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Integrating Gait With Multilevel Scale Representations for Depression Severity Prediction.

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    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
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    This study introduces a new method combining gait analysis and depression scale content for better mental health assessment. Integrating semantic information from scales with gait data significantly improves depression severity prediction.

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    Area of Science:

    • Mental Health
    • Biomedical Engineering
    • Computational Neuroscience

    Background:

    • Depression assessment often relies on subjective self-rating scales.
    • Gait analysis offers an objective, non-intrusive method for evaluating mental state.
    • Previous research has not fully utilized the semantic content of depression scales.

    Purpose of the Study:

    • To develop a novel framework for depression severity prediction.
    • To integrate gait features with multilevel semantic content from depression scales.
    • To enhance the accuracy and tailoring of depression assessment.

    Main Methods:

    • Modeled scale content into individual-independent and individual-specific components.
    • Employed a cross-attention mechanism to fuse general symptom descriptions with gait features.
    • Aligned personalized response content with gait features for tailored prediction.

    Main Results:

    • The proposed framework significantly improved depression severity prediction.
    • Integration of scale content with gait features yielded a 6.74% increase in Concordance Correlation Coefficient.
    • Performance enhancement was observed compared to gait-only models.

    Conclusions:

    • Combining gait analysis with the semantic content of depression scales offers a more comprehensive approach to assessment.
    • The multilevel semantic modeling and tailored fusion strategies are effective.
    • This integrated method shows promise for objective and personalized mental health evaluation.