Evidence map›Paper›PMID 36560066›Full record

ArticleSensors (Basel, Switzerland)2022

Analysis of Signal Processing Methods to Reject the DC Offset Contribution of Static Reflectors in FMCW Radar-Based Vital Signs Monitoring.

Marco Mercuri, Tom Torfs, Maxim Rykunov, Stefano Laureti, Marco Ricci, Felice Crupi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Marco MercuriDipartimento di Informatica, Modellistica, Elettronica e Sistemistica (DIMES), University of Calabria, 87036 Rende, Italy.ORCID 0000-0002-2384-1107
Tom TorfsImec, 3001 Leuven, Belgium.ORCID 0000-0002-1302-3346
Maxim RykunovImec, 3001 Leuven, Belgium.
Stefano LauretiDipartimento di Informatica, Modellistica, Elettronica e Sistemistica (DIMES), University of Calabria, 87036 Rende, Italy.ORCID 0000-0002-7283-2004
Marco RicciDipartimento di Informatica, Modellistica, Elettronica e Sistemistica (DIMES), University of Calabria, 87036 Rende, Italy.ORCID 0000-0003-1938-1758
Felice CrupiDipartimento di Informatica, Modellistica, Elettronica e Sistemistica (DIMES), University of Calabria, 87036 Rende, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Frequency-modulated continuous wave (FMCW) radars are currently being investigated for remote vital signs monitoring (measure of respiration and heart rates) as an innovative wireless solution for healthcare and ambient assisted living. However, static reflectors (furniture, objects, stationary body parts, etc.) within the range or range angular bin where the subject is present contribute in the Doppler signal to a direct current (DC) offset. The latter is added to the person's information, containing also a useful DC component, causing signal distortion and hence reducing the accuracy in measuring the vital sign parameters. Removing the sole contribution of the unwanted DC offset is fundamental to perform proper phase demodulation, so that accurate vital signs monitoring can be achieved. In this work, we analyzed different DC offset calibration methods to determine which one achieves the highest accuracy in measuring the physiological parameters as the transmitting frequency varies. More precisely, by using two FMCW radars, operating below 10 GHz and at millimeter wave (mmWave), we applied four DC offset calibration methods to the baseband radar signals originated by the cardiopulmonary activities. We experimentally determined the accuracy of the methods by measuring the respiration and the heart rates of different subjects in an office setting. It was found that the linear demodulation outperforms the other methods if operating below 10 GHz while the geometric fitting provides the best results at mmWave.

Indexed as

RadarSignal Processing, Computer-AssistedAlgorithmsHeart RateHumansMonitoring, PhysiologicRespirationVital SignsDC offset calibrationDopplerFMCWheart ratemmWavephase demodulationremote radar sensingrespiration ratesub-10 GHz radarvital signs monitoring

Identifiers

PMID36560066
PMCPMC9781610

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.