Breath as a biomarker: A survey of contact and contactless applications and approaches in respiratory monitoring

TL;DR

This survey reviews contact and contactless breath analysis methods, emphasizing recent machine learning advances, applications in health monitoring, and discussing challenges and future research directions.

Contribution

It provides a comprehensive overview of recent technological advances in breath analysis, including machine learning techniques and noninvasive methods, highlighting open challenges and future trends.

Findings

Contactless methods like Wi-Fi CSI and acoustic sensing enable noninvasive respiratory monitoring.

Machine learning and deep learning models are effectively applied for breath analysis tasks.

Key challenges include dataset scarcity, multi-user interference, and data privacy concerns.

Abstract

Breath analysis has emerged as a critical tool in health monitoring, offering insights into respiratory function, disease detection, and continuous health assessment. While traditional contact-based methods are reliable, they often pose challenges in comfort and practicality, particularly for long-term monitoring. This survey comprehensively examines contact-based and contactless approaches, emphasizing recent advances in machine learning and deep learning techniques applied to breath analysis. Contactless methods, including Wi-Fi Channel State Information and acoustic sensing, are analyzed for their ability to provide accurate, noninvasive respiratory monitoring. We explore a broad range of applications, from single-user respiratory rate detection to multi-user scenarios, user identification, and respiratory disease detection. Furthermore, this survey details essential data preprocessing, feature extraction, and classification techniques, offering comparative insights into machine learning/deep learning models suited to each approach. Key challenges like dataset scarcity, multi-user interference, and data privacy are also discussed, along with emerging trends like Explainable AI, federated learning, transfer learning, and hybrid modeling. By synthesizing current methodologies and identifying open research directions, this survey offers a comprehensive framework to guide future innovations in breath analysis, bridging advanced technological capabilities with practical healthcare applications.