Dual ultra-wideband (UWB) radar-based sleep posture recognition system: Towards ubiquitous sleep monitoring

Derek Ka Hei Lai, Li Wen Zha, Tommy Yau Nam Leung, Andy Yiu Chau Tam, Bryan Pak Hei So, Hyo Jung Lim, Daphne Sze Ki Cheung, Duo Wai Chi Wong, James Chung Wai Cheung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

Sleep posture monitoring is an essential assessment for obstructive sleep apnea (OSA) patients. The objective of this study is to develop a machine learning-based sleep posture recognition system using a dual ultra-wideband radar system. We collected radiofrequency data from two radars positioned over and at the side of the bed for 16 patients performing four sleep postures (supine, left and right lateral, and prone). We proposed and evaluated deep learning approaches that streamlined feature extraction and classification, and the traditional machine learning approaches that involved different combinations of feature extractors and classifiers. Our results showed that the dual radar system performed better than either single radar. Predetermined statistical features with random forest classifier yielded the best accuracy (0.887), which could be further improved via an ablation study (0.938). Deep learning approach using transformer yielded accuracy of 0.713.

Original languageEnglish
Pages (from-to)36-43
Number of pages8
JournalEngineered Regeneration
Volume4
Issue number1
DOIs
Publication statusPublished - 26 Nov 2022

Keywords

  • Ablation study
  • Deep learning
  • Feature extraction
  • Obstructive sleep apnea
  • Sleep monitoring

ASJC Scopus subject areas

  • Biomedical Engineering
  • Medicine (miscellaneous)
  • Biomaterials

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