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Related Experiment Video

Updated: Jun 26, 2025

Adapting Human Videofluoroscopic Swallow Study Methods to Detect and Characterize Dysphagia in Murine Disease Models
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sEMG-based automatic characterization of swallowed materials.

Eman A Hassan1, Yassin Khalifa2,3,4, Ahmed A Morsy2

  • 1Biomedical Engineering Dept., Cairo University, Giza, Egypt. eman.ayman@eng1.cu.edu.eg.

Biomedical Engineering Online
|May 17, 2024
PubMed
Summary
This summary is machine-generated.

Automated methods using wireless surface electromyography (sEMG) and IMU sensors accurately detect and quantify eating and swallowing activities. This non-invasive approach aids in managing chronic health conditions through ingestive behavior monitoring.

Keywords:
Bolus volumeClassificationIMUSwallowingWeight managementsEMG

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Area of Science:

  • Biomedical Engineering
  • Physiology
  • Machine Learning

Background:

  • Monitoring ingestive activities is crucial for managing health conditions like diabetes and obesity.
  • Swallowing is a complex physiological process vital for survival.
  • Current monitoring methods can be invasive or lack precision.

Purpose of the Study:

  • To develop automated, non-invasive methods for detecting and quantifying eating and drinking activities.
  • To utilize wireless surface electromyography (sEMG) and IMU sensors for ingestive behavior analysis.
  • To establish a foundation for cost-effective health management tools.

Main Methods:

  • Wireless surface electromyography (sEMG) signals from the sternocleidomastoid muscle and wrist-mounted IMU data were collected.
  • 4675 swallows from 55 participants were analyzed.
  • Machine learning models (neural networks, SVM, decision trees) were employed for detection, classification, and volume estimation.

Main Results:

  • High accuracy (R²=0.88, RMSE=0.2) was achieved in estimating fluid bolus volumes using neural network regression.
  • Convolutional neural networks achieved >99% accuracy in classifying bolus volumes.
  • Classification of solid bolus type and estimation of solid bolus weight demonstrated high accuracy (>94% cross-subject) and precision (RMSE as low as 0.00037).

Conclusions:

  • The study presents a cost-effective, non-invasive method for monitoring swallowing and ingestive activities.
  • This technology can significantly benefit individuals with chronic health conditions requiring ingestive behavior management.
  • The developed automated methods provide a foundation for advanced health and wellness monitoring systems.