基于虚拟现实的恐惧测量模型建立
|
摘要
恐惧程度的客观评价在心理健康评估和职业能力评测中具有重要的作用。在沉浸式训练过程中,生理和运动数据有助于提升交互界面设计,增加虚拟训练的安全性。文中使用头戴式VR系统HTC Vive实现沉浸式VR体验,利用OpenVR开源软件包采集控制器和头部的位置信息,并结合Equivital belt EQ02 Lifemonitor所采集的心电信号、躯体加速度实现多种信号的特征提取。文中使用递归特征消除的特征排序方法和支持向量回归从50个生理及运动特征中选取了10个特征。基于自测恐惧值和特征的多元多项式回归实现了准确率为90%的二分类,即完成了对受试者恐惧或者非恐惧的分类。
The objective evaluation of the degree of fear plays an important role in mental health and professional ability assessment. Physiological and exercise data in the immersive training process can improve the interface design and increase the safety of virtual training. This article used the HTC Vive head-mounted VR system to realize the immersive VR experience. OpenVR open source software package was used to collect the position information of the controller and the head, which was combined the ECG signals and body acceleration collected by the Equivital belt EQ02 Lifemonitor to realize feature extraction of various signals. In this paper, the feature ranking method and support vector regression utilizing recursive feature elimination were used to select 10 features from 50 physiological and motion features. Finally, the multivariate polynomial regression based on self-tested fear values and features achieved a binary classification with an accuracy rate of 90%, which completed the classification of fear or non-fear of the subject.
The objective evaluation of the degree of fear plays an important role in mental health and professional ability assessment. Physiological and exercise data in the immersive training process can improve the interface design and increase the safety of virtual training. This article used the HTC Vive head-mounted VR system to realize the immersive VR experience. OpenVR open source software package was used to collect the position information of the controller and the head, which was combined the ECG signals and body acceleration collected by the Equivital belt EQ02 Lifemonitor to realize feature extraction of various signals. In this paper, the feature ranking method and support vector regression utilizing recursive feature elimination were used to select 10 features from 50 physiological and motion features. Finally, the multivariate polynomial regression based on self-tested fear values and features achieved a binary classification with an accuracy rate of 90%, which completed the classification of fear or non-fear of the subject.
引文
[1] Ekman P,Levenson R W,Friesen W V.Autonomic nervous system activity distinguishes among emotions [J].Science,1983,221(4616):1208-1210.
[2] Barrett L F.Are emotions natural kinds[J].Perspectives on Psychological Science,2006,1(1):28-58.
[3] Kreibig S D,Wilhelm F H,Roth W T,et al.Cardiovascular,electrodermal,and respiratory response patterns to fear- and sadness-inducing films[J].Psychophysiology,2007,44(5):787.
[4] Novak D,Mihelj M,Munih M.A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing[J].Interacting with Computers,2012,24(3):154-172.
[5] Koelstra S,Muhl C,Soleymani M,et al.DEAP:a database for emotion analysis using physiological signals[J].IEEE Transactions on Affective Computing,2012,3(1):18-31.
[6] 李娅丽,忻尚芝,郑春雷.基于MATLAB/GUI的数据分析界面设计[J].电子科技,2016,29(4):88-91.Li Yali,Xin Shangzhi,Zheng Chunlei.Design of data analysis system based on MATLAB/GUI[J].Electronic Science and Technology,2016,29(4):88-91.
[7] Listed N.Task force of the european society of cardiology and the North American society of pacing and electrophysiology[M].Batavia:Quintessence Editora,1996.
[8] Kamath M V,Fallen E L.Power spectral analysis of heart rate variability:a noninvasive signature of cardiac autonomic function[J].Critical Reviews in Biomedical Engineering,1993,21(3):245-259.
[9] Kreibig S D.Autonomic nervous system activity in emotion:A review[J].Biological Psychology,2010,84(3):394-421.
[10] Stemmler G,Aue T,Wacker J.Anger and fear:Separable effects of emotion and motivational direction on somatovisceral responses[J].International Journal of Psychophysiology,2007,66(2):141-153.
[11] Gilissen R,Koolstra C M,Ijzendoorn M H,et al.Physiological reactions of preschoolers to fear-inducing film clips:effects of temperamental fearfulness and quality of the parent-child relationship[J].Developmental Psychobiology,2007,49(2):187-195.
[12] Gilissen R,Bakermans-Kranenburg M J,Ijzendoorn M H V,et al.Parent–child relationship,temperament,and physiological reactions to fear-inducing film clips:Further evidence for differential susceptibility[J].Journal of Exp Child Psychol,2008,99(3):182-195.
[13] Guyon I,Weston J,Barnhill S,et al.Gene selection for cancer classification using support vector machines[J].Machine Learning,2002,46(1-3):389-422.
[14] Gunn S R.Support vector machines for classification and regression[R].Southampton:University of Southampton,1998.
[15] Hastie T,Friedman J,Tibshirani R.Unsupervised learning[M].Hoboken:Wiley-IEEE Press,2009.
[16] 杜宝江,王衍昊,倪佳伟,等.电梯安装虚拟培训系统技术研究[J].电子科技,2017,30(6):115-117.Du Baojiang,Wang Yanhao,Ni Jiawei,et al.Research on virtual training system for elevator installation[J].Electronic Science and Technology,2017,30(6):115-117.
[2] Barrett L F.Are emotions natural kinds[J].Perspectives on Psychological Science,2006,1(1):28-58.
[3] Kreibig S D,Wilhelm F H,Roth W T,et al.Cardiovascular,electrodermal,and respiratory response patterns to fear- and sadness-inducing films[J].Psychophysiology,2007,44(5):787.
[4] Novak D,Mihelj M,Munih M.A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing[J].Interacting with Computers,2012,24(3):154-172.
[5] Koelstra S,Muhl C,Soleymani M,et al.DEAP:a database for emotion analysis using physiological signals[J].IEEE Transactions on Affective Computing,2012,3(1):18-31.
[6] 李娅丽,忻尚芝,郑春雷.基于MATLAB/GUI的数据分析界面设计[J].电子科技,2016,29(4):88-91.Li Yali,Xin Shangzhi,Zheng Chunlei.Design of data analysis system based on MATLAB/GUI[J].Electronic Science and Technology,2016,29(4):88-91.
[7] Listed N.Task force of the european society of cardiology and the North American society of pacing and electrophysiology[M].Batavia:Quintessence Editora,1996.
[8] Kamath M V,Fallen E L.Power spectral analysis of heart rate variability:a noninvasive signature of cardiac autonomic function[J].Critical Reviews in Biomedical Engineering,1993,21(3):245-259.
[9] Kreibig S D.Autonomic nervous system activity in emotion:A review[J].Biological Psychology,2010,84(3):394-421.
[10] Stemmler G,Aue T,Wacker J.Anger and fear:Separable effects of emotion and motivational direction on somatovisceral responses[J].International Journal of Psychophysiology,2007,66(2):141-153.
[11] Gilissen R,Koolstra C M,Ijzendoorn M H,et al.Physiological reactions of preschoolers to fear-inducing film clips:effects of temperamental fearfulness and quality of the parent-child relationship[J].Developmental Psychobiology,2007,49(2):187-195.
[12] Gilissen R,Bakermans-Kranenburg M J,Ijzendoorn M H V,et al.Parent–child relationship,temperament,and physiological reactions to fear-inducing film clips:Further evidence for differential susceptibility[J].Journal of Exp Child Psychol,2008,99(3):182-195.
[13] Guyon I,Weston J,Barnhill S,et al.Gene selection for cancer classification using support vector machines[J].Machine Learning,2002,46(1-3):389-422.
[14] Gunn S R.Support vector machines for classification and regression[R].Southampton:University of Southampton,1998.
[15] Hastie T,Friedman J,Tibshirani R.Unsupervised learning[M].Hoboken:Wiley-IEEE Press,2009.
[16] 杜宝江,王衍昊,倪佳伟,等.电梯安装虚拟培训系统技术研究[J].电子科技,2017,30(6):115-117.Du Baojiang,Wang Yanhao,Ni Jiawei,et al.Research on virtual training system for elevator installation[J].Electronic Science and Technology,2017,30(6):115-117.