Evidence map›Paper›PMID 39184969›Full record

ArticleIEEE open journal of engineering in medicine and biology2024

EmRad: Ubiquitous Vital Sign Sensing Using Compact Continuous-Wave Radars.

Nils C Albrecht, Dominik Langer, Daniel Krauss, Robert Richer, Luca Abel, Bjoern M Eskofier, Nicolas Rohleder, Alexander Koelpin

Abstract read
In one paragraph

Article in IEEE open journal of engineering in medicine and biology, 2024. 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. Contactless Sleep Staging With Radar: A Transfer Learning Approach.IEEE open journal of engineering in medicine and biology · 2026
    Article
  2. Article
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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

8 authors.

Nils C AlbrechtInstitute of High-Frequency TechnologyTechnische Universität Hamburg 21073 Hamburg Germany.ORCID https://orcid.org/0000-0001-8991-2505
Dominik LangerInstitute of High-Frequency TechnologyTechnische Universität Hamburg 21073 Hamburg Germany.ORCID https://orcid.org/0000-0001-8659-8119
Daniel KraussMachine Learning and Data Analytics LabFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU) 91054 Erlangen Germany.ORCID https://orcid.org/0009-0009-0051-2835
Robert RicherMachine Learning and Data Analytics LabFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU) 91054 Erlangen Germany.ORCID https://orcid.org/0000-0003-0272-5403
Luca AbelMachine Learning and Data Analytics LabFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU) 91054 Erlangen Germany.ORCID https://orcid.org/0000-0002-5044-7113
Bjoern M EskofierMachine Learning and Data Analytics LaboratoryFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU) 91054 Erlangen Germany.ORCID https://orcid.org/0000-0002-0417-0336
Nicolas RohlederChair of Health Psychology, Institute of PsychologyFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU) 91054 Erlangen Germany.ORCID https://orcid.org/0000-0003-2602-517X
Alexander KoelpinInstitute of High-Frequency TechnologyTechnische Universität Hamburg 21073 Hamburg Germany.ORCID https://orcid.org/0000-0002-9071-5661

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In biomedical monitoring, non-intrusive and continuous tracking of vital signs is a crucial yet challenging objective. Although accurate, traditional methods, such as electrocardiography (ECG) and photoplethysmography (PPG), necessitate direct contact with the patient, posing limitations for long-term and unobtrusive monitoring. To address this challenge, we introduce the EmRad system, an innovative solution harnessing the capabilities of continuous-wave (CW) radar technology for the contactless detection of vital signs, including heart rate and respiratory rate. EmRad discerns itself by emphasizing miniaturization, performance, scalability, and its ability to generate large-scale datasets in various environments. This article explains the system's design, focusing on signal processing strategies and motion artifact reduction to ensure precise vital sign extraction. The EmRad system's versatility is showcased through various case studies, highlighting its potential to transform vital sign monitoring in research and clinical contexts.

Indexed as

Biomedical monitoringdoppler radarmachine learning

Identifiers

PMID39184969
PMCPMC11342936

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.