An RFID-Based Method for Multi-Person Respiratory Monitoring.

Chaowei Zang, Chi Zhang, Min Zhang, Qiang Niu

Open access · goldAbstract 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

0.6field-weighted citation impact, top 40% 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, 8 citations in OpenAlex.

Wearable Sensors for Respiration Monitoring: A Review.Sensors (Basel, Switzerland) · 2023
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

4 authors at 1 institution in 1 country.

Chaowei ZangSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.

Chi ZhangSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.

Min ZhangSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.

Qiang NiuSchool of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.

China University of Mining and Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Respiratory monitoring is widely used in the field of health care. Traditional respiratory monitoring methods bring much inconvenience to users. In recent years, a great number of respiratory monitoring methods based on wireless technology have emerged, but multi-person respiratory monitoring is still very challenging; therefore, this paper explores multi-person respiratory monitoring. Firstly, the characteristics of human respiratory movement have been analyzed, and a suitable tag deployment method for respiratory monitoring is proposed. Secondly, aiming at the ambiguity and entanglement of radio frequency identification (RFID) phase data, a method of removal of phase ambiguity and phase wrapping is given. Then, in order to monitor multi-person respiration in a noisy environment, the frequency extraction method and waveform reconstruction method of multi-person respiration are proposed. Finally, the feasibility of the method is verified by experiments.

Indexed as

Radio Frequency Identification DeviceHumansMonitoring, PhysiologicWireless Technologyfrequency extractionrespiratory monitoringRFIDwaveform reconstruction

Identifiers

PMID36015926

DOI10.3390/s22166166

PMCPMC9416178

OpenAlexW4293065388

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.