Towards More Efficient Depression Risk Recognition via Gait
Min Ren Muchan Tao Xuecai Hu Xiaotong Liu Qiong Li Yongzhen Huang
Depression, a highly prevalent mental illness, affects over 280 million
individuals worldwide. Early detection and timely intervention are crucial for
promoting remission, preventing relapse, and alleviating the emotional and
financial burdens associated with depression. However, patients with depression
often go undiagnosed in the primary care setting. Unlike many physiological
illnesses, depression lacks objective indicators for recognizing depression
risk, and existing methods for depression risk recognition are time-consuming
and often encounter a shortage of trained medical professionals. The
correlation between gait and depression risk has been empirically established.
Gait can serve as a promising objective biomarker, offering the advantage of
efficient and convenient data collection. However, current methods for
recognizing depression risk based on gait have only been validated on small,
private datasets, lacking large-scale publicly available datasets for research
purposes. Additionally, these methods are primarily limited to hand-crafted
approaches. Gait is a complex form of motion, and hand-crafted gait features
often only capture a fraction of the intricate associations between gait and
depression risk. Therefore, this study first constructs a large-scale gait
database, encompassing over 1,200 individuals, 40,000 gait sequences, and
covering six perspectives and three types of attire. Two commonly used
psychological scales are provided as depression risk annotations. Subsequently,
a deep learning-based depression risk recognition model is proposed, overcoming
the limitations of hand-crafted approaches. Through experiments conducted on
the constructed large-scale database, the effectiveness of the proposed method
is validated, and numerous instructive insights are presented in the paper,
highlighting the significant potential of gait-based depression risk
recognition.