TY  - JOUR
A1  - Pfitzner, Christian
A1  - May, Stefan
A1  - Nüchter, Andreas
T1  - Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data
JF  - Sensors
N2  - 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.
KW  - RGB-D
KW  - human body weight
KW  - image processing
KW  - kinect
KW  - machine learning
KW  - perception
KW  - segmentation
KW  - sensor fusion
KW  - stroke
KW  - thermal camera
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-176642
VL  - 18
IS  - 5
ER  - 
TY  - JOUR
A1  - Ehrenfeld, Stephan
A1  - Herbort, Oliver
A1  - Butz, Martin V.
T1  - Modular neuron-based body estimation: maintaining consistency over different limbs, modalities, and frames of reference
JF  - Frontiers in Computational Neuroscience
N2  - This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control.
KW  - information
KW  - posterior parietal cortex
KW  - hand
KW  - population code
KW  - conflicting information
KW  - multimodal interaction
KW  - probabilistic inference
KW  - modular body schema
KW  - sensor fusion
KW  - multisensory perception
KW  - fusion
KW  - representation
KW  - multisensory processing
KW  - see
KW  - implementation
KW  - perspective
KW  - multisensory integration
KW  - population codes
Y1  - 2013
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-122253
VL  - 7
IS  - 148
ER  -