Evidence map›Paper›PMID 35493959›Full record

ArticleSensors and actuators. A, Physical2022

A biomimetic skin phantom for characterizing wearable electrodes in the low-frequency regime.

Krittika Goyal, David A Borkholder, Steven W Day

Open access · greenAbstract read
In one paragraph

Article in Sensors and actuators. A, Physical, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

11 citing papers in PubMed, 23 citations in OpenAlex.

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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

3 authors at 1 institution in 1 country.

Krittika GoyalDepartment of Microsystems Engineering, Rochester Institute of Technology, Rochester, NY, 14623, USA.
David A BorkholderDepartment of Microsystems Engineering, Rochester Institute of Technology, Rochester, NY, 14623, USA.
Steven W DayDepartment of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, 14623, USA.
Rochester Institute of Technology · US

Funding

Inconspicuous Daily Monitoring to Reduce Heart Failure HospitalizationsR01NR018301 · NINR · ROCHESTER INSTITUTE OF TECHNOLOGY · PI WANG, LINWEI, ZAREBA, WOJCIECH · 2020 to 2024
$2.8M
NINR NIH HHS R01 NR018301
6 · The paper itself

Abstract

Advances in the integration of wearable devices in our daily life have led to the development of new electrode designs for biopotential monitoring. Historically, the development and testing of wearable electrodes for the acquisition of biopotential signals has been empirical, relying on experiments on human volunteers. However, the lack of explicit control on human variables, the intra-, and inter-subject variability complicates the understanding of the performance of these wearable electrodes. Herein, phantom mimicking the electrical properties of the skin in the low-frequency range (1 Hz-1000 Hz), which has the potential to be used as a platform for controlled benchtop experiments for testing electrode functionality, is demonstrated. The fabricated phantom comprises two layers representing the deeper tissues and stratum corneum. The lower layer of the phantom mimicking deeper tissues was realized using polyvinyl alcohol cryogel (PVA-c) prepared with 0.9% W/W saline solution by a freeze-thaw technique. The properties of the upper layer representing the stratum corneum were simulated using a 100μm thick layer fabricated by spin-coating a mixture of polydimethylsiloxane (PDMS), 2.5% W/W carbon black (CB) for conductance, and 40% W/W barium titanate (BaTiO

Indexed as

Biopotential signalImpedance spectroscopySkin hydrationSkin phantomWearable electrodes

Identifiers

PMID35493959
PMCPMC9053740
OpenAlexW4220885473

What OpenQuestion holds

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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.