Determining Cardiopulmonary Resuscitation Parameters with Differential Evolution Optimization of Sinusoidal Curves
Title | Determining Cardiopulmonary Resuscitation Parameters with Differential Evolution Optimization of Sinusoidal Curves |
Publication Type | Conference Paper |
Year of Publication | 2018 |
Authors | Lins C., Klausen A., Fudickar S., Hellmers S., Lipprandt M., Röhrig R., Hein A. |
Conference Name | Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies |
Pages | 665 - 670 |
Date Published | 2018 |
Publisher | SCITEPRESS - Science and Technology Publications |
Conference Location | Funchal - Madeira, Portugal |
ISBN Number | 978-989-758-281-3 |
Keywords | Cardiac 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. |
URL | http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006732806650670 |
DOI | 10.5220/0006732806650670 |