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Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification

Medicine and Health

Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification

H. K. Lee, S. U. Park, et al.

Discover an innovative approach to real-time respiratory pattern monitoring using a wireless mechano-acoustic sensor integrated with a deep learning system. This groundbreaking research conducted by Hee Kyu Lee, Sang Uk Park, Sunga Kong, Heyin Ryu, Hyun Bin Kim, Sang Hoon Lee, Danbee Kang, Sun Hye Shin, Ki Jun Yu, Juhee Cho, Joohoon Kang, Il Yong Chun, Hye Yun Park, and Sang Min Won could revolutionize COPD diagnosis, providing a promising alternative to traditional spirometry.

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Playback language: English
Abstract
This study integrates a wireless mechano-acoustic sensor with a multi-modal deep learning system for real-time respiratory pattern monitoring. The system uses an epidermally mounted sensor to capture vocal fold vibrations, transmitting data to a machine learning server for real-time analysis and feedback. The system classifies spoken phrases, user characteristics, and COPD severity, offering a potential alternative to spirometry for COPD diagnosis.
Publisher
npj Flexible Electronics
Published On
Nov 16, 2024
Authors
Hee Kyu Lee, Sang Uk Park, Sunga Kong, Heyin Ryu, Hyun Bin Kim, Sang Hoon Lee, Danbee Kang, Sun Hye Shin, Ki Jun Yu, Juhee Cho, Joohoon Kang, Il Yong Chun, Hye Yun Park, Sang Min Won
Tags
respiratory monitoring
deep learning
COPD diagnosis
mechano-acoustic sensor
real-time analysis

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