Evidence map›Paper›PMID 40576484›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

A Wearable AI-Driven Mask with Humidity-Sensing Respiratory Microphone for Non-Vocal Communication.

Jianfei Wang, Hongyu Zhang, Xiaomin Wu, Mingyan Gao, He Wen, Zhibo Zhang, Kremena Makasheva, Wen Jung Li, Zuobin Wang

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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

9 authors.

Jianfei WangInternational Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Jilin, 130022, China.ORCID https://orcid.org/0000-0003-3545-4638
Hongyu ZhangCAS-CityU Joint Laboratory for Robotic Research, Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong, 999077, China.
Xiaomin WuCollege of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China.
Mingyan GaoCentre for Opto/Bio-Nano Measurement and Manufacturing, Zhongshan Institute of Changchun University of Science and Technology, Zhongshan, 528437, China.
He WenInternational Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Jilin, 130022, China.
Zhibo ZhangInternational Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Jilin, 130022, China.
Kremena MakashevaLaboratory on Plasma and Conversion of Energy (LAPLACE), CNRS, Toulouse, 31062, France.
Wen Jung LiCAS-CityU Joint Laboratory for Robotic Research, Department of Mechanical Engineering, City University of Hong Kong, Kowloon, Hong Kong, 999077, China.ORCID https://orcid.org/0000-0001-9616-6213
Zuobin WangInternational Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Jilin, 130022, China.

Funding

Horizon Europe Program 101086226National Key R&D Program of China 2023YFE0108800
6 · The paper itself

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.

Indexed as

Artificial IntelligenceHumidityMasksWearable Electronic DevicesHumanscontactless sensinggold nanoparticleshuman‐machine interactionshumidity sensorrespiration language

Identifiers

PMID40576484
PMCPMC12412612

What OpenQuestion holds

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LicenceCC BY
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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.