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  - Glémarec, Yann
A1  - Lugrin, Jean-Luc
A1  - Bosser, Anne-Gwenn
A1  - Collins Jackson, Aryana
A1  - Buche, Cédric
A1  - Latoschik, Marc Erich
T1  - Indifferent or Enthusiastic? Virtual Audiences Animation and Perception in Virtual Reality
JF  - Frontiers in Virtual Reality
N2  - In this paper, we present a virtual audience simulation system for Virtual Reality (VR). The system implements an audience perception model controlling the nonverbal behaviors of virtual spectators, such as facial expressions or postures. Groups of virtual spectators are animated by a set of nonverbal behavior rules representing a particular audience attitude (e.g., indifferent or enthusiastic). Each rule specifies a nonverbal behavior category: posture, head movement, facial expression and gaze direction as well as three parameters: type, frequency and proportion. In a first user-study, we asked participants to pretend to be a speaker in VR and then create sets of nonverbal behaviour parameters to simulate different attitudes. Participants manipulated the nonverbal behaviours of single virtual spectator to match a specific levels of engagement and opinion toward them. In a second user-study, we used these parameters to design different types of virtual audiences with our nonverbal behavior rules and evaluated their perceptions. Our results demonstrate our system’s ability to create virtual audiences with three types of different perceived attitudes: indifferent, critical, enthusiastic. The analysis of the results also lead to a set of recommendations and guidelines regarding attitudes and expressions for future design of audiences for VR therapy and training applications.
KW  - virtual reality
KW  - perception
KW  - nonverbal behavior
KW  - interaction
KW  - virtual agent
KW  - virtual audience
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-259328
VL  - 2
ER  -