ArticleSensors (Basel, Switzerland)2023
Convolutional Neural Networks for the Real-Time Monitoring of Vital Signs Based on Impulse Radio Ultrawide-Band Radar during Sleep.
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 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Radar Multiple Bin Selection for Breathing and Heart Rate Monitoring in Acute Stroke Patients in a Clinical Setting.Sensors (Basel, Switzerland) · 2025Article
- Biomedical Signal Processing and Health Monitoring Based on Sensors.Sensors (Basel, Switzerland) · 2025Article
- Sex and age estimation from cardiac signals captured via radar using data augmentation and deep learning: a privacy concern.Frontiers in digital health · 2025Article
- Millimeter-Wave Radar-Based Identity Recognition Algorithm Built on Multimodal Fusion.Sensors (Basel, Switzerland) · 2024Article
- HomeOSD: Appliance Operating-Status Detection Using mmWave Radar.Sensors (Basel, Switzerland) · 2024Article
- Heart Rate Variability Monitoring Based on Doppler Radar Using Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- Contactless Heart and Respiration Rates Estimation and Classification of Driver Physiological States Using CW Radar and Temporal Neural Networks.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Vital signs provide important biometric information for managing health and disease, and it is important to monitor them for a long time in a daily home environment. To this end, we developed and evaluated a deep learning framework that estimates the respiration rate (RR) and heart rate (HR) in real time from long-term data measured during sleep using a contactless impulse radio ultrawide-band (IR-UWB) radar. The clutter is removed from the measured radar signal, and the position of the subject is detected using the standard deviation of each radar signal channel. The 1D signal of the selected UWB channel index and the 2D signal applied with the continuous wavelet transform are entered as inputs into the convolutional neural-network-based model that then estimates RR and HR. From 30 recordings measured during night-time sleep, 10 were used for training, 5 for validation, and 15 for testing. The average mean absolute errors for RR and HR were 2.67 and 4.78, respectively. The performance of the proposed model was confirmed for long-term data, including static and dynamic conditions, and it is expected to be used for health management through vital-sign monitoring in the home environment.
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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 Socratic graph.