Simple action for depression detection: using kinect-recorded human kinematic skeletal data.

Journal: BMC psychiatry
Published Date:

Abstract

BACKGROUND: Depression, a common worldwide mental disorder, which brings huge challenges to family and social burden around the world is different from fluctuant emotion and psychological pressure in their daily life. Although body signs have been shown to present manifestations of depression in general, few researches focus on whole body kinematic cues with the help of machine learning methods to aid depression recognition. Using the Kinect V2 device to record participants' simple kinematic skeleton data of the participant's body joints, the presented spatial features and low-level features is directly extracted from the record original Kinect-3D coordinates. This research aimed to constructed machine learning model with the preprocessed data importing, which could be used for depression automatic classification.

Authors

  • Wentao Li
    State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, Hubei, People's Republic of China.
  • Qingxiang Wang
    School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China. wangqx@qlu.edu.cn.
  • Xin Liu
    Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences, Weifang, Shandong, China.
  • Yanhong Yu
    College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China. yhysdutcm@163.com.