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Related Experiment Video

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An interpretable framework for sleep posture change detection and postural inactivity segmentation using wrist

Omar Elnaggar1, Roselina Arelhi2, Frans Coenen3

  • 1School of Engineering, University of Liverpool, Liverpool, L69 3GH, UK.

Scientific Reports
|October 21, 2023
PubMed
Summary

This study introduces a new wearable sensor framework to accurately detect sleep posture changes and inactivity. This technology offers reliable, home-based sleep movement analysis for better patient-centred care.

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Sleep Medicine

Background:

  • Sleep posture and movements are vital indicators of neurophysiological health and quality of life.
  • Current clinical sleep assessment methods like polysomnography are invasive and resource-intensive.
  • Wearable sensor technologies offer less invasive alternatives, but reliability and standardized algorithms remain challenges.

Purpose of the Study:

  • To develop and evaluate a comprehensive framework for objective sleep posture change detection and postural inactivity segmentation.
  • To utilize clinically relevant joint kinematics measured by a custom wearable sensor.
  • To provide an explainable framework for potential clinical monitoring and diagnosis.

Main Methods:

  • Development of a comprehensive framework using a custom-made wearable sensor to capture joint kinematics.
  • Application of dimension reduction for intuitive 3D visualization of kinematic time series.
  • Evaluation of the framework on wrist kinematic data from five healthy participants during simulated sleep.

Main Results:

  • The proposed framework achieved up to 99.2% F1-score for sleep posture detection.
  • A Pearson's correlation coefficient of 0.96 was achieved for temporal segmentation of postural inactivity.
  • The framework demonstrated intuitive 3D visualizations and explainability through dimension reduction.

Conclusions:

  • The developed framework enables reliable, objective analysis of sleep posture and movement.
  • This technology supports home-based sleep movement analysis for patient-centred longitudinal care.
  • The explainable nature of the framework may aid clinical monitoring and diagnosis.