Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data
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Department of Electrical Engineering, Precision Engineering, Information Technology at the Techniche Hochschule Nürnberg Georg Simon Ohm; Keßlerplatz 12, 90489 Nuremberg, Germany
2
Department of Informatics VII: Robotics and Telematics at the Julius-Maximilians University Würzburg, Am Hubland, 97074 Wuerzburg, Germany
*
Authors to whom correspondence should be addressed.
Sensors 2018, 18(5), 1311; https://doi.org/10.3390/s18051311
Received: 28 January 2018 / Revised: 3 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
(This article belongs to the Special Issue Optical Methods in Sensing and Imaging for Medical and Biological Applications)
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. 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.
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Keywords:
image processing; machine learning; perception; sensor fusion; segmentation; RGB-D; thermal camera; kinect; human body weight; stroke
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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Externally hosted supplementary file 1
Doi: 10.17605/OSF.IO/RHQ3M
Link: https://osf.io/rhq3m/
Description: RGB-D(-T) Datasets for Body Weight Estimation of Stroke Patients from the Libra3D Project
MDPI and ACS Style
Pfitzner, C.; May, S.; Nüchter, A. Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data. Sensors 2018, 18, 1311. https://doi.org/10.3390/s18051311
AMA Style
Pfitzner C, May S, Nüchter A. Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data. Sensors. 2018; 18(5):1311. https://doi.org/10.3390/s18051311
Chicago/Turabian StylePfitzner, Christian; May, Stefan; Nüchter, Andreas. 2018. "Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data" Sensors 18, no. 5: 1311. https://doi.org/10.3390/s18051311
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