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Related Experiment Video

Updated: Sep 17, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

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

Jianfei Wang¹, Hongyu Zhang², Xiaomin Wu³

¹International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Jilin, 130022, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|June 27, 2025

Summary

A new wearable microphone decodes breathing patterns into speech for individuals with voice loss. This humidity-sensing respiratory microphone (HSRM) offers a non-invasive solution for voice rehabilitation and improved communication.

Keywords:

contactless sensing gold nanoparticles human‐machine interactions humidity sensor respiration language

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Area of Science:

Biomedical Engineering
Wearable Technology
Assistive Speech Technology

Background:

Hoarseness and dysphonia significantly impair communication and quality of life.
Existing assistive speech devices have limitations, including reliance on residual vocal fold vibrations or skin-contact sensors.
There is a need for non-invasive, generalized solutions for individuals with aphonia.

Purpose of the Study:

To develop and evaluate a plug-and-play humidity-sensing respiratory microphone (HSRM) for individuals with voice loss.
To enable verbal communication without relying on vocal cord activity.
To establish a new benchmark in wearable assistive technologies for voice rehabilitation.

Main Methods:

Integration of gold nanoparticle-based humidity sensors into commercially available wearable face masks.
Utilizing nanoparticle-polymer interfaces to convert humidity fluctuations into electrical signals.
Employing advanced convolutional neural networks for decoding respiratory patterns into speech.

Main Results:

The HSRM system achieved an 85.61% speech recognition accuracy.
Demonstrated accurate decoding of respiratory patterns into intelligible speech.
Pioneered a contactless and non-invasive paradigm in assistive speech technology.

Conclusions:

The HSRM system provides a practical, generalized solution for individuals with aphonia.
This innovation facilitates naturalistic communication and has transformative potential in healthcare applications.
The HSRM system sets a new standard for wearable assistive technologies in voice rehabilitation and human-machine interaction.