Evidence map›Paper›PMID 36772611›Full record

ArticleSensors (Basel, Switzerland)2023

A Hydrogel-Based Electronic Skin for Touch Detection Using Electrical Impedance Tomography.

Huiyang Zhang, Anubha Kalra, Andrew Lowe, Yang Yu, Gautam Anand

Abstract 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 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
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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

5 authors.

Huiyang ZhangInstitute of Biomedical Technologies, Auckland University of Technology, Auckland 1010, New Zealand.
Anubha KalraInstitute of Biomedical Technologies, Auckland University of Technology, Auckland 1010, New Zealand.
Andrew LoweInstitute of Biomedical Technologies, Auckland University of Technology, Auckland 1010, New Zealand.ORCID 0000-0002-0612-1036
Yang YuInstitute of Biomedical Technologies, Auckland University of Technology, Auckland 1010, New Zealand.ORCID 0000-0002-7673-8090
Gautam AnandInstitute of Biomedical Technologies, Auckland University of Technology, Auckland 1010, New Zealand.ORCID 0000-0002-3388-1225

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advancement in wearable and robot-assisted healthcare technology gives rise to the demand for smart interfaces that allow more efficient human-machine interaction. In this paper, a hydrogel-based soft sensor for subtle touch detection is proposed. Adopting the working principle of a biomedical imaging technology known as electrical impedance tomography (EIT), the sensor produces images that display the electrical conductivity distribution of its sensitive region to enable touch detection. The sensor was made from a natural gelatin hydrogel whose electrical conductivity is considerably less than that of human skin. The low conductivity of the sensor enabled a touch-detection mechanism based on a novel short-circuiting approach, which resulted in the reconstructed images being predominantly affected by the electrical contact between the sensor and fingertips, rather than the conventionally used piezoresistive response of the sensing material. The experimental results indicated that the proposed sensor was promising for detecting subtle contacts without the necessity of exerting a noticeable force on the sensor.

Indexed as

TouchWearable Electronic DevicesElectric ImpedanceHumansHydrogelsTomography, X-Ray ComputedHydrogelselectronic skinhuman-machine interfacehydrogel sensorimpedance tomographysoft interfacesoft roboticssoft sensortomographic imagingtouch sensor

Identifiers

PMID36772611
PMCPMC9918904

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.