Evidence map›Paper›PMID 37447629›Full record

ArticleSensors (Basel, Switzerland)2023

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar.

Jingwen Zhang, Qingjie Qi, Huifeng Cheng, Lifeng Sun, Siyun Liu, Yue Wang, Xinlei Jia

Open access · goldAbstract read
In one paragraph

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.3field-weighted citation impact, top 12% of its field
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, 21 citations in OpenAlex.

  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

7 authors at 1 institution in 1 country.

Jingwen ZhangEmergency Research Institute, Chinese Institute of Coal Science CICS, Beijing 100013, China.
Qingjie QiEmergency Research Institute, Chinese Institute of Coal Science CICS, Beijing 100013, China.
Huifeng ChengEmergency Research Institute, Chinese Institute of Coal Science CICS, Beijing 100013, China.
Lifeng SunEmergency Research Institute, Chinese Institute of Coal Science CICS, Beijing 100013, China.
Siyun LiuEmergency Research Institute, Chinese Institute of Coal Science CICS, Beijing 100013, China.ORCID 0000-0003-1899-0885
Yue WangEmergency Research Institute, Chinese Institute of Coal Science CICS, Beijing 100013, China.
Xinlei JiaEmergency Research Institute, Chinese Institute of Coal Science CICS, Beijing 100013, China.
China Coal Research Institute (China) · CN

Funding

Chinese Institute of Coal Science Co. , Ltd 2021-KXYJ-006
6 · The paper itself

Abstract

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE-K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD-WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO-SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

Indexed as

cluster analysisensemble empirical mode decomposition (EEMD)life detectionpermutation entropy (PE)stochastic resonance (SR)ultra-wideband radar (UWB)vital sign extraction

Identifiers

PMID37447629
PMCPMC10346925
OpenAlexW4381663220

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.