Evidence map›Paper›PMID 34083521›Full record

ArticleNature communications2021

Digital electronics in fibres enable fabric-based machine-learning inference.

Gabriel Loke, Tural Khudiyev, Brian Wang, Stephanie Fu, Syamantak Payra, Yorai Shaoul, Johnny Fung, Ioannis Chatziveroglou, Pin-Wen Chou, Itamar Chinn and 4 more

Abstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 1 pooled it
–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

43 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  8. Curvy Surface Reconstruction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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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

14 authors.

Gabriel Loke *Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Tural Khudiyev *Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Brian WangDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Stephanie FuDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0001-6591-6026
Syamantak PayraDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0002-7810-7962
Yorai ShaoulDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Johnny FungDepartment of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Ioannis ChatziveroglouDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Pin-Wen ChouHarrisburg University of Science and Technology, Harrisburg, PA, USA.
Itamar ChinnDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Wei YanResearch Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0003-4646-7039
Anna Gitelson-KahnTextile Department, Rhode Island School of Design, Providence, RI, USA.
John JoannopoulosInstitute for Soldier Nanotechnologies, Massachusetts Institute of Technology, Cambridge, MA, USA.
Yoel FinkDepartment of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. yoel@mit.edu.ORCID 0000-0001-9752-2283

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital devices are the essential building blocks of any modern electronic system. Fibres containing digital devices could enable fabrics with digital system capabilities for applications in physiological monitoring, human-computer interfaces, and on-body machine-learning. Here, a scalable preform-to-fibre approach is used to produce tens of metres of flexible fibre containing hundreds of interspersed, digital temperature sensors and memory devices with a memory density of ~7.6 × 10

Indexed as

Machine LearningTextilesWearable Electronic DevicesBody TemperatureDigital TechnologyElectronicsHumansMemoryMonitoring, PhysiologicNeural Networks, ComputerRemote Sensing TechnologyUser-Computer Interface

Identifiers

PMID34083521
PMCPMC8175338

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.