Evidence map›Paper›PMID 41432750›Full record

ArticleDysphagia2026

A Wearable Fabric Sensing Device for Swallow Monitoring and Classification.

David Zhang, Wei Li, Jonathan Chen, Corinne Jones

Abstract read
PubMed Publisher
In one paragraph

Article in Dysphagia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

David ZhangWalker Department of Mechanical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA. david.zhang@austin.utexas.edu.
Wei LiWalker Department of Mechanical Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA.
Jonathan ChenSchool of Human Ecology, College of Natural Sciences, Texas Materials Institute, The University of Texas at Austin, Austin, TX, USA.
Corinne JonesDepartment of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin, Austin, TX, USA. corinne.jones@austin.utexas.edu.ORCID http://orcid.org/0000-0001-8719-3068

Funding

University of Texas at Austin Proof of Concept AwardUniversity of Texas at Austin Texas Health Catalyst
6 · The paper itself

Abstract

Laryngeal elevation during swallowing is critical to airway protection, pharyngeal shortening, and upper esophageal sphincter opening. Currently, videofluoroscopy is the only way to quantify amplitude and timing of laryngeal elevation during swallowing. Although wearable devices exist, none are able to quantify amplitude of laryngeal elevation. This proof-of-concept study introduces a novel wearable fabric sensing device equipped with three knitted strain sensors, designed for real-time monitoring and precise classification of swallowing actions. Simultaneous recordings were made with our sensors and submental surface electromyography (sEMG) on 12 healthy adults who performed swallowing and non-swallowing tasks. K-nearest neighbors models were utilized to classify swallowing and non-swallowing tasks using data from knitted sensors and sEMG signals. Surveys were conducted to assess the comfort of wearing the device. The overall accuracy of KNN classifications to predict swallowing versus non-swallowing tasks was 0.97 for the knitted sensor, 0.82 for sEMG, and 0.98 for the combined dataset (knitted sensor + sEMG), while the accuracy to predict specific tasks was 0.75 for the knitted sensor, 0.32 for sEMG, and 0.77 for the combined dataset. Participants rated the knitted sensor's discomfort an average rating of 7.33/100, indicating a low level of discomfort. Our findings show that laryngeal movement during swallowing can be detected using knitted strain sensors worn on the outside of the neck, with the ability to distinguish between swallowing and non-swallowing tasks, as well as between types of swallows. The detection accuracy is significantly higher than that using the state-of-the-art sEMG method.

Indexed as

DeglutitionWearable Electronic DevicesAdultElectromyographyEquipment DesignFemaleHealthy VolunteersHumansLarynxMaleMonitoring, PhysiologicProof of Concept StudyTextilesYoung AdultDeglutitionElectromyographyStrain sensorSwallow measurementWearable device

Identifiers

What OpenQuestion holds

Textmetadata
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.