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Human sleep position classification using a lightweight model and acceleration data.

Hoang-Dieu Vu1,2, Duc-Nghia Tran3, Huy-Hieu Pham4

  • 1Faculty of Electrical and Electronic Engineering, Phenikaa University, Yen Nghia, Hanoi, 12116, Vietnam.

Sleep & Breathing = Schlaf & Atmung
|February 10, 2025
PubMed
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This summary is machine-generated.

A new wearable device accurately monitors sleep positions using a deep learning model. This technology aids individuals with conditions like GERD in improving sleep quality and managing symptoms at home.

Area of Science:

  • Biomedical Engineering
  • Wearable Technology
  • Machine Learning for Health

Background:

  • Sleep posture significantly impacts conditions like gastroesophageal reflux disease (GERD).
  • Current sleep monitoring often requires inconvenient hospital-based settings.
  • Home-based monitoring solutions are needed for continuous, non-invasive tracking.

Purpose of the Study:

  • To introduce a portable, wearable device for monitoring twelve sleep positions using a single accelerometer.
  • To develop a system for home use to assist patients with GERD and similar conditions.
  • To improve sleep quality and reflux symptoms through personalized sleep habit tracking.

Main Methods:

  • Development of AnpoNet, a deep learning model combining 1D-CNN and LSTM with BN and Dropout.
Keywords:
Accelerometer-based wearableDeep learning classificationGERDPositional therapySleep position monitoringSleep posture

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  • Recording sleep position data from 15 participants at 50 Hz.
  • Evaluation using 5-Fold cross-validation and testing on unseen participants for generalization.

    • Main Results:

    • AnpoNet achieved high classification accuracy (94.67% ± 0.80%) and F1-score (92.94% ± 1.35%).
    • The model demonstrated robustness through rigorous cross-validation and testing.
    • Performance surpassed baseline models, indicating suitability for real-world applications.

    Conclusions:

    • The study establishes a foundation for a portable, non-invasive sleep posture monitoring system for home use.
    • The device shows potential for improving sleep quality and supporting positional therapy for GERD patients.
    • Future work will involve larger studies and enhanced user interfaces for wider adoption.