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Related Experiment Video

Updated: Jul 8, 2025

Design and Analysis for Fall Detection System Simplification
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A Multimodal Dataset for Automatic Edge-AI Cough Detection.

Lara Orlandic, Jerome Thevenot, Tomas Teijeiro

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    Summary

    This study introduces a new multimodal biosignal dataset for accurate, private, on-device cough counting. This enables the development of edge AI algorithms for continuous monitoring of chronic cough patients.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Accurate cough counting is crucial for evaluating antitussive therapies and personalizing patient care.
    • Existing automatic cough counting tools often suffer from inaccuracies, privacy concerns due to offline processing, or high computational demands unsuitable for wearable devices.
    • There is a need for privacy-preserving, edge AI solutions for accurate, on-device cough counting using multimodal sensors.

    Purpose of the Study:

    • To introduce the first publicly accessible dataset of multimodal biosignals for cough counting.
    • To facilitate the development of machine learning algorithms for automatic cough detection on wearable devices.
    • To demonstrate the feasibility of edge AI for real-time, privacy-preserving cough monitoring.

    Main Methods:

    • Collected multimodal biosignal data including acoustic and kinematic signals.
    • Annotated approximately 4,300 cough events from 15 subjects, alongside non-cough sounds and daily activity motion scenarios.
    • Validated the dataset's technical characteristics, including signal-to-noise ratios and consistency across trials.

    Main Results:

    • The dataset comprises nearly 4 hours of multimodal biosignal data, representing diverse real-life conditions.
    • Technical validation confirmed the dataset's suitability for developing robust cough counting algorithms.
    • A simple classifier trained on the dataset achieved 91% sensitivity, 92% specificity, and 80% precision on unseen data.

    Conclusions:

    • The developed multimodal dataset and edge AI approach enable accurate, private, and continuous ambulatory cough monitoring.
    • This resource accelerates the development of wearable solutions for chronic cough patients.
    • Edge AI algorithms are a promising direction for personalized healthcare and treatment efficacy assessment.