ArticleSensors (Basel, Switzerland)2024
Classification of Breathing Phase and Path with In-Ear Microphones.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Multimodal In-Ear Audio and Physiological Dataset for Swallowing and Non-Verbal Event Classification.Sensors (Basel, Switzerland) · 2026Article
- Optimizing MFCC Parameters for Breathing Phase Detection.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
3 authors.
Funding
Abstract
In recent years, the use of smart in-ear devices (hearables) for health monitoring has gained popularity. Previous research on in-ear breath monitoring with hearables uses signal processing techniques based on peak detection. Such techniques are greatly affected by movement artifacts and other challenging real-world conditions. In this study, we use an existing database of various breathing types captured using an in-ear microphone to classify breathing path and phase. Having a small dataset, we use XGBoost, a simple and fast classifier, to address three different classification challenges. We achieve an accuracy of 86.8% for a binary path classifier, 74.1% for a binary phase classifier, and 67.2% for a four-class path and phase classifier. Our path classifier outperforms existing algorithms in recall and F1, highlighting the reliability of our approach. This work demonstrates the feasibility of the use of hearables in continuous breath monitoring tasks with machine learning.
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Registered trials
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