ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
A Wearable AI-Driven Mask with Humidity-Sensing Respiratory Microphone for Non-Vocal Communication.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
- A Wearable AI-Driven Mask with Humidity-Sensing Respiratory Microphone for Non-Vocal Communication.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Artificial Intelligence-Powered Nanosensor Platforms for Non-Invasive Breathomic Diagnostics.Nanotechnology, science and applications · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Hoarseness and dysphonia caused by vocal cord conditions or laryngeal surgeries significantly hinder communication and quality of life. This study presents a plug-and-play humidity-sensing respiratory microphone (HSRM) with generalized features for individual users. Leveraging gold nanoparticle-based humidity sensors integrated into commercially available wearable face masks, the system enables patients to produce verbal communication without relying on vocal cord activity. By integrating nanoparticle-enhanced humidity sensors with advanced convolutional neural networks, the HSRM system accurately decodes respiratory patterns into intelligible speech, achieving a recognition accuracy of 85.61%. Leveraging nanoparticle-polymer interfaces that effectively convert atmospheric humidity fluctuations into precise electrical signals, the system pioneers a contactless and non-invasive paradigm in assistive speech technology. This innovation addresses limitations of existing devices, such as reliance on residual vocal fold vibrations or skin-contact sensors, offering a practical generalized solution for individuals with aphonia. With its potential to facilitate naturalistic communication and transform healthcare applications, the HSRM system sets a new benchmark in wearable assistive technologies for voice rehabilitation and human-machine interaction.
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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.