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Ubi-AD: Towards Ubiquitous, Passive Alzheimer Detection using the Smartwatch

Published: 26 August 2024 Publication History

Abstract

Alzheimer’s disease (AD) is an insidious and progressive neurodegenerative disease, and the annual relevant social cost for AD patients can reach about $1 trillion worldwide. Therefore, early diagnosis and treatment of AD play a vital role in slowing disease progression. However, existing detection methods for cognitive impairment cannot consistently screen the stage of AD. To tackle this challenge, we propose an AD detection system, Ubi-AD, which combines the features of multiple biomarkers to realize passive and accurate AD detection. Unlike existing work, Ubi-AD can passively recognize the AD digital biomarkers during daily smartwatch usage without interfering with the user. At the user end, Ubi-AD first extracts the non-speech sounds (pause words, such as em, ah), which contain no privacy-sensitive content. Then, Ubi-AD recognizes the user’s walking activity, dining activity, and sleep activity from daily activities. Ubi-AD analyzes these data from smartwatch and predicts the AD stages using a multi-modal fusion neural network at the cloud end. We evaluate our model on a collected dataset from 45 volunteers. As a result, Ubi-AD can reach a detection accuracy of 93.4%, which means that Ubi-AD can provide multiple effective biomarkers for ubiquitous and passive detection in daily life.

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    Published In

    cover image ACM Transactions on Sensor Networks
    ACM Transactions on Sensor Networks  Volume 20, Issue 5
    September 2024
    349 pages
    EISSN:1550-4867
    DOI:10.1145/3618084
    • Editor:
    • Wen Hu
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

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    Publication History

    Published: 26 August 2024
    Online AM: 03 April 2024
    Accepted: 29 March 2024
    Revised: 18 September 2023
    Received: 08 December 2022
    Published in TOSN Volume 20, Issue 5

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    1. Mobile health
    2. Alzheimer’s disease
    3. passive detection
    4. multi-modal fusion

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