Data Augmentation for Depression Detection Using Skeleton-Based Gait Information

TL;DR

This paper introduces a skeleton data augmentation approach for depression detection via gait analysis, significantly improving detection accuracy by applying specific augmentation techniques to enhance limited datasets.

Contribution

The study proposes five novel skeleton data augmentation methods and analyzes their effectiveness for depression screening, addressing data scarcity issues in gait-based depression detection.

Findings

Rotation augmentation achieves 92.15% accuracy.

Channel mask augmentation reaches 91.34% accuracy.

Augmentation strategies that preserve data properties improve detection performance.

Abstract

In recent years, the incidence of depression is rising rapidly worldwide, but large-scale depression screening is still challenging. Gait analysis provides a non-contact, low-cost, and efficient early screening method for depression. However, the early screening of depression based on gait analysis lacks sufficient effective sample data. In this paper, we propose a skeleton data augmentation method for assessing the risk of depression. First, we propose five techniques to augment skeleton data and apply them to depression and emotion datasets. Then, we divide augmentation methods into two types (non-noise augmentation and noise augmentation) based on the mutual information and the classification accuracy. Finally, we explore which augmentation strategies can capture the characteristics of human skeleton data more effectively. Experimental results show that the augmented training data set that retains more of the raw skeleton data properties determines the performance of the detection model. Specifically, rotation augmentation and channel mask augmentation make the depression detection accuracy reach 92.15% and 91.34%, respectively.