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Deep Neural Networks for Image-Based Dietary Assessment
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In-Bed Posture Classification Using Deep Neural Network.

Lindsay Stern1,2, Atena Roshan Fekr1,2

  • 1Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.

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|March 11, 2023
PubMed
Summary

This study introduces 2D and 3D Convolutional Neural Networks for in-bed posture recognition using body heat maps. The models accurately detect patient positions, aiding in preventing pressure ulcers and improving sleep quality monitoring.

Keywords:
classificationdeep learningposturespressure ulcer

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Area of Science:

  • Medical Informatics
  • Computer Vision
  • Artificial Intelligence

Background:

  • In-bed posture monitoring is crucial for preventing pressure ulcers and enhancing sleep quality.
  • Current methods require effective automated systems for continuous patient assessment.

Purpose of the Study:

  • To develop and compare 2D and 3D Convolutional Neural Networks (CNNs) for in-bed posture recognition.
  • To evaluate the effectiveness of image versus video data for posture classification.
  • To address dataset imbalance using sampling strategies and class weights.

Main Methods:

  • Utilized an open-access dataset of body heat maps from 13 subjects in 17 positions.
  • Trained 2D CNNs (including ResNet-18) and 3D CNNs on image and video data, respectively.
  • Employed down-sampling, over-sampling, and class weights to handle imbalanced data.

Main Results:

  • The best 3D model achieved high accuracy (98.90% 5-fold, 97.80% LOSO).
  • The best 2D model (ResNet-18) demonstrated superior performance (99.97% 5-fold, 99.62% LOSO).
  • Both 2D and 3D models showed promising results for in-bed posture recognition.

Conclusions:

  • The developed CNN models offer effective solutions for automated in-bed posture recognition.
  • These systems can assist caregivers in preventing pressure ulcers and monitoring patient sleep quality.
  • Further research can refine models for more detailed posture subclassifications.