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@INPROCEEDINGS{,
     author = {Harms, Holger and Amft, Oliver and Tr{\"{o}}ster, Gerhard},
      title = {Modeling and simulation of sensor orientation errors in garments},
  booktitle = {BodyNets09: In Proceedings of the 4th International Conference on Body Area Networks},
       year = {2009},
   abstract = {We report in this paper on a novel modeling and simulation
approach to predict orientation errors of garment-attached
sensors and their e{\textregistered}ect on posture classi¯cation. Such errors
occur frequently in smart garment implementations and can
reduce sensor information quality for movement and posture
recognition. A kinematic model of the human upper-body
was developed to simulate upper limb postures and the out-
put of virtual 3D acceleration sensors. The model was en-
hanced with a statistical approximation of garment-related
orientation errors. We derived this model from acceleration
sensor deviations between skin- and garment-attached units.
The feasibility of our body model and the garment-attached
sensor deviation was validated in experimental data. We
compared the classi¯cation performance for ten posture types
that are frequently used in shoulder rehabilitation. In a val-
idation set of 7 participants we observed similar classi¯er
confusions and a relative error of 2.6\% (SD:{\S}3.2\%) between
simulation and experiment. We utilized the model to esti-
mate classi¯cation performance for further simulated textile
error distributions. Our simulations showed that classi¯ca-
tion performance depends on low deviations of an acceler-
ation sensor at the lower arm, while a sensor at the upper
arm was less critical. Moreover, we included magnetic ¯eld
sensors in our simulation. With the help of this additional
modality our posture classi¯cation performance increased by
18\%. We conclude that simulation of skin- and garment-
attached sensors is a feasible approach to expedite design
and development process of smart garments.}
}