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A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software
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S2VQ-VAE: Semi-Supervised Vector Quantised-Variational AutoEncoder for Automatic Evaluation of Trail Making Test.

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    This study introduces a new deep learning method for detecting cognitive impairment in older adults using digital device data. The approach enhances screening accuracy and efficiency, aiding community-based health assessments.

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    Area of Science:

    • Digital Health
    • Neuroscience
    • Artificial Intelligence

    Background:

    • Computer-aided detection of cognitive impairment is crucial for community-dwelling older adults.
    • Multimodal sensing technology on digital devices offers objective and convenient cognitive assessments.

    Purpose of the Study:

    • To develop an automated method for screening cognitive impairment using digital TMT data.
    • To enhance cognitive diagnosis capabilities for large-scale community screening.

    Main Methods:

    • Proposed a novel Semi-Supervised Vector Quantised-Variational AutoEncoder (S2VQ-VAE) for deep representation learning.
    • Incorporated intra- and inter-class correlation losses for disentangling factors.
    • Combined disentangled factors with demographic, time, pressure, and jerk features.
    • Utilized a light gradient boosting machine as the optimal classifier.

    Main Results:

    • The proposed multi-type feature fusion method demonstrated superior screening performance.
    • Outperformed conventional paper-based TMT methods and existing VAE-based feature extraction.

    Conclusions:

    • The deep representation learning method significantly improves cognitive diagnosis for behavior-based TMTs.
    • Streamlines community-based cognitive impairment screening and reduces healthcare staff workload.