成像式光体积描记术精神压力检测  

Image photoplethysmography for mental stress detection

作　　者：饶治 李炳霖 隋雅茹 嵇晓强[1] 李明烨 RAO Zhi;LI Bing-lin;SUI Ya-ru;JI Xiao-qiang;LI Ming-ye(School of Life Science and Technology,Changchun University of Science and Technology,Changchun 130022,China;Department of Information Systems and Business Analytics,RMIT University,Melbourne 3001,Australia;School of Computing and Information Systems,The University of Melbourne,Melbourne 3053,Australia)

机构地区：[1]长春理工大学生命科学技术学院,吉林长春130022 [2]皇家墨尔本理工大学信息系统和商业分析系,维多利亚州卡尔顿3001 [3]墨尔本大学计算与信息系统学院,墨尔本3053

出　　处：《中国光学（中英文）》2022年第6期1350-1359,共10页Chinese Optics

基　　金：吉林省科技发展计划项目(No.20210204131YY)。

摘　　要：为了实现非接触式的日常精神压力检测,本文提出了一种基于成像式光体积描记术的精神压力检测方法。首先,通过手机摄像头记录受试者面部视频,再采用本文所提出的基于Face Mesh的动态感兴趣区域(Region of Interest,ROI)提取方法获得心率波动引起的皮肤微弱颜色变化。接下来,将快速独立成分分析(FastICA)算法、小波变换和窄带带通滤波相结合,提取基于图像的光体积描记术信号和心率变异性信息。然后,对30名受试者进行了压力诱导实验,通过比较受试者正常和应激状态下心率变异性参数的差异,筛选了用于精神压力检测的14个特征,并探讨了压力诱导的短期精神压力和日常精神压力之间的关系。最后,另外选取67名受试者进行日常精神压力检测,使用机器学习算法建立了精神压力检测的三分类器。实验结果表明:精神压力三分类准确率达到95.2%。鉴于这种方法不需要长期测量,仅使用智能手机就可以准确检测人类精神压力水平,而且测量方法简单,测量时间短,易操作,不会影响受试者的正常心理和精神状态,因此可以作为一种有效的心理学研究工具。To achieve non-contact daily mental stress detection, this paper proposes a image photoplethysmography to detect mental stress. First, a video of the subject’s face is recorded by the cell phone camera. Then, the proposed Dynamic Region of Interest(ROI) extraction method based on Face Mesh is used to obtain the weak skin color changes caused by heart rate fluctuations. Next, the Fast Independent Component Analysis(FastICA) algorithm, wavelet transform and narrowband bandpass filtering are combined to extract the signal and heart rate variability information based on image photoplethysmography. Then, stress-induced experiments are conducted on 30 subjects to screen 14 features for mental stress detection by comparing the differences in heart rate variability parameters between normal and stressful states, and to explore the relationship between short-term mental stress and daily mental stress due to stress induction. Finally, an additional 67 subjects are tested for daily mental stress, and a triple classifier for mental stress detection is built using the machine learning algorithm. The experimental results show that the accuracy of the three classifications of mental stress can reach 95.2%. Given that this method does not require long-term measurements and can accurately detect human mental stress levels using only a smartphone, and that the measurement method is simple, and easy to administer, and does not affect the normal psychological and mental state of the subject, it can be used as a valid tool in psychological research.

关 键 词：非接触 精神压力检测 成像式光体积描记术 心率变异性 三分类 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术] TH691.9[自动化与计算机技术—计算机科学与技术]