Towards dynamically configurable context recognition systems

Kai Kunze, David Bannach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

General representation, abstraction and exchange definitions are crucial for dynamically configurable context recognition. However, to evaluate potential definitions, suitable standard datasets are needed. This paper presents our effort to create and maintain large scale, multimodal standard datasets for context recognition research. 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 abstractions 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 recognition research. Applying higher level context reasoning on the gathered context data is a obvious extension to our work.

Original languageEnglish
Title of host publicationActivity Context Representation
Subtitle of host publicationTechniques and Languages - Papers from the 2012 AAAI Workshop, Technical Report
Pages60-64
Number of pages5
Publication statusPublished - 2012 Dec 1
Externally publishedYes
Event2012 AAAI Workshop - Toronto, ON, Canada
Duration: 2012 Jul 232012 Jul 23

Publication series

NameAAAI Workshop - Technical Report
VolumeWS-12-05

Other

Other2012 AAAI Workshop
CountryCanada
CityToronto, ON
Period12/7/2312/7/23

ASJC Scopus subject areas

  • Engineering(all)

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