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A TinyML Motion-Based Embedded Cough Detection System.

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    This study introduces a wearable Tiny Machine Learning (TinyML) system for cough detection using accelerometer data. The system achieves high accuracy, enabling on-device inference for respiratory disease monitoring.

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    Area of Science:

    • Biomedical Engineering
    • Machine Learning
    • Wearable Technology
    • Respiratory Health

    Background:

    • Cough detection is crucial for monitoring chronic respiratory diseases.
    • Integrating cough detection algorithms into wearable devices is a key research goal.
    • Existing methods require improvement for efficient, on-device implementation.

    Purpose of the Study:

    • To propose a wearable Tiny Machine Learning (TinyML) cough detection system.
    • To utilize accelerometer motion data for cough event identification.
    • To enable on-device inference for real-time respiratory monitoring.

    Main Methods:

    • Collected accelerometry data from 5 subjects using a Nordic Thingy:53 IoT platform.
    • Pre-processed signals and extracted 18 time-domain features.
    • Trained a two-hidden-layer neural network; deployed models using Edge Impulse on nRF5340 SoC.

    Main Results:

    • Achieved high performance: 94.38% accuracy, 93.92% sensitivity, 94.84% specificity, 94.35% F1 score.
    • Quantized models achieved inference within 1 ms.
    • EON compiler model showed superior resource efficiency (1.4 KB RAM, 14.8 KB Flash) compared to TFLite.

    Conclusions:

    • The proposed TinyML system effectively detects coughs using wearable accelerometer data.
    • On-device deployment is feasible with high accuracy and low latency.
    • The system offers a promising solution for remote and continuous respiratory health monitoring.