%0 Journal Article %1 SENSORS2018 %A Pfitzner, C. %A May, S. %A Nüchter, A. %D 2018 %J Sensors %K cp %N 5 %R 10.3390/s18051311 %T Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data %U https://robotik.informatik.uni-wuerzburg.de/telematics/download/mdpi2018.pdf %V 18 %X 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.

@article{SENSORS2018, abstract = {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.}, added-at = {2018-05-07T08:46:57.000+0200}, author = {Pfitzner, C. and May, S. and N{\"u}chter, A.}, biburl = {https://www.bibsonomy.org/bibtex/2cd2dafc7758c2d40219cfb6cc5ceebe7/baywiss1}, doi = {10.3390/s18051311}, interhash = {1d3cfb6d3f1494ea428fad3ad26ed670}, intrahash = {cd2dafc7758c2d40219cfb6cc5ceebe7}, journal = {Sensors}, keywords = {cp}, month = {April}, number = 5, timestamp = {2019-03-25T11:55:16.000+0100}, title = {Body Weight Estimation for Dose-Finding and Health Monitoring of Lying, Standing and Walking Patients Based on RGB-D Data}, url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/mdpi2018.pdf}, volume = 18, year = 2018 }