  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.
