ArticleSensors (Basel, Switzerland)2023
Continuous Non-Invasive Blood Pressure Measurement Using 60 GHz-Radar-A Feasibility Study.
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 10 papers.
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Who cites it
10 citing papers in PubMed, 31 citations in OpenAlex.
- Beyond the Cuff: State-of-the-Art on Cuffless Blood Pressure Monitoring.Sensors (Basel, Switzerland) · 2026Review
- Review
- Smart e-Textile Singlet Prototype and Concept: Multi Sensor Sensing for Geriatric Monitoring.Bioengineering (Basel, Switzerland) · 2025Article
- A Novel Wearable Device for Continuous Blood Pressure Monitoring Utilizing Strain Gauge Technology.Biosensors · 2025Article
- A feasibility study on non-invasive and non-contact jugular venous pulse measurement using 60 GHz FMCW radar.Royal Society open science · 2025Article
- Non-Contact Blood Pressure Monitoring Using Radar Signals: A Dual-Stage Deep Learning Network.Bioengineering (Basel, Switzerland) · 2025Article
- Gastroretentive microballoons of olmesartan medoxomil: formulation andTherapeutic delivery · 2025Article
- Accurate Cardiac Duration Detection for Remote Blood Pressure Estimation Using mm-Wave Doppler Radar.Sensors (Basel, Switzerland) · 2025Article
- History and evolution of blood pressure measurement.Clinical hypertension · 2024Review
- PPG Signals-Based Blood-Pressure Estimation Using Grid Search in Hyperparameter Optimization of CNN-LSTM.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Blood pressure monitoring is of paramount importance in the assessment of a human's cardiovascular health. The state-of-the-art method remains the usage of an upper-arm cuff sphygmomanometer. However, this device suffers from severe limitations-it only provides a static blood pressure value pair, is incapable of capturing blood pressure variations over time, is inaccurate, and causes discomfort upon use. This work presents a radar-based approach that utilizes the movement of the skin due to artery pulsation to extract pressure waves. From those waves, a set of 21 features was collected and used-together with the calibration parameters of age, gender, height, and weight-as input for a neural network-based regression model. After collecting data from 55 subjects from radar and a blood pressure reference device, we trained 126 networks to analyze the developed approach's predictive power. As a result, a very shallow network with just two hidden layers produced a systolic error of 9.2±8.3 mmHg (mean error ± standard deviation) and a diastolic error of 7.7±5.7 mmHg. While the trained model did not reach the requirements of the AAMI and BHS blood pressure measuring standards, optimizing network performance was not the goal of the proposed work. Still, the approach has displayed great potential in capturing blood pressure variation with the proposed features. The presented approach therefore shows great potential to be incorporated into wearable devices for continuous blood pressure monitoring for home use or screening applications, after improving this approach even further.
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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.