ArticleSensors (Basel, Switzerland)2023
Vision Transformers (ViT) for Blanket-Penetrating Sleep Posture Recognition Using a Triple Ultra-Wideband (UWB) Radar System.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Deep Learning Method for Human Sleeping Pose Estimation with Millimeter Wave Radar.Sensors (Basel, Switzerland) · 2024Article
- Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM).Sensors (Basel, Switzerland) · 2024Article
- Smart Sleep Monitoring: Sparse Sensor-Based Spatiotemporal CNN for Sleep Posture Detection.Sensors (Basel, Switzerland) · 2024Article
- A smart detection method for sleep posture based on a flexible sleep monitoring belt and vital sign signals.Heliyon · 2024Article
- A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring.IEEE open journal of engineering in medicine and biology · 2024Article
- A Deep Learning Method of Human Identification from Radar Signal for Daily Sleep Health Monitoring.Bioengineering (Basel, Switzerland) · 2023Article
- Assessing Non-Specific Neck Pain through Pose Estimation from Images Based on Ensemble Learning.Life (Basel, Switzerland) · 2023Article
- Research on Diesel Engine Fault Status Identification Method Based on Synchro Squeezing S-Transform and Vision Transformer.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.