论文标题
使用基于骨架的步态信息进行抑郁检测的数据增强
Data Augmentation for Depression Detection Using Skeleton-Based Gait Information
论文作者
论文摘要
近年来，抑郁症的发病率在全球范围内迅速上升，但大规模的抑郁症筛查仍然具有挑战性。步态分析为抑郁症提供了一种非接触，低成本和有效的早期筛查方法。但是，基于步态分析的抑郁症的早期筛查缺乏足够的有效样本数据。在本文中，我们提出了一种评估抑郁症风险的骨骼数据增强方法。首先，我们提出了五种技术来增强骨骼数据并将其应用于抑郁和情感数据集中。然后，我们根据相互信息和分类准确性将增强方法分为两种类型（非噪声增强和噪音增强）。最后，我们探索哪些增强策略可以更有效地捕获人类骨骼数据的特征。实验结果表明，保留更多原始骨骼数据属性的增强训练数据集决定了检测模型的性能。具体而言，旋转增强和通道掩模的增强使抑郁症检测精度分别达到92.15％和91.34％。
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.
