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Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data
Zitieren Sie bitte immer diese 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.…
Autor(en): | Christian PfitznerORCiD, Stefan May, Andreas NüchterORCiD |
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URN: | urn:nbn:de:bvb:20-opus-176642 |
Dokumentart: | Artikel / Aufsatz in einer Zeitschrift |
Institute der Universität: | Fakultät für Mathematik und Informatik / Institut für Informatik |
Sprache der Veröffentlichung: | Englisch |
Titel des übergeordneten Werkes / der Zeitschrift (Englisch): | Sensors |
Erscheinungsjahr: | 2018 |
Band / Jahrgang: | 18 |
Heft / Ausgabe: | 5 |
Seitenangabe: | 1311 |
Originalveröffentlichung / Quelle: | Sensors 2018, 18(5):1311. DOI: 10.3390/s18051311 |
DOI: | https://doi.org/10.3390/s18051311 |
Allgemeine fachliche Zuordnung (DDC-Klassifikation): | 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 | |
Freie Schlagwort(e): | RGB-D; human body weight; image processing; kinect; machine learning; perception; segmentation; sensor fusion; stroke; thermal camera |
Datum der Freischaltung: | 26.02.2019 |
Sammlungen: | Open-Access-Publikationsfonds / Förderzeitraum 2018 |
Lizenz (Deutsch): | CC BY: Creative-Commons-Lizenz: Namensnennung 4.0 International |