Clinical Research on the Application of Inertial Measurement Units in Respiratory Cycle Measurement

Objective: The purpose of this study is to explore the feasibility of using inertial measurement unit (IMU) sensors to monitor respiratory cycles.Methods: The study involved monitoring the abdominal wall movements of 8 healthy volunteers for 9 minutes using IMU sensors. Acceleration change signals were extracted conditionally, and the respiratory cycle (Ttot) was calculated Results: The post-filtering algorithm proposed in this study demonstrated high performance in monitoring respiratory cycle time, as evidenced by the Ttot based on waveform recognition, with an average absolute error of 0.2 seconds per respiratory cycle, a correlation of r > 0.88, and a BlandAltman analysis with an average difference of 0.017 seconds. Conclusion: Monitoring respiratory cycles using inertial measurement unit sensors is feasible and can be widely applied in clinical and home settings. This provides a new approach and method for developing low-cost, wearable, real-time, and stable respiratory monitoring systems.

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Publication Details

Published
2024-08-23
DOI
https://doi.org/10.1109/i-create62067.2024.10776418
Primary Topic
Chronic Obstructive Pulmonary Disease (COPD) Research
Type
article
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article

Clinical Research on the Application of Inertial Measurement Units in Respiratory Cycle Measurement

Juan Fu, Yi Han, Yujie Wu, Hui Feng et al.
Chronic Obstructive Pulmonary Disease (COPD) Research
article

Clinical Research on the Application of Inertial Measurement Units in Respiratory Cycle Measurement

Juan Fu, Yi Han, Yujie Wu, Hui Feng, Huaping Pan
article en

Abstract

Objective: The purpose of this study is to explore the feasibility of using inertial measurement unit (IMU) sensors to monitor respiratory cycles.Methods: The study involved monitoring the abdominal wall movements of 8 healthy volunteers for 9 minutes using IMU sensors. Acceleration change signals were extracted conditionally, and the respiratory cycle (Ttot) was calculated Results: The post-filtering algorithm proposed in this study demonstrated high performance in monitoring respiratory cycle time, as evidenced by the Ttot based on waveform recognition, with an average absolute error of 0.2 seconds per respiratory cycle, a correlation of r > 0.88, and a BlandAltman analysis with an average difference of 0.017 seconds. Conclusion: Monitoring respiratory cycles using inertial measurement unit sensors is feasible and can be widely applied in clinical and home settings. This provides a new approach and method for developing low-cost, wearable, real-time, and stable respiratory monitoring systems.

Nanjing Jiangning Hospital (CN), Nanjing Medical University (CN)
Openalex Percentile: Top 58%
Chronic Obstructive Pulmonary Disease (COPD) Research
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