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Related Experiment Video

Updated: Jan 20, 2026

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Joint Audio-Ultrasound Food Recognition for Noisy Environments.

Ki-Seung Lee

    IEEE Journal of Biomedical and Health Informatics
    |September 5, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an automatic food recognition system using audio and ultrasonic signals for obesity screening. The novel method achieves high accuracy, even in noisy conditions, by adaptively weighting signal inputs.

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    Area of Science:

    • Biomedical Engineering
    • Signal Processing
    • Artificial Intelligence

    Background:

    • Continuous food recognition aids in pre-screening for obesity and diet-related diseases.
    • Existing methods often rely on user input or are sensitive to environmental noise.

    Purpose of the Study:

    • To develop an automatic food recognition system combining audio and ultrasonic signals.
    • To improve recognition accuracy in various noise conditions for dietary monitoring.

    Main Methods:

    • A multimodal approach integrating audio and ultrasonic (US) signal classification.
    • A novel weighting strategy adaptively adjusts signal contributions based on signal-to-noise ratio (SNR).
    • Determining optimal SNR classification boundaries and stream weights for each SNR class.

    Main Results:

    • The proposed audio-US method achieved 90.13% accuracy with artificial noise and 89.67% in real noisy environments (0-20 dB SNR).
    • Performance was comparable or superior to conventional in-ear and throat microphone methods.
    • US recognizer performance remained stable regardless of acoustic noise levels.

    Conclusions:

    • The adaptive audio-US food recognition system demonstrates robust performance in diverse acoustic environments.
    • This technology offers a promising non-invasive tool for continuous dietary intake monitoring and health pre-screening.