Home Physiological hardware-in-the-loop test bench for mechanical ventilation
Article
Licensed
Unlicensed Requires Authentication

Physiological hardware-in-the-loop test bench for mechanical ventilation

  • Philip von Platen

    Philip von Platen received his M.Sc. degree in Electrical Engineering, Information Technology and Computer Engineering from RWTH Aachen University, Aachen, Germany. He is currently working as a Senior Scientist at the Chair for Medical Information Technology, RWTH Aachen University, whilst pursuing the Dr.-Ing. degree. His main research interests are in the field of automation of lung-protective mechanical ventilation.

     EMAIL logo     , Lennard Lesch

    Lennard Lesch is studying towards his B.Sc. degree in Business Administration and Engineering: Electrical Power Engineering at the RWTH Aachen University, Aachen, Germany. His main research interests are in the field of hardware-in-the-loop test benches, 3D printing and mechanical design.

         , Arnhold Lohse

    Arnhold Lohse received his M.Sc. degree in Electrical Engineering, Information Technology and Computer Engineering from the RWTH Aachen University, Aachen, Germany. He is currently working as a Research Associate at the Chair for Medical Information Technology, RWTH Aachen University, whilst pursuing the Dr.-Ing. degree. His main research interests are in the field of phrenic nerve stimulation for ventilation.

         , Steffen Leonhardt

    Steffen Leonhardt was born in Frankfurt, Germany. He received the M.S. degree in computer engineering from the University at Buffalo, NY, USA, the Dipl.-Ing and Dr.-Ing in electrical engineering from the Technical University of Darmstadt, Darmstadt, Germany, the M.D. degree in medicine from J.W. Goethe University, Frankfurt, Germany, and the Dr.h.c. (Honorary) degree from Czech Technical University in Prague, Czech Republic. In 2003, he was appointed Full Professor and director of the Chair for Medical Information Technology at RWTH Aachen University, Aachen, Germany. In 2018, he was appointed a Distinguished Professor with IIT Madras, Chennai, India.

         and Marian Walter

    Marian Walter received the Dipl.-Ing. and Dr.-Ing. degrees in electrical engineering with a specialisation in control engineering from the Technical University of Darmstadt, Darmstadt, Germany, in 1995 and 2002, respectively. He worked for three years in R&D at Draeger Medical, Lübeck, developing anaesthesia machines. Subsequently, he was appointed Senior Scientist and Deputy Head with the Chair of Medical Information Technology, RWTH Aachen University, Aachen, Germany, in 2004. His current research interests include noncontact monitoring techniques, signal processing, and feedback control in medicine.
Published/Copyright: May 7, 2024
at - Automatisierungstechnik
From the journal at - Automatisierungstechnik Volume 72 Issue 5

Abstract

This article presents a hardware-in-the-loop system that can simulate a patient’s physiological responses to mechanical ventilation. The system includes a hardware platform with a mechatronic lung that can physically simulate the respiratory mechanics. A computational patient model replicates the pressure/volume behaviour of the lungs and the impaired gas exchange. Based on current ventilator settings, the model calculates signal curves, which are then transmitted to the physically existing sensors. This enables the test bench to reproduce the pressure/volume behaviour of the lungs and the gas exchange of a simulated patient on mechanical ventilation. In the future, the hardware-in-the-loop system could play an important role in testing and validating highly automated functions in mechanical ventilation and represent an alternative to animal testing.

Zusammenfassung

In diesem Artikel wird ein Hardware-in-the-Loop System vorgestellt, welches die physiologischen Reaktionen eines Patienten auf die maschinelle Beatmung nachbilden kann. Das System umfasst eine Hardwareplattform mit einer mechatronischen Lunge, die variable Eigenschaften der Atemmechanik physisch simulieren kann. Ein computergestütztes Patientenmodell bildet das Druck/Volumen-Verhalten der Lunge und den beeinträchtigten Gasaustausch ab. Basierend auf aktuellen Beatmungsparametern werden mithilfe des Patientenmodells Signalverläufe berechnet, die an die physisch existierenden Sensoren des Beatmungsgerätes weitergeleitet werden. Somit kann der Prüfstand das Druck/Volumen-Verhalten der Lunge und den Gasaustausch eines simulierten Patienten auf die maschinelle Beatmung abbilden. Zukünftig könnten solche Hardware-in-the-Loop Systeme als Testumgebung eine zunehmend wichtige Rolle für die Prüfung und Validierung hochautomatisierter Funktionen in der maschinellen Beatmung spielen. Außerdem stellt dieser Ansatz eine mögliche Alternative zu Tierversuchen dar.

Keywords: mechanical ventilation; hardware-in-the-loop; physiological closed-loop control; simulator
Schlagwörter: Maschinelle Beatmung; Hardware-in-the-Loop; physiologische Regelkreise; Simulator
Corresponding author: Philip von Platen, Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52062 Aachen, Germany, E-mail:

About the authors

Philip von Platen

Philip von Platen received his M.Sc. degree in Electrical Engineering, Information Technology and Computer Engineering from RWTH Aachen University, Aachen, Germany. He is currently working as a Senior Scientist at the Chair for Medical Information Technology, RWTH Aachen University, whilst pursuing the Dr.-Ing. degree. His main research interests are in the field of automation of lung-protective mechanical ventilation.

Lennard Lesch

Lennard Lesch is studying towards his B.Sc. degree in Business Administration and Engineering: Electrical Power Engineering at the RWTH Aachen University, Aachen, Germany. His main research interests are in the field of hardware-in-the-loop test benches, 3D printing and mechanical design.

Arnhold Lohse

Arnhold Lohse received his M.Sc. degree in Electrical Engineering, Information Technology and Computer Engineering from the RWTH Aachen University, Aachen, Germany. He is currently working as a Research Associate at the Chair for Medical Information Technology, RWTH Aachen University, whilst pursuing the Dr.-Ing. degree. His main research interests are in the field of phrenic nerve stimulation for ventilation.

Steffen Leonhardt

Steffen Leonhardt was born in Frankfurt, Germany. He received the M.S. degree in computer engineering from the University at Buffalo, NY, USA, the Dipl.-Ing and Dr.-Ing in electrical engineering from the Technical University of Darmstadt, Darmstadt, Germany, the M.D. degree in medicine from J.W. Goethe University, Frankfurt, Germany, and the Dr.h.c. (Honorary) degree from Czech Technical University in Prague, Czech Republic. In 2003, he was appointed Full Professor and director of the Chair for Medical Information Technology at RWTH Aachen University, Aachen, Germany. In 2018, he was appointed a Distinguished Professor with IIT Madras, Chennai, India.

Marian Walter

Marian Walter received the Dipl.-Ing. and Dr.-Ing. degrees in electrical engineering with a specialisation in control engineering from the Technical University of Darmstadt, Darmstadt, Germany, in 1995 and 2002, respectively. He worked for three years in R&D at Draeger Medical, Lübeck, developing anaesthesia machines. Subsequently, he was appointed Senior Scientist and Deputy Head with the Chair of Medical Information Technology, RWTH Aachen University, Aachen, Germany, in 2004. His current research interests include noncontact monitoring techniques, signal processing, and feedback control in medicine.

Acknowledgment

The authors would like to express their sincere gratitude to the students A. Cupo and E. Ünlüer for their support in building the electro-mechanical tube clamp. Furthermore, the authors thank L. Hinken and W. Braun (Fritz Stephan GmbH, Gackenbach) for their technical support throughout the project.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: P. von Platen was supported by the Federal Ministry of Education and Research (BMBF, Germany) grant number 13GW0539.

  5. Data availability: The raw data can be obtained on request from the corresponding author.

Appendix
Figure 8: Schematic of the physiological hardware-in-the-loop test bench for mechanical ventilation.
Figure 8:

Schematic of the physiological hardware-in-the-loop test bench for mechanical ventilation.

Table 3:

Component list for the hardware-in-the-loop test bench.

Component Type Manufacturer
Flow sensor SFM3300-D Sensirion AG
Pressure sensor AMS 6915 Analog Microelectronics GmbH
O 2 sensor FDO2 PyroScience GmbH
Stepper motor ( R rs ) NEMA 17 Stepperonline
Motor driver ( R rs ) A4988 Elegoo, Inc.
Stepper motor ( C rs ) NEMA 23 ACT Motor GmbH
Motor driver ( C rs ) DM542 Changzhou ACT Motor Co., Ltd
Leakage valve Solenoid valve Buschjost GmbH
Bellows F-1216-NBR Thodacon Werkzeugmaschinenschutz GmbH
Spring RZ-162AI Gutekunst Co.KG
Table 4:

Default parameters for the cardiopulmonary model.

Parameter Default value Source
V A,O 2 2.5 L [11]
V A,CO 2 3.2 L [11]
Q ̇ co 5.5 L/ min [13]
V tis,O 2 6 L [15]
V tis,CO 2 15 L [15]
k btps 863 [11]
β O 2 1.39 mL/g [13]
α O 2 0.0031 mL/(dL mmHg) [13]
K CO 2 0.0065/mmHg [15]
M R O 2 0.25 L/ min [13]
M R CO 2 0.2 L/ min [13]
[ eHb ] 14 g/dL [13]

References

[1] P. von Platen, A. Pomprapa, B. Lachmann, and S. Leonhardt, “The dawn of physiological closed-loop ventilation-a review,” Crit. Care, vol. 24, pp. 1–11, 2020. https://doi.org/10.1186/s13054-020-2810-1.Search in Google Scholar PubMed PubMed Central

[2] P. D. Wendel Garcia, et al.., “Closed-loop versus conventional mechanical ventilation in COVID-19 ARDS,” J. Intensive Care Med., vol. 36, no. 10, pp. 1184–1193, 2021. https://doi.org/10.1177/08850666211024139.Search in Google Scholar PubMed PubMed Central

[3] E. Bialais, et al.., “Closed-loop ventilation mode (IntelliVent®-ASV) in intensive care unit: a randomized trial,” Minerva Anestesiol., vol. 82, no. 6, pp. 657–668, 2016, 26957117.Search in Google Scholar

[4] B. Parvinian, C. Scully, H. Wiyor, A. Kumar, and S. Weininger, “Regulatory considerations for physiological closed-loop controlled medical devices used for automated critical care: food and drug administration workshop discussion topics,” Anesth. Analg., vol. 126, no. 6, pp. 1916–1925, 2018. https://doi.org/10.1213/ane.0000000000002329.Search in Google Scholar PubMed PubMed Central

[5] R. Pasteka, M. Forjan, S. Sauermann, and A. Drauschke, “Electro-mechanical lung simulator using polymer and organic human lung equivalents for realistic breathing simulation,” Sci. Rep., vol. 9, no. 1, p. 19778, 2019. https://doi.org/10.1038/s41598-019-56176-6.Search in Google Scholar PubMed PubMed Central

[6] F. Bautsch, G. Männel, and P. Rostalski, “Development of a novel low-cost lung function simulator,” Curr. Dir. Biomed. Eng., vol. 5, no. 1, pp. 557–560, 2019. https://doi.org/10.1515/cdbme-2019-0140.Search in Google Scholar

[7] J. G. Chase, T. Yuta, K. J. Mulligan, G. M. Shaw, and B. Horn, “A novel mechanical lung model of pulmonary diseases to assist with teaching and training,” BMC Pulm. Med., vol. 6, p. 21, 2006, https://doi.org/10.1186/1471-2466-6-21.Search in Google Scholar PubMed PubMed Central

[8] J. H. T. Bates, Lung Mechanics: An Inverse Modeling Approach, Cambridge, Cambridge University Press, 2009.10.1017/CBO9780511627156Search in Google Scholar

[9] The MathWorks Inc., “PID tuning algorithm for linear plant model – MATLAB pidtune,” 2023 [Online]. Available at: https://de.mathworks.com/help/control/ref/dynamicsystem.pidtune.html.Search in Google Scholar

[10] L. Chiari, G. Avanzolini, and M. Ursino, “A comprehensive simulator of the human respiratory system: validation with experimental and simulated data,” Ann. Biomed. Eng., vol. 25, no. 6, pp. 985–999, 1997. https://doi.org/10.1007/bf02684134.Search in Google Scholar

[11] J. J. Batzel, F. Kappel, D. Schneditz, and H. T. Tran, Cardiovascular and Respiratory Systems: Modeling, Analysis, and Control, Philadelphia, Society for Industrial and Applied Mathematics, 2007.10.1137/1.9780898717457Search in Google Scholar

[12] R. L. Riley and A. Cournand, “Ideal alveolar air and the analysis of ventilation-perfusion relationships in the lungs,” J. Appl. Physiol., vol. 1, no. 12, pp. 825–847, 1949. https://doi.org/10.1152/jappl.1949.1.12.825.Search in Google Scholar PubMed

[13] A. B. Lumb, Nunn’s Applied Respiratory Physiology, 8th ed. Elsevier, 2017.10.1016/B978-0-7020-6294-0.00025-3Search in Google Scholar

[14] J. W. Severinghaus, “Simple, accurate equations for human blood O2 dissociation computations,” J. Appl. Physiol., vol. 46, no. 3, pp. 599–602, 1979. https://doi.org/10.1152/jappl.1979.46.3.599.Search in Google Scholar PubMed

[15] M. C. Khoo, R. E. Kronauer, K. P. Strohl, and A. S. Slutsky, “Factors inducing periodic breathing in humans: a general model,” J. Appl. Physiol. Respir. Environ. Exerc. Physiol., vol. 53, no. 3, pp. 644–659, 1982. https://doi.org/10.1152/jappl.1982.53.3.644.Search in Google Scholar PubMed

[16] W. F. Fincham and F. T. Tehrani, “A mathematical model of the human respiratory system,” J. Biomed. Eng., vol. 5, no. 2, pp. 125–133, 1983. https://doi.org/10.1016/0141-5425(83)90030-4.Search in Google Scholar PubMed

[17] P. von Platen, et al.., “SOLVe: a closed-loop system focused on protective mechanical ventilation,” Biomed. Eng. Online, vol. 22, no. 1, p. 47, 2023. https://doi.org/10.1186/s12938-023-01111-0.Search in Google Scholar PubMed PubMed Central

[18] S. Henn, et al.., “Concept for the testing of automated functions in therapeutic medical devices: Konzept für die Prüfung von autonomen Funktionen von therapeutischen Medizingeräten,” Automatisierungstechnik, vol. 70, no. 11, pp. 946–956, 2022. https://doi.org/10.1515/auto-2022-0010.Search in Google Scholar

[19] H. C. Ngo Nguyen, “Model-based analysis of respiratory mechanics for diagnosis of cardiopulmonary diseases,” Ph.D. dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2019.Search in Google Scholar
Received: 2023-11-28
Accepted: 2024-03-27
Published Online: 2024-05-07
Published in Print: 2024-05-27

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Special issue AUTOMED
  4. Survey
  5. Reviewing the potential of hearables for the assessment of bruxism
  6. Applications
  7. Model predictive control of blood glucose in critically ill patients using Gaussian processes
  8. Muscle fatigue detection based on sEMG signal using autocorrelation function and neural networks
  9. A switching lung mechanics model for detection of expiratory flow limitation
  10. Physiological hardware-in-the-loop test bench for mechanical ventilation
  11. Gaussian process-based nonlinearity compensation for pneumatic soft actuators
  12. Design, development, and optimization of the Somnomat Casa: a rocking bed for sleep studies and nocturnal interventions in home settings
  13. Tools
  14. Classroom-ready open-source educational exoskeleton for biomedical and control engineering
  15. An open-source research platform for mechanical ventilation based on Simulink® and STM32 nucleo
  16. Designing the user interface of a ventilator under the constraints of a pandemic
https://doi.org/10.1515/auto-2023-0215
Keywords for this article
mechanical ventilation; hardware-in-the-loop; physiological closed-loop control; simulator

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. Special issue AUTOMED
  4. Survey
  5. Reviewing the potential of hearables for the assessment of bruxism
  6. Applications
  7. Model predictive control of blood glucose in critically ill patients using Gaussian processes
  8. Muscle fatigue detection based on sEMG signal using autocorrelation function and neural networks
  9. A switching lung mechanics model for detection of expiratory flow limitation
  10. Physiological hardware-in-the-loop test bench for mechanical ventilation
  11. Gaussian process-based nonlinearity compensation for pneumatic soft actuators
  12. Design, development, and optimization of the Somnomat Casa: a rocking bed for sleep studies and nocturnal interventions in home settings
  13. Tools
  14. Classroom-ready open-source educational exoskeleton for biomedical and control engineering
  15. An open-source research platform for mechanical ventilation based on Simulink® and STM32 nucleo
  16. Designing the user interface of a ventilator under the constraints of a pandemic
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 10.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/auto-2023-0215/pdf
Scroll to top button