ArticleDysphagia2026
A Wearable Fabric Sensing Device for Swallow Monitoring and Classification.
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.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Biomimetic Wearable Device for Dysphagia: Digital Conceptual Design via KANO-EWM-TOPSIS and Extended FCBS Mapping.Biomimetics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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
Identifiers
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Registered trials
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.