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

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

doi:10.1038/s41528-024-00355-7

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

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

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning (ML) algorithm interfaces. The findings from these previous studies have established a strong foundation for the analysis of high-resolution, intricate signals, typically through frequency domain conversion. In this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 kHz. This interface provides real-time data visualization and communication with the ML server, creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user’s speech patterns.

Original language	English
Article number	69
Journal	npj Flexible Electronics
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41528-024-00355-7
Publication status	Published - 2024 Dec

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

All Science Journal Classification (ASJC) codes

General Materials Science
Electrical and Electronic Engineering

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10.1038/s41528-024-00355-7

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Lee, H. K., Park, S. U., Kong, S., Ryu, H., Kim, H. B., Lee, S. H., Kang, D., Shin, S. H., Yu, K. J., Cho, J., Kang, J., Chun, I. Y., Park, H. Y., & Won, S. M. (2024). Real-time deep learning-assisted mechano-acoustic system for respiratory diagnosis and multifunctional classification. npj Flexible Electronics, 8(1), Article 69. https://doi.org/10.1038/s41528-024-00355-7

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abstract = "Epidermally mounted sensors using triaxial accelerometers have been previously used to monitor physiological processes with the implementation of machine learning (ML) algorithm interfaces. The findings from these previous studies have established a strong foundation for the analysis of high-resolution, intricate signals, typically through frequency domain conversion. In this study we integrate a wireless mechano-acoustic sensor with a multi-modal deep learning system for the real-time analysis of signals emitted by the laryngeal prominence area of the thyroid cartilage at frequency ranges up to 1 kHz. This interface provides real-time data visualization and communication with the ML server, creating a system that assesses severity of chronic obstructive pulmonary disease and analyzes the user{\textquoteright}s speech patterns.",

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doi = "10.1038/s41528-024-00355-7",

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AU - Kong, Sunga

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AU - Kim, Hyun Bin

AU - Lee, Sang Hoon

AU - Kang, Danbee

AU - Shin, Sun Hye

AU - Yu, Ki Jun

AU - Cho, Juhee

AU - Kang, Joohoon

AU - Chun, Il Yong

AU - Park, Hye Yun

AU - Won, Sang Min

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

Abstract

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