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

• Published in: BMC psychiatry Vol. 21; no. 1; pp. 205 - 11
• Main Authors: Li, Wentao, Wang, Qingxiang, Liu, Xin, Yu, Yanhong
• Format: Journal Article
• Language: English
• Published: England BioMed Central 22.04.2021; BMC
• Subjects: Age; Biomechanical Phenomena; Classification; Computer applications; Data collection; Datasets; Depression - diagnosis; Depression detection; Design; Experiments; Female; Gender; Human skeleton joints; Humans; Insomnia; Kinect sensor; Kinematics; Learning algorithms; Logistic Models; Machine Learning; Male; Mathematical models; Mental depression; Mental disorders; Mental health; Predictions; Psychiatry; Questionnaires; Skeleton; Suicides & suicide attempts; Support Vector Machine; Depression detection; Kinect sensor; Human skeleton joints; Machine learning
• DOI: 10.1186/s12888-021-03184-4
• ISSN: 1471-244X

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. Considering some patients' conditions and current status and refer to psychiatrists' advices, simple and significant designed stimulus task will lead human skeleton data collection job. With original Kinect skeleton data extracting and preprocessing, the proposed experiment demonstrated four strong machine learning tools: Support Vector Machine, Logistic Regression, Random Forest and Gradient Boosting. Using the precision, recall, sensitivity, specificity, roc-curve, confusion matrix et.al, indicators were calculated as the measurement of methods, which were commonly used to evaluate classification methodologies. Across screened 64 pairs with age and gender totally matching in depression and control group, and Gradient Boosting achieved the best performance with the prediction accuracy of 76.92%. Sorted by female (54.69%) and male for the gender-based depression recognition, we applied best performance classifier Gradient Boosting got prediction accuracy of 66.67% in the male group, and 71.73% in the female group. Utilizing the best model Gradient Boosting for age-based classification, prediction accuracy got 76.92% in the older group (age >40, 50% of total) and 53.85% accuracy in the younger group (age <= 40). The depression and non-depression individuals can be well classified by computational models using Kinect captured skeletal data. The Gradient Boosting, an excellent machine learning tool, get the performance in the four methods we demonstrated. Meanwhile, in the gender-based depression classification also gets reasonable accuracy. In particular, the recognition results of the old group are significantly better than that of the young group. All these findings suggest that kinematic skeletal data based depression recognition can be applied as an effective tool for assisting in depression analysis.