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    Summary
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    Commercial smart devices show promise for prescreening cognitive impairments like Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) using gait analysis. Wearable sensors detected gait changes, aiding early intervention for those needing treatment.

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    Area of Science:

    • Digital health
    • Biomedical engineering
    • Neuroscience

    Background:

    • Cognitive impairments, including Mild Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and Subjective Cognitive Decline (SCD), require early detection for timely intervention.
    • Distinguishing individuals with MCI or AD from those with SCD using real-world data presents a significant challenge due to overlapping symptoms and uncontrolled environments.

    Purpose of the Study:

    • To investigate the feasibility of using commercial smart devices for prescreening cognitive impairments.
    • To differentiate individuals requiring treatment (AD and MCI) from those without cognitive impairment (SCD) based on gait-related data collected during daily life.

    Main Methods:

    • 125 individuals aged 50+ were recruited and categorized by cognitive diagnosis.
    • Participants used smartphones and smartwatches for one month to collect accelerometer and gyroscope data during daily activities.
    • A deep learning classifier was trained on preprocessed walking segments to distinguish between cognitive impairment groups.

    Main Results:

    • The deep learning model achieved an Area Under the Curve (AUC) of 0.70 for cognitive impairment detection.
    • A sensor ablation study indicated that wrist-worn gyroscope data alone yielded a comparable AUC of 0.70.
    • Integrating smartphone accelerometers and smartwatch gyroscopes improved classification performance.

    Conclusions:

    • Commercial smart devices and wearable-based gait analysis show potential for prescreening cognitive impairments.
    • Gait analysis using accessible technology can aid in the early detection of conditions like AD and MCI.
    • Future research will focus on multimodal data fusion to enhance the accuracy and reliability of cognitive impairment screening tools.