ArticleSensors (Basel, Switzerland)2019
Peak Detection Algorithm for Vital Sign Detection Using Doppler Radar Sensors.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Literature Review Regarding Heart Rate and Respiratory Rate Measurement by Means of Radar Technology.Sensors (Basel, Switzerland) · 2024Pooled it
- Embedded Cardiorespiratory Monitoring Using a 77 GHz FMCW Radar: On-Chip Multi-Timescale Tiny 1D CNN and Whole-Node Power Evaluation.Sensors (Basel, Switzerland) · 2026Article
- Motion Cancellation Technique of Vital Signal Detectors Based on Continuous-Wave Radar Technology.Sensors (Basel, Switzerland) · 2025Review
- Radar-Based Heart Cardiac Activity Measurements: A Review.Sensors (Basel, Switzerland) · 2024Review
- Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System.Sensors (Basel, Switzerland) · 2024Article
- A Real-Time Evaluation Algorithm for Noncontact Heart Rate Variability Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Non-Contact VITAL Signs Monitoring of a Patient Lying on Surgical Bed Using Beamforming FMCW Radar.Sensors (Basel, Switzerland) · 2022Article
- Vital Signal Detection Using Multi-Radar for Reductions in Body Movement Effects.Sensors (Basel, Switzerland) · 2021Article
- Detrending Technique for Denoising in CW Radar.Sensors (Basel, Switzerland) · 2021Article
- Non-Contact Monitoring of Human Vital Signs Using FMCW Millimeter Wave Radar in the 120 GHz Band.Sensors (Basel, Switzerland) · 2021Article
- Machine Learning-Based Human Recognition Scheme Using a Doppler Radar Sensor for In-Vehicle Applications.Sensors (Basel, Switzerland) · 2020Article
- Vital-Signs Detector Based on Frequency-Shift Keying Radar.Sensors (Basel, Switzerland) · 2020Article
- Vital Sign Detection during Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor.Sensors (Basel, Switzerland) · 2020Article
- An FMCW Radar for Localization and Vital Signs Measurement for Different Chest Orientations.Sensors (Basel, Switzerland) · 2020Article
- A Novel Signal Separation and De-Noising Technique for Doppler Radar Vital Signal Detection.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
An accurate method for detecting vital signs obtained from a Doppler radar sensor is proposed. A Doppler radar sensor can remotely obtain vital signs such as heartbeat and respiration rate, but the vital signs obtained by using the sensor do not show clear peaks like in electrocardiography (ECG) because of the operating characteristics of the radar. The proposed peak detection algorithm extracts the vital signs from the raw data. The algorithm shows the mean accuracy of 96.78% compared to the peak count from the reference ECG sensor and a processing time approximately two times faster than the gradient-based algorithm. To verify whether heart rate variability (HRV) analysis similar to that with an ECG sensor is possible for a radar sensor when applying the proposed method, the continuous parameter variations of the HRV in the time domain are analyzed using data processed with the proposed peak detection algorithm. Experimental results with six subjects show that the proposed method can obtain the heart rate with high accuracy but cannot obtain the information for an HRV analysis because the proposed method cannot overcome the characteristics of the radar sensor itself.
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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.