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Related Experiment Video

Updated: Jul 10, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Lung model parameter estimation by unscented Kalman filter.

Esra Saatçi¹, Aydin Akan

¹Department of Electronic Engineering, Istanbul Kültür University, Bakirkoy, Istanbul, Türkiye. esra.saatci@iku.edu.tr

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|November 16, 2007

Summary

Unscented Kalman Filtering (UKF) effectively estimates lung model parameters for respiratory system analysis. This method shows promise for analyzing both simulated data and data from Chronic Obstructive Pulmonary Diseased (COPD) patients.

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Last Updated: Jul 10, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Physiology
Biomedical Engineering
Computational Modeling

Background:

Dynamic nonlinear models are essential for analyzing respiratory system mechanics.
Accurate parameter estimation is crucial for understanding lung function and disease.

Purpose of the Study:

To estimate dynamic nonlinear lung model parameters using Unscented Kalman Filtering (UKF).
To evaluate the UKF algorithm's performance in respiratory system analysis.

Main Methods:

Utilized Unscented Kalman Filtering (UKF) for parameter estimation.
Employed measured airway flow, mask pressure, and integrated lung volume data.
Analyzed both artificially generated data and patient data from Chronic Obstructive Pulmonary Diseased (COPD) individuals.

Main Results:

The UKF algorithm successfully estimated dynamic nonlinear lung model parameters.
Simulation results demonstrated UKF's effectiveness with both synthetic and real-world COPD data.

Conclusions:

Unscented Kalman Filtering (UKF) is a promising method for respiratory system analysis.
The proposed UKF approach provides reliable parameter estimation for lung models.