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Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data

Please always quote using this URN: urn:nbn:de:bvb:20-opus-176642
  • This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. BesidesThis paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.show moreshow less

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Metadaten
Author: Christian PfitznerORCiD, Stefan May, Andreas NüchterORCiD
URN:urn:nbn:de:bvb:20-opus-176642
Document Type:Journal article
Faculties:Fakultät für Mathematik und Informatik / Institut für Informatik
Language:English
Parent Title (English):Sensors
Year of Completion:2018
Volume:18
Issue:5
Pagenumber:1311
Source:Sensors 2018, 18(5):1311. DOI: 10.3390/s18051311
DOI:https://doi.org/10.3390/s18051311
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 614 Inzidenz und Prävention von Krankheiten
Tag:RGB-D; human body weight; image processing; kinect; machine learning; perception; segmentation; sensor fusion; stroke; thermal camera
Release Date:2019/02/26
Collections:Open-Access-Publikationsfonds / Förderzeitraum 2018
Licence (German):License LogoCC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International