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

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.

Keywords

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