@article{PfitznerMayNuechter2018,
  author    = {Pfitzner, Christian and May, Stefan and N{\"u}chter, Andreas},
  title     = {Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data},
  series = {Sensors},
  volume    = {18},
  journal   = {Sensors},
  number    = {5},
  doi       = {10.3390/s18051311},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176642},
  pages     = {1311},
  year      = {2018},
  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.},
  language  = {en}
}
@article{GlemarecLugrinBosseretal.2021,
  author    = {Gl{\´e}marec, Yann and Lugrin, Jean-Luc and Bosser, Anne-Gwenn and Collins Jackson, Aryana and Buche, C{\´e}dric and Latoschik, Marc Erich},
  title     = {Indifferent or Enthusiastic? Virtual Audiences Animation and Perception in Virtual Reality},
  series = {Frontiers in Virtual Reality},
  volume    = {2},
  journal   = {Frontiers in Virtual Reality},
  doi       = {10.3389/frvir.2021.666232},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259328},
  pages     = {666232},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}