  This article describes an approach to detect the wearing location of smart
devices worn by pedestrians and cyclists. The detection, which is based solely
on the sensors of the smart devices, is important context-information which can
be used to parametrize subsequent algorithms, e.g. for dead reckoning or
intention detection to improve the safety of vulnerable road users. The wearing
location recognition can in terms of Organic Computing (OC) be seen as a step
towards self-awareness and self-adaptation. For the wearing location detection
a two-stage process is presented. It is subdivided into moving detection
followed by the wearing location classification. Finally, the approach is
evaluated on a real world dataset consisting of pedestrians and cyclists.
