Evidence map›Paper›PMID 32331325›Full record

ArticleSensors (Basel, Switzerland)2020

Highly Sensitive E-Textile Strain Sensors Enhanced by Geometrical Treatment for Human Monitoring.

Chi Cuong Vu, Jooyong Kim

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

9 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
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  5. Article
  6. Review
  7. Article
  8. Article
  9. Wearable Textile UHF-RFID Sensors: A Systematic Review.Materials (Basel, Switzerland) · 2020
    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

2 authors at 1 institution in 1 country.

Chi Cuong VuDepartment of Organic Materials and Fiber Engineering, Soongsil University, Seoul 156-743, Korea.ORCID 0000-0002-7158-8441
Jooyong KimDepartment of Organic Materials and Fiber Engineering, Soongsil University, Seoul 156-743, Korea.ORCID 0000-0003-1969-5730
Soongsil University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic textiles, also known as smart textiles or smart fabrics, are one of the best form factors that enable electronics to be embedded in them, presenting physical flexibility and sizes that cannot be achieved with other existing electronic manufacturing techniques. As part of smart textiles, e-sensors for human movement monitoring have attracted tremendous interest from researchers in recent years. Although there have been outstanding developments, smart e-textile sensors still present significant challenges in sensitivity, accuracy, durability, and manufacturing efficiency. This study proposes a two-step approach (from structure layers and shape) to actively enhance the performance of e-textile strain sensors and improve manufacturing ability for the industry. Indeed, the fabricated strain sensors based on the silver paste/single-walled carbon nanotube (SWCNT) layers and buffer cutting lines have fast response time, low hysteresis, and are six times more sensitive than SWCNT sensors alone. The e-textile sensors are integrated on a glove for monitoring the angle of finger motions. Interestingly, by attaching the sensor to the skin of the neck, the pharynx motions when speaking, coughing, and swallowing exhibited obvious and consistent signals. This research highlights the effect of the shapes and structures of e-textile strain sensors in the operation of a wearable e-textile system. This work also is intended as a starting point that will shape the standardization of strain fabric sensors in different applications.

Indexed as

TextilesWearable Electronic DevicesHumansMovementNanotubes, CarbonNanotubes, Carbone-textilesshapesilver pastesingle-walled carbon nanotubes (SWCNT)structure

Identifiers

PMID32331325
PMCPMC7219329
OpenAlexW3017642735

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.