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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
First AuthorZhao, Nan
2019-05-22
Source PublicationPLOS ONE
Correspondent Emailzhu, tingshao(tszhu@psych.ac.cn)
ISSN1932-6203
Subtypearticle
Volume14Issue:5Pages:13
Contribution Rank1
Abstract

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.

DOI10.1371/journal.pone.0216591
Indexed BySCI
Language英语
Funding OrganizationNational Key Research & Development Program of China ; National Natural Science Foundation of China
Funding ProjectNational Key Research & Development Program of China[2016YFC1307200] ; National Natural Science Foundation of China[31700984]
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000468607400024
PublisherPUBLIC LIBRARY SCIENCE
WOS KeywordGENDER-DIFFERENCES ; DISORDER ; DYSFUNCTION ; PATTERNS ; POSTURES ; DISEASE ; PHQ-9
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/29266
Collection中国科学院行为科学重点实验室
Corresponding AuthorZhu, Tingshao
Affiliation1.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
Recommended Citation
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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