Evaluating the severity of depressive symptoms using upper body motion captured by RGB-depth sensors and machine learning in a clinical interview setting: A preliminary study

Toshiro Horigome; Brian Sumali; Momoko Kitazawa; Michitaka Yoshimura; Kuo-Ching Liang; Yuki Tazawa; Takanori Fujita; Masaru Mimura; Taishiro Kishimoto

doi:10.1016/j.comppsych.2020.152169

Evaluating the severity of depressive symptoms using upper body motion captured by RGB-depth sensors and machine learning in a clinical interview setting: A preliminary study

Compr Psychiatry. 2020 Feb 20:98:152169. doi: 10.1016/j.comppsych.2020.152169. Online ahead of print.

Authors

Toshiro Horigome¹, Brian Sumali², Momoko Kitazawa¹, Michitaka Yoshimura¹, Kuo-Ching Liang¹, Yuki Tazawa¹, Takanori Fujita³, Masaru Mimura¹, Taishiro Kishimoto⁴

Affiliations

¹ Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
² Department of System Design Engineering, Keio University, Kanagawa, Japan.
³ Department of Health Policy and Management, Keio University School of Medicine, Tokyo, Japan.
⁴ Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan. Electronic address: tkishimoto@keio.jp.

PMID: 32145559
DOI: 10.1016/j.comppsych.2020.152169

Abstract

Background: Mood disorders have long been known to affect motor function. While methods to objectively assess such symptoms have been used in experiments, those same methods have not yet been applied in clinical practice because the methods are time-consuming, labor-intensive, or invasive.

Methods: We videotaped the upper body of each subject using a Red-Green-Blue-Depth (RGB-D) sensor during a clinical interview setting. We then examined the relationship between depressive symptoms and body motion by comparing the head motion of patients with major depressive disorders (MDD) and bipolar disorders (BD) to the motion of healthy controls (HC). Furthermore, we attempted to predict the severity of depressive symptoms by using machine learning.

Results: A total of 47 participants (HC, n = 16; MDD, n = 17; BD, n = 14) participated in the study, contributing to 144 data sets. It was found that patients with depression move significantly slower compared to HC in the 5th percentile and 50th percentile of motion speed. In addition, Hamilton Depression Rating Scale (HAMD)-17 scores correlated with 5th percentile, 50th percentile, and mean speed of motion. Moreover, using machine learning, the presence and/or severity of depressive symptoms based on HAMD-17 scores were distinguished by a kappa coefficient of 0.37 to 0.43.

Limitations: Limitations include the small number of subjects, especially the number of severe cases and young people.

Conclusions: The RGB-D sensor captured some differences in upper body motion between depressed patients and controls. If much larger samples are accumulated, machine learning may be useful in identifying objective measures for depression in the future.

Keywords: Depression; Machine learning; Psychomotor agitation; Psychomotor retardation; RGB-Depth sensor; Upper body motion.