Next Article in Journal
Infrastructure for Integration of Legacy Electrical Equipment into a Smart-Grid Using Wireless Sensor Networks
Next Article in Special Issue
Feasibility of Optical Coherence Tomography (OCT) for Intra-Operative Detection of Blood Flow during Gastric Tube Reconstruction
Previous Article in Journal
Stand-Off Detection of Alcohol Vapors Exhaled by Humans
Previous Article in Special Issue
Vibration and Noise in Magnetic Resonance Imaging of the Vocal Tract: Differences between Whole-Body and Open-Air Devices
Open AccessArticle

Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data

1
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 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. View Full-Text
Keywords: image processing; machine learning; perception; sensor fusion; segmentation; RGB-D; thermal camera; kinect; human body weight; stroke image processing; machine learning; perception; sensor fusion; segmentation; RGB-D; thermal camera; kinect; human body weight; stroke
Show Figures

Figure 1

  • 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 Style

Pfitzner, 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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop