Method for Estimating Amount of Saliva Secreted Using a Throat Microphone.

Kai Washino, Ayumi Ohnishi, Tsutomu Terada, Masahiko Tsukamoto

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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

4 authors.

Kai WashinoGraduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe 657-8501, Japan.ORCID 0009-0000-4255-1706

Ayumi OhnishiGraduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe 657-8501, Japan.ORCID 0000-0003-3583-6118

Tsutomu TeradaGraduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe 657-8501, Japan.ORCID 0000-0003-2260-3788

Masahiko TsukamotoGraduate School of Engineering, Kobe University, 1-1 Rokkodaicho, Nada, Kobe 657-8501, Japan.ORCID 0000-0002-8662-4070

Funding

Japan Science and Technology Agency 06-08

6 · The paper itself

Abstract

Saliva is an important secretion, and a continued insufficient amount of saliva secreted causes glossitis, stomatitis, and so on. Since the amount of saliva secreted changes daily, adverse effects occur daily. Therefore, it is necessary to constantly measure the amount of saliva secreted and take appropriate measures when it decreases. However, there is no method to constantly measure saliva. We propose a method to estimate the amount of saliva secreted from the sound acquired by a wearable throat microphone. The proposed method uses deep learning to classify whether the sound acquired by the throat microphone is swallowing or not. Based on the swallowing information, the proposed method estimates the amount of saliva secreted. The accuracy of the classification of swallowing was 96.96%. For the estimation of the amount of saliva secreted, the R was 0.600 and MAE was 0.0487.

Indexed as

PharynxSalivaAdultDeep LearningDeglutitionHumansMaleWearable Electronic Deviceshealthcareoral environmentsaliva secretionwearable computing

Identifiers

PMID40573471

DOI10.3390/s25123584

PMCPMC12197301

What OpenQuestion holds

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LicenceCC BY

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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.