ArticleNPJ digital medicine2025
Multimodal deep ensemble classification system with wearable vibration sensor for detecting throat-related events.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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Who cites it
7 citing papers in PubMed.
- Human-AI collaboration for dysphagia rehabilitation from effectiveness to implementation complexity: a systematic review.NPJ digital medicine · 2026Article
- Throat and acoustic paired speech dataset for deep learning-based speech enhancement.Scientific data · 2026Article
- A Multimodal In-Ear Audio and Physiological Dataset for Swallowing and Non-Verbal Event Classification.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning in Otolaryngology: A Narrative Review.JAMA otolaryngology-- head & neck surgery · 2026Review
- An mHealth App and System Architecture for Respiratory Disease Management: Design Principles, Tool Development, and Pilot Usability Study.JMIR formative research · 2025Article
- Wearable bioelectronics for cancer theranostics.Microsystems & nanoengineering · 2025Review
- Application of intelligent technologies for dysphagia risk prediction: A scoping review.Digital healthReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dysphagia, a swallowing disorder, requires continuous monitoring of throat-related events to obtain comprehensive insights into the patient's pharyngeal and laryngeal functions. However, conventional assessments were performed by medical professionals in clinical settings, limiting persistent monitoring. We demonstrate feasibility of a ubiquitous monitoring system for autonomously detecting throat-related events utilizing a soft skin-attachable throat vibration sensor (STVS). The STVS accurately records throat vibrations without interference from surrounding noise, enabling measurement of subtle sounds such as swallowing. Out of the continuous data stream, we automatically classify events of interest using an ensemble-based deep learning model. The proposed model integrates multiple deep neural networks based on multi-modal acoustic features of throat-related events to enhance robustness and accuracy of classification. The performance of our model outperforms previous studies with a classification accuracy of 95.96%. These results show the potential of wearable solutions for improving dysphagia management and patient outcomes outside of clinical environments.
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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.