Towards dynamically configurable context recognition systems
Kai S. Kunze, David Bannach · 2012
General representation, abstraction and exchange def-initions are crucial for dynamically configurable con-text recognition. However, to evaluate potential defini-tions, suitable standard datasets are needed. This paper presents our effort to create and maintain large scale, multimodal standard datasets for context recognition re-search. We ourselves used these datasets in previous research to deal with placement effects and presented low-level sensor abstractions in motion based on-body sensing. Researchers, conducting novel data collections, can rely on the toolchain and the the low-level sensor abstrac-tions summarized in this paper. Additionally, they can draw from our experiences developing and conducting context recognition experiments. Our toolchain is already a valuable rapid prototyping tool. Still, we plan to extend it to crowd-based sensing, enabling the general public to gather context data, learn more about their lives and contribute to context recog-nition research. Applying higher level context reasoning on the gathered context data is a obvious extension to our work.