎo)疲勞狀態(tài),利用近紅外測(cè)量了15名被試包括前額(PFC)共計(jì)10個(gè)通道的腦皮層近紅外信號(hào)。研究結(jié)果證實(shí)了提取出的心動(dòng)和呼吸特征對(duì)疲勞敏感,且增大了疲勞識(shí)別的準(zhǔn)確性(84%→90%)。因此,所建立的模型能夠有效地檢測(cè)腦力疲勞并且降低了多生理腦力疲勞檢測(cè)設(shè)備的復(fù)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(guó)航天員科研訓(xùn)練中心;
【基金】：國(guó)家自然科學(xué)基金(81671861) 中國(guó)航天醫(yī)學(xué)工程預(yù)先研究項(xiàng)目(YJGF151204) 中國(guó)航天員科研訓(xùn)練中心人因國(guó)家重點(diǎn)實(shí)驗(yàn)室自主課題(SYFD150051805)項(xiàng)目資助
【分類號(hào)】：R318;TN219

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