See your mental state from your walk: Recognizing anxiety and depression through Kinect-recorded gait data

• Published in: PloS one Vol. 14; no. 5; p. e0216591
• Main Authors: Zhao, Nan, Zhang, Zhan, Wang, Yameng, Wang, Jingying, Li, Baobin, Zhu, Tingshao, Xiang, Yuanyuan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.05.2019; Public Library of Science (PLoS)
• Subjects: Adult; Algorithms; Anxiety; Anxiety disorders; Anxiety Disorders - diagnosis; Archives & records; Behavioral sciences; Biology and Life Sciences; Cameras; Classification; Computer and Information Sciences; Computer science; Correlation coefficient; Correlation coefficients; Data mining; Depression (Mood disorder); Depressive Disorder - diagnosis; Diagnosis; Epidemiology; Fast Fourier transformations; Feature extraction; Female; Fourier transforms; Gait; Gait - physiology; Gaussian process; Gender differences; Generalized anxiety disorder; Health aspects; Health problems; Health surveys; Humans; Internal medicine; International conferences; Laboratories; Learning algorithms; Levels; Machine Learning; Male; Medicine and Health Sciences; Mental depression; Mental disorders; Mental Health; Model accuracy; Models, Statistical; Monitoring methods; Motion capture; Novels; Patients; Physical Sciences; Posture; Psychiatry; Psychological Tests; Psychology; Questionnaires; Real time; Regression analysis; Regression models; Research and Analysis Methods; Researchers; Signs and symptoms; Social Sciences; Support vector machines; Surveys and Questionnaires; Systematic review; Walking; Walking - physiology; Young Adult; Beijing China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0216591

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.