Improving Reliability of Magnetic Localization Using Input Space Transformation.

Cem O Yaldiz, Nordine Sebkhi, Arpan Bhavsar, Jun Wang, Omer T Inan

Abstract read

In one paragraph

Article in IEEE sensors journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Cem O YaldizSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Nordine SebkhiSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Arpan BhavsarSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Jun WangDepartment of Speech, Language, and Hearing Sciences and the Department of Neurology, The University of Texas at Austin, Austin, TX 78712, USA.

Omer T InanSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

Funding

Wearable silent speech technology to enhance impaired oral communicationR01DC016621 · NIDCD · UNIVERSITY OF TEXAS AT AUSTIN · PI WANG, JUN · 2019 to 2023

$3.2M

NIDCD NIH HHS R01 DC016621

6 · The paper itself

Abstract

Body motion tracking for medical applications has the potential to improve quality of life for people with physical or speech motor disorders. Current solutions available in the market are either inaccurate, not affordable, or are impractical for a medical setting or at home. Magnetic localization can address these issues thanks to its high accuracy, simplicity of use, wearability, and use of inexpensive sensors such as magnetometers. However, sources of unreliability affect magnetometers to such an extent that the localization model trained in a controlled environment might exhibit poor tracking accuracy when deployed to end users. Traditional magnetic calibration methods, such as ellipsoid fit (EF), do not sufficiently attenuate the effect of these sources of unreliability to reach a positional accuracy that is both consistent and satisfactory for our target applications. To improve reliability, we developed a calibration method called

Indexed as

distribution shiftinertial measurement unitmachine learningmagnetic localizationmagnetizationmagnetometerMotion trackingneural networktongue tracking

Identifiers

PMID38962278

DOI10.1109/jsen.2023.3320033

PMCPMC11218913

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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.