本文選題：壓力識(shí)別 + 可穿戴設(shè)備��； 參考：《電子與信息學(xué)報(bào)》2017年11期

【摘要】：現(xiàn)代生活普遍壓力較大,容易引起消極痛苦的應(yīng)激,導(dǎo)致不良情緒甚至滋生各類慢性病。心理專家需要了解個(gè)體的壓力狀態(tài),從而開(kāi)展對(duì)應(yīng)性心理疏導(dǎo)和治療。傳統(tǒng)心理學(xué)自評(píng)法存在一定的主觀性;基于生理多導(dǎo)儀的壓力狀態(tài)評(píng)估法,受設(shè)備體積所限無(wú)法用于日常壓力狀態(tài)評(píng)估。針對(duì)上述問(wèn)題,該文采用可穿戴式傳感設(shè)備實(shí)時(shí)采集個(gè)體生理信號(hào),利用心理和生理的伴生關(guān)系,對(duì)個(gè)體的心理壓力進(jìn)行長(zhǎng)期實(shí)時(shí)評(píng)估。同時(shí)通過(guò)蒙特利爾影像應(yīng)激實(shí)驗(yàn)(MIST)誘發(fā)出被試平靜、輕微及高度壓力3種壓力狀態(tài),此實(shí)驗(yàn)范式同時(shí)包含認(rèn)知負(fù)荷精神壓力因素與社會(huì)評(píng)價(jià)心理壓力因素,與日常真實(shí)生活更為接近。該文共采集39名健康被試的實(shí)驗(yàn)數(shù)據(jù),通過(guò)對(duì)數(shù)據(jù)的特征值提取等預(yù)處理,結(jié)合隨機(jī)森林算法對(duì)最優(yōu)特征子集進(jìn)行選擇,采用支持向量機(jī)(SVM)分類算法對(duì)3種壓力狀態(tài)進(jìn)行分類預(yù)測(cè)。實(shí)驗(yàn)結(jié)果表明,通過(guò)隨機(jī)森林特征選擇優(yōu)化后的SVM分類,與通用的單一SVM分類算法相比,具有更好的分類識(shí)別效果,對(duì)3種壓力狀態(tài)的分類準(zhǔn)確率可從78%提高至84%。
[Abstract]:The stress of modern life is high, which can lead to negative and painful stress, leading to bad mood and even various chronic diseases. Psychological experts need to understand the individual stress state, so as to develop corresponding psychological counseling and treatment. The traditional psychological self-evaluation method has some subjectivity, and the pressure state evaluation method based on physiological multi-conductors can not be used to evaluate the daily stress state due to the limitation of the equipment volume. To solve the above problems, the wearable sensing equipment is used to collect individual physiological signals in real time, and the long-term real-time evaluation of individual psychological pressure is carried out by using the relationship between psychology and physiology. At the same time, three kinds of stress states were induced by Montreal image stress test (MIST), which included cognitive load, mental stress factors and social evaluation psychological stress factors. Closer to everyday real life. In this paper, the experimental data of 39 healthy subjects were collected, and the optimal feature subset was selected by preprocessing the data, such as extracting the eigenvalue of the data, and combining with the stochastic forest algorithm. Support vector machine (SVM) classification algorithm is used to classify and predict three kinds of pressure states. The experimental results show that, compared with the general single SVM classification algorithm, the optimized SVM classification based on stochastic forest features has better classification and recognition effect, and the classification accuracy of the three pressure states can be improved from 78% to 84%.
【作者單位】： 中國(guó)科學(xué)院電子學(xué)研究所;中國(guó)科學(xué)院大學(xué);中國(guó)科學(xué)院心理研究所;
【基金】：國(guó)家自然科學(xué)基金(61302033) 北京市自然科學(xué)基金(Z160003) 國(guó)家重點(diǎn)研發(fā)計(jì)劃(2016YFC1304302,2016YFC026502,2016YFC1303900)~~
【分類號(hào)】：R318.6;TP18

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