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

2025-08-07

AI Summary

Traditional contact-based respiration monitoring suffers from limitations in user comfort and long-term practicality. To address this, this paper presents a systematic review of both contact and non-contact respiration analysis techniques, emphasizing respiratory signals as biomarkers for health assessment, disease detection, and multi-user identification. We propose an end-to-end respiratory analysis framework that uniquely integrates Wi-Fi channel state information (CSI) and acoustic sensing—two prominent non-contact modalities—spanning signal acquisition, feature extraction, model training, and deployment. Methodologically, we innovatively incorporate explainable AI (XAI), federated learning, and transfer learning to enhance model generalizability and ensure privacy preservation. Experimental evaluation demonstrates strong performance across respiratory rate estimation, user identification, and respiratory disease classification tasks. Furthermore, we establish a comprehensive technical evaluation framework assessing accuracy, practicality, and scalability. This work provides both a methodological foundation and actionable implementation pathways for non-contact intelligent health monitoring.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Interaction Techniques and Devices
• Computer Vision: Multi-modal Vision

Application Category

• Responsible Web: Algorithmic accountability and transparency on the web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Comparing contact and contactless breath analysis methods for health monitoring
• Evaluating machine learning techniques in respiratory disease detection
• Addressing challenges like data scarcity and privacy in breath analysis

Innovation

• Contactless respiratory monitoring using Wi-Fi CSI
• Machine learning for breath analysis applications
• Explainable AI and federated learning integration