TY  - CONF
T1  - Modeling and simulation of sensor orientation errors in garments
A1  - Harms, Holger
A1  - Amft, Oliver
A1  - Tröster, Gerhard
TI  - BodyNets09: In Proceedings of the 4th International Conference on Body Area Networks
Y1  - 2009
N2  - We report in this paper on a novel modeling and simulation
approach to predict orientation errors of garment-attached
sensors and their e®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:§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.
ER  -