Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review

Abstract

Breathing pattern assessment holds critical importance in clinical practice for detecting respiratory dysfunctions and their impact on health and wellbeing. This systematic literature review investigates the efficacy of inertial sensors in assessing adult human breathing patterns, exploring various methodologies, challenges, and limitations. Utilizing the PSALSAR framework, incorporating the PICOC method and PRISMA statement for comprehensive research, 22 publications were scrutinized from the Scopus, Web of Science, and PubMed databases. A diverse range of sensor fusion methods, data signal analysis techniques, and classifier performances were investigated. Notably, Madgwick’s algorithm and the Principal Component Analysis showed superior performance in tracking respiratory movements. Classifiers like Long Short-Term Memory Recurrent Neural Networks exhibited high accuracy in detecting breathing events. Motion artifacts, limited sample sizes, and physiological variability posed challenges, highlighting the need for further research. Optimal sensor configurations were explored, suggesting improvements with multiple sensors, especially in different body postures. In conclusion, this systematic literature review elucidates methods, challenges, and potential future developments in using inertial sensors for assessing adult human breathing patterns. Overcoming the challenges related to sensor placement, motion artifacts, and algorithm development is essential for progress. Future research should focus on extending sensor applications to clinical settings and diverse populations, enhancing respiratory health management.

Keywords

• breathing mobility dysfunction
• breathing pattern assessment
• inertial sensors
• non-intrusive measurement
• machine learning classifiers
• algorithm