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

Qiong Li, Min Ren, Xuecai Hu

    IEEE Journal of Biomedical and Health Informatics
    |July 10, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Gait analysis offers a new way to detect depression early. A novel Spatio-temporal Multi-granularity Network (STM-Net) analyzes dynamic gait patterns for improved depression risk recognition.

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    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Computer Science

    Background:

    • Depression prevalence is increasing, necessitating improved early detection methods.
    • Current diagnostic approaches lack objective biomarkers and efficient early recognition.
    • Gait patterns show a significant correlation with depression risk, indicating potential for gait analysis in diagnosis.

    Purpose of the Study:

    • To propose a novel deep learning model for recognizing depression risk using gait analysis.
    • To develop a method that captures both dynamic temporal and spatial gait characteristics associated with depression.

    Main Methods:

    • Introduction of the Spatio-temporal Multi-granularity Network (STM-Net).
    • Development of a Multi-grain Temporal Focus (MTF) module to capture temporal gait dynamics.
    • Development of a Multi-grain Spatial Focus (MSF) module using joint-level and part-level attention for spatial feature extraction.

    Main Results:

    • STM-Net demonstrated state-of-the-art performance in depression risk recognition.
    • The model effectively captured dynamic, spatio-temporal gait abnormalities.
    • Experimental validation was conducted on a large, open-source dataset.

    Conclusions:

    • Gait analysis, particularly using advanced models like STM-Net, shows significant promise for objective and early depression risk recognition.
    • The proposed STM-Net effectively integrates temporal and spatial gait information for enhanced diagnostic accuracy.
    • This approach could lead to more timely interventions and improved outcomes for individuals with depression.