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See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data
Zhao, Nan1; Zhang, Zhan1,2; Wang, Yameng1,2; Wang, Jingying1; Li, Baobin2; Zhu, Tingshao1; Xiang, Yuanyuan1
第一作者Zhao, Nan
通讯作者邮箱tszhu@psych.ac.cn
心理所单位排序1
摘要

As the challenge of mental health problems such as anxiety and depression increasing today, more convenient, objective, real-time assessing techniques of mental state are in need. The Microsoft Kinect camera is a possible option for contactlessly capturing human gait, which could reflect the walkers' mental state. So we tried to propose a novel method for monitoring individual's anxiety and depression based on the Kinect-recorded gait pattern. In this study, after finishing the 7-item Generalized Anxiety Disorder Scale (GAD-7) and the 9-item Patient Health Questionnaire (PHQ-9), 179 participants were required to walked on the footpath naturally while shot by the Kinect cameras. Fast Fourier Transforms (FFT) were conducted to extract features from the Kinect-captured gait data after preprocessing, and different machine learning algorithms were used to train the regression models recognizing anxiety and depression levels, and the classification models detecting the cases with specific depressive symptoms. The predictive accuracies of the regression models achieved medium to large level: The correlation coefficient between predicted and questionnaire scores reached 0.51 on anxiety (by epsilon-Support Vector Regression, e-SVR) and 0.51 on depression (by Gaussian Processes, GP). The predictive accuracies could be even higher, 0.74 on anxiety (by GP) and 0.64 on depression (by GP), while training and testing the models on the female sample. The classification models also showed effectiveness on detecting the cases with some symptoms. These results demonstrate the possibility to recognize individual's questionnaire measured anxiety/depression levels and some depressive symptoms based on Kinect-recorded gait data through machine learning method. This approach shows the potential to develop non-intrusive, low-cost methods for monitoring individuals' mental health in real time.

2019-05-22
DOI10.1371/journal.pone.0216591
发表期刊PLOS ONE
ISSN1932-6203
卷号14期号:5页码:13
收录类别SCI
资助项目National Key Research & Development Program of China[2016YFC1307200] ; National Natural Science Foundation of China[31700984]
出版者PUBLIC LIBRARY SCIENCE
WOS关键词GENDER-DIFFERENCES ; DISORDER ; DYSFUNCTION ; PATTERNS ; POSTURES ; DISEASE ; PHQ-9
WOS研究方向Science & Technology - Other Topics
WOS类目Multidisciplinary Sciences
WOS记录号WOS:000468607400024
WOS分区Q1
资助机构National Key Research & Development Program of China ; National Natural Science Foundation of China
引用统计
被引频次:37[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/29266
专题中国科学院行为科学重点实验室
通讯作者Zhu, Tingshao
作者单位1.Chinese Acad Sci, Inst Psychol, Key Lab Behav Sci, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China
第一作者单位中国科学院行为科学重点实验室
通讯作者单位中国科学院行为科学重点实验室
推荐引用方式
GB/T 7714
Zhao, Nan,Zhang, Zhan,Wang, Yameng,et al. See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data[J]. PLOS ONE,2019,14(5):13.
APA Zhao, Nan.,Zhang, Zhan.,Wang, Yameng.,Wang, Jingying.,Li, Baobin.,...&Xiang, Yuanyuan.(2019).See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data.PLOS ONE,14(5),13.
MLA Zhao, Nan,et al."See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data".PLOS ONE 14.5(2019):13.
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