Evidence map›Paper›PMID 40751045›Full record

ArticleScientific reports2025

Detection of vital signs based on millimeter wave radar.

Zhanjun Hao, Yue Wang, Fenfang Li, Guozhen Ding, Kai Fan, Yifei Gao

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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.

Zhanjun HaoCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China. haozhj@nwnu.edu.cn.
Yue WangCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China.
Fenfang LiCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China.
Guozhen DingCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China.
Kai FanCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China.
Yifei GaoCollege of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, China.

Funding

Gansu Provincial Department of Education: Industry Support Program Project 2022CYZC-12Lanzhou City Talent Innovation and Entrepreneurship Project 2021-RC-81Major Science and Technology Projects of Gansu 23ZDGA009National Natural Science Foundation of China Grant 62262061Northwest Normal University Young Teachers Research Ability Enhancement Program Project NWNU--LKQN2019--28Science and Technology Commissioner Special Project of Gansu 23CXGA0086
6 · The paper itself

Abstract

With the growing demand for health monitoring, non-contact vital signs monitoring technology has garnered widespread attention. While traditional health monitoring methods are accurate, they have limitations in terms of non-contact and non-invasive capabilities. This paper proposes a non-contact vital signs monitoring method based on frequency modulated continuous wave (FMCW) millimeter-wave radar, named MRVS, to enhance both convenience and accuracy. The method consists of three steps: signal processing, decomposition, and reconstruction. Firstly, the millimeter-wave radar is used to detect chest movements, extracting both respiration and heartbeat signals. Then, combining signal superposition with phase difference techniques effectively eliminates static clutter and respiratory harmonic interference, enhancing the signal. Next, discrete wavelet transform (DWT) is utilized to suppress clutter and noise further, performing signal decomposition. The reconstruction module employs an adaptive Kalman filter (AKF) combined with square root normalization for accurate heart rate estimation. Experimental results demonstrate that this method achieves an estimation error rate of less than 7% under different distances, angles, and postures, showcasing high accuracy and robustness and providing a new solution for non-contact vital signs monitoring.

Indexed as

RadarVital SignsAlgorithmsHeart RateHumansMonitoring, PhysiologicRespirationSignal Processing, Computer-AssistedWavelet AnalysisAKFDWTHeart rate estimationMillimeter-wave radarVital signs

Identifiers

PMID40751045
PMCPMC12317012

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Registered trials

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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.