Spatio-temporal Multi-granularity for Skeleton-based Depression Risk Recognition.

Abstract

As the prevalence of depression continues to rise, the timely and accurate recognition of its early signs is crucial for effective prevention and intervention. However, current clinical diagnostic methods are limited by the absence of objective biomarkers and inefficiencies in early recognition. Recent research has revealed a significant correlation between gait patterns and depression risk, suggesting that gait analysis could serve as a promising tool for early diagnosis. Depression-associated gait characteristics are defined by two key aspects: (1) they are dynamic, reflecting temporal abnormalities in movement, and (2) they manifest across both localized body regions and broader global movement patterns of the body. Based on these insights, we propose a novel Spatio-temporal Multi-granularity Network (STM-Net) for depression risk recognition. In the temporal domain, we present a Multi-grain Temporal Focus (MTF) module, designed to capture the rich dynamic temporal information embedded in the gait cycle of individuals with depression. In the spatial domain, we introduce a Multi-grain Spatial Focus (MSF) module, which effectively captures spatial features and their interactions in depression-related body regions through joint-level and part-level attention mechanisms. Extensive experimental results demonstrate that STM-Net achieves state-of-the-art performance on a large open-source dataset.