Evidence map›Paper›PMID 37731914›Full record

ArticleMicrosystems & nanoengineering2023

Stretchable and durable HD-sEMG electrodes for accurate recognition of swallowing activities on complex epidermal surfaces.

Ding Zhang, Zhitao Chen, Longya Xiao, Beichen Zhu, RuoXuan Wu, ChengJian Ou, Yi Ma, Longhan Xie, Hongjie Jiang

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in Microsystems & nanoengineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
3.6field-weighted citation impact, top 6% 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

17 citing papers in PubMed, 33 citations in OpenAlex.

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  16. Surface EMG in China: a report on the 2023 surface EMG symposium.Frontiers in rehabilitation sciences · 2024
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Ding ZhangShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China.
Zhitao ChenShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China.
Longya XiaoShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China.
Beichen ZhuShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China.
RuoXuan WuSchool of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, 511442 P. R. China.
ChengJian OuShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China.
Yi MaShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China.
Longhan XieShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China.
Hongjie JiangShien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, 511442 P. R. China.ORCID 0000-0002-6756-704X
South China University of Technology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surface electromyography (sEMG) is widely used in monitoring human health. Nonetheless, it is challenging to capture high-fidelity sEMG recordings in regions with intricate curved surfaces such as the larynx, because regular sEMG electrodes have stiff structures. In this study, we developed a stretchable, high-density sEMG electrode array via layer-by-layer printing and lamination. The electrode offered a series of excellent human‒machine interface features, including conformal adhesion to the skin, high electron-to-ion conductivity (and thus lower contact impedance), prolonged environmental adaptability to resist water evaporation, and epidermal biocompatibility. This made the electrode more appropriate than commercial electrodes for long-term wearable, high-fidelity sEMG recording devices at complicated skin interfaces. Systematic in vivo studies were used to investigate its ability to classify swallowing activities, which was accomplished with high accuracy by decoding the sEMG signals from the chin via integration with an ear-mounted wearable system and machine learning algorithms. The results demonstrated the clinical feasibility of the system for noninvasive and comfortable recognition of swallowing motions for comfortable dysphagia rehabilitation.

Indexed as

Electrical and electronic engineeringMaterials science

Identifiers

PMID37731914
PMCPMC10507084
OpenAlexW4386835103

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.