Modeling and Alignment Algorithms of Multiple Sensors for the Wearable Human Respiration Monitoring System

The health condition can be reflected by human respiration monitoring, which requires the cooperation of various sensors including flow and concentration sensors. Based on the signal characteristics of the respiratory process, several modeling methods were used to reduce measurement error and improv...

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Bibliographic Details
Published in:IEEE sensors journal 2024-02, Vol.24 (3), p.1-1
Main Authors: Chen, Hanyu, Xiao, Liying, Guan, Xinyu
Format: Article
Language:English
Subjects:
Algorithms
Alignment
autoregressive exogenous (ARX) model
Autoregressive models
black-box model
Carbon dioxide
Condition monitoring
Error analysis
Error reduction
Flowmeters
Gas detectors
Human respiration monitor
Measurement methods
Modelling
Monitoring
Respiration
Response time
Sensor phenomena and characterization
Sensor systems
Sensors
signal alignment
System effectiveness
Turbines
Wiener filter
Wiener filtering
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Summary:The health condition can be reflected by human respiration monitoring, which requires the cooperation of various sensors including flow and concentration sensors. Based on the signal characteristics of the respiratory process, several modeling methods were used to reduce measurement error and improve response speed. The autoregressive exogenous (ARX) model resulted in smoother data from the turbine flowmeter and enabled 96.2% of tests to have an error of less than 3%. Wiener filtering significantly reduced the response time of the gas concentration sensors. The response time was shortened from 140 ms to 100 ms for the CO 2 sensor, and 220 ms to 100 ms for the O 2 sensor. The end-tidal gas concentration characteristics were used to perform an alignment criterion between the different sensors to calculate end-tidal oxygen (F ET O 2 ) and end-tidal carbon dioxide (F ET CO 2 ) in comparison with Cosmed K5, which shows clinically insignificant differences according to the Bland-Altman analysis. This paper provides a comprehensive modeling approach for the breath-by-breath (BBB) respiratory measurement method, enhancing the system's performance through sensor modeling and sensor signal alignment. Results indicate potential for practical application and scalability, offering an effective reference for similar systems.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2023.3339180