生理信號的數(shù)據(jù)采集及其在情緒識別中的應(yīng)用
[Abstract]:Being in negative mood for a long time can lead to disorder of emotional system and affect environmental adaptability and daily learning. However, the emotion of this kind of individual is difficult to be detected by the outside world. Multi-physiological signal emotion recognition is to monitor the changes of physiological indexes by analyzing the characteristic parameters or the combination of characteristic parameters of multi-physiological signals, so as to feedback the real emotional state of the individual. Psychological intervention is to deal with problems through psychological theory and methods, so that people's unbalanced cognitive and emotional state tends to stabilize. In this paper, we use chaos theory to recognize individual's multi-physiological signals, and on the basis of verifying the feasibility of this method, we give psychological counseling to individuals whose negative emotion is identified by psychological intervention. Firstly, physiological signal instruments were used to extract multiple physiological signals (ECG, respiratory signals) from two volunteers (a man and a woman) under four different emotions (sadness, pleasure, anger, happiness). Chaos characteristic parameters (maximum Lyapunov exponent, information entropy, approximate entropy, box and complexity). Secondly, chaotic feature matrix is composed of the extracted chaotic characteristic parameters, and four kinds of emotions are identified and classified by using C5.0 decision tree classifier. The experimental results show that the recognition rate of C5.0 decision tree is 91% and 93% respectively for emotion recognition based on multi-feature parameters of chaos theory, and the gender difference is not significant. On this basis, a person who identified the negative emotion as a result was tracked, and the hot spots that caused the negative emotion were found by means of cognitive behavioral therapy and physiological signal instrument. PR-II biofeedback relaxation training instrument and psychological intervention were used to provide psychological guidance to individuals. The results showed that the recognition rate of positive emotion increased from 60% to 100%, which indicated that psychological intervention based on emotion recognition had a better effect of emotional intervention.
【學(xué)位授予單位】:長春大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2017
【分類號】:R318;TP274.2
【參考文獻】
相關(guān)期刊論文 前10條
1 聶春燕;賀方;佐藤禮華;;多種生理信號的采集及其在情緒分析中應(yīng)用[J];長春大學(xué)學(xué)報;2016年06期
2 趙國朕;宋金晶;葛燕;劉永進;姚林;文濤;;基于生理大數(shù)據(jù)的情緒識別研究進展[J];計算機研究與發(fā)展;2016年01期
3 唐嵩瀟;;情緒識別研究述評[J];吉林化工學(xué)院學(xué)報;2015年10期
4 堯新瑜;李雪梅;;“精神助產(chǎn)術(shù)”及其應(yīng)用課例[J];新課程研究(下旬刊);2014年05期
5 舒娜;張曉星;孫才新;;自治混沌系統(tǒng)中抗噪聲干擾的頻率檢測模型[J];重慶大學(xué)學(xué)報;2013年06期
6 孫洪央;徐祖洋;王靜;雷沛;吳開杰;柴新禹;;基于PSO-kNN算法與多生理參數(shù)的壓力狀態(tài)下情緒識別[J];中國醫(yī)療器械雜志;2013年02期
7 李章勇;姜瑜;王偉;劉亞東;;基于小波變換的皮電信號濾波及奇異性檢測[J];科學(xué)技術(shù)與工程;2013年04期
8 王愛平;萬國偉;程志全;李思昆;;支持在線學(xué)習(xí)的增量式極端隨機森林分類器[J];軟件學(xué)報;2011年09期
9 王曉昕;;仿生色彩設(shè)計應(yīng)用研究[J];數(shù)位時尚(新視覺藝術(shù));2011年01期
10 ;The interaction between cognition and emotion[J];Chinese Science Bulletin;2009年22期
相關(guān)會議論文 前1條
1 郝連旺;宋濤;;呼吸信號檢測方法的研究[A];第十屆全國敏感元件與傳感器學(xué)術(shù)會議論文集[C];2007年
相關(guān)博士學(xué)位論文 前1條
1 溫萬惠;基于生理信號的情感識別方法研究[D];西南大學(xué);2010年
相關(guān)碩士學(xué)位論文 前7條
1 何成;基于多生理信號的情緒識別方法研究[D];浙江大學(xué);2016年
2 郭漩;基于人工神經(jīng)網(wǎng)絡(luò)的多生理信號情緒識別系統(tǒng)設(shè)計與實現(xiàn)[D];華東師范大學(xué);2014年
3 曹夢思;基于腦電信號的中文情感詞的情感識別[D];北京郵電大學(xué);2012年
4 徐亞;基于心電信號的情感識別研究[D];西南大學(xué);2010年
5 張維平;基于生物反饋的放松技能訓(xùn)練系統(tǒng)的研究[D];燕山大學(xué);2006年
6 李玉霞;放松訓(xùn)練對抑郁癥患者心算的皮電、心率及心率變異性的影響[D];河北師范大學(xué);2006年
7 周潔;語音信號中情感信息的分析和處理研究[D];東南大學(xué);2005年
,本文編號:2468568
本文鏈接:http://sikaile.net/yixuelunwen/swyx/2468568.html