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Determining Cardiopulmonary Resuscitation Parameters with Differential Evolution Optimization of Sinusoidal Curves

TitleDetermining Cardiopulmonary Resuscitation Parameters with Differential Evolution Optimization of Sinusoidal Curves
Publication TypeConference Paper
Year of Publication2018
AuthorsLins C., Klausen A., Fudickar S., Hellmers S., Lipprandt M., Röhrig R., Hein A.
Conference NameProceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies
Pages665 - 670
Date Published2018
PublisherSCITEPRESS - Science and Technology Publications
Conference LocationFunchal - Madeira, Portugal
ISBN Number978-989-758-281-3
KeywordsCardiac Massage, CPR training, Curve Fitting, Evolutionary Algorithm, UNIAMT, UNILLM
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 fre-quency 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 an-gles 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.

URLhttp://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006732806650670
DOI10.5220/0006732806650670
Created at November 22, 2021 - 2:56pm by Fudickar.

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