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Related Experiment Video

Updated: Jan 9, 2026

Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models
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A swallowing assessment system based on dual-position multi-source sensing fusion.

Ying Zhang, Ping Wang, Junjie Li

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    This study introduces a smart system using wearable sensors to detect swallowing problems early. It combines data from inertial measurement units (IMU) and photoplethysmography (PPG) for accurate daily swallowing assessment.

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    Area of Science:

    • Biomedical Engineering
    • Wearable Technology
    • Signal Processing

    Background:

    • Traditional swallowing disorder tests require specialized clinical settings, limiting early detection.
    • Intelligent swallowing research using multi-source sensing fusion offers a potential solution for daily pre-screening.

    Purpose of the Study:

    • To develop and validate a novel swallowing assessment system integrating wireless sensing and multi-source data fusion.
    • To evaluate the efficacy of decision-level and feature-level fusion strategies for swallowing disorder detection.

    Main Methods:

    • A wireless platform collecting 6-axis inertial measurement unit (IMU) and dual-channel photoplethysmography (PPG) signals from two body positions.
    • Implementation of both decision-level fusion (weighting models) and feature-level fusion (stacking features) using sensor data.
    • Validation on 21,024 data segments and 219 Water Swallowing Test recordings.

    Main Results:

    • The proposed system achieved average F1 scores of 81.1% (decision-level) and 80.8% (feature-level) fusion, outperforming single-sensor or single-position models.
    • Sensor and position comparisons revealed distinct information contributions, with IMU and central positioning showing advantages.
    • A simplified two-level classification for the Water Swallowing Test yielded accuracies of 81.0% and 71.3%.

    Conclusions:

    • The developed multi-source sensing fusion system effectively monitors swallowing function and enables daily recognition.
    • The system provides a promising approach for early pre-screening of swallowing disorders, particularly for the Water Swallowing Test.