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Authors: Christian Lins 1 ; Andreas Klausen 2 ; Sebastian Fudickar 2 ; Sandra Hellmers 2 ; Myriam Lipprandt 2 ; Rainer Röhrig 2 and Andreas Hein 2

Affiliations: 1 OFFIS - Institute for Information Technology, Germany ; 2 Carl von Ossietzky University, Germany

ISBN: 978-989-758-281-3

Keyword(s): CPR Training, Curve Fitting, Evolutionary Algorithm, Cardiac Massage.

Abstract: In this paper, we present a robust sinusoidal curve fitting method based on the Differential Evolution (DE) algorithm for determining cardiopulmonary resuscitation (CPR) parameters – naming chest compression frequency and depth – from skeletal motion data. Our implementation uses skeletal data from the RGB-D (RGB + Depth) Kinect v2 sensor and works without putting non-sensor related constraints such as specific view angles or distance to the system. Our approach is intended to be part of a robust and easy-to-use feedback system for CPR training, allowing its unsupervised training. We compare the sensitivity of our DE implementation with data recorded by a Laerdal Resusci Anne mannequin. Results show that the frequency of the DE-based CPR is recognized with a variance of 4:4 bpm (4.1%) in comparison to the reference of the Resusci Anne mannequin.

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Paper citation in several formats:
Lins C., Klausen A., Fudickar S., Hellmers S., Lipprandt M., Röhrig R. and Hein A. (2018). Determining Cardiopulmonary Resuscitation Parameters with Differential Evolution Optimization of Sinusoidal Curves.In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: AI4Health, ISBN 978-989-758-281-3, pages 665-670. DOI: 10.5220/0006732806650670

@conference{ai4health18,
author={Christian Lins and Andreas Klausen and Sebastian Fudickar and Sandra Hellmers and Myriam Lipprandt and Rainer Röhrig and Andreas Hein},
title={Determining Cardiopulmonary Resuscitation Parameters with Differential Evolution Optimization of Sinusoidal Curves},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: AI4Health,},
year={2018},
pages={665-670},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006732806650670},
isbn={978-989-758-281-3},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: AI4Health,
TI - Determining Cardiopulmonary Resuscitation Parameters with Differential Evolution Optimization of Sinusoidal Curves
SN - 978-989-758-281-3
AU - Lins C.
AU - Klausen A.
AU - Fudickar S.
AU - Hellmers S.
AU - Lipprandt M.
AU - Röhrig R.
AU - Hein A.
PY - 2018
SP - 665
EP - 670
DO - 10.5220/0006732806650670

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