Authors:
Andreas Jahn
1
;
Sven Tomforde
2
;
Michel Morold
1
;
Klaus David
1
and
Bernhard Sick
2
Affiliations:
1
Communication Technology Group, University of Kassel, Kassel and Germany
;
2
Intelligent Embedded Systems Group, University of Kassel, Kassel and Germany
Keyword(s):
Activity Recognition, Self-adapting, Organic Computing, Cooperation, Active Learning.
Related
Ontology
Subjects/Areas/Topics:
Mobile and Pervasive Computing
;
Telecommunications
;
Ubiquitous Computing Systems and Services
Abstract:
Activity Recognition (AR) aims at deriving high-level knowledge about human activities and the situation in the human’s environment. Although being a well-established research field, several basic issues are still insufficiently solved, including extensibility of an AR system at runtime, adaption of classification models to a very specific behaviour of a user, or utilising of all information available, including other AR systems within range. To overcome these limitations, the cooperation of AR systems including sporadic interaction with humans and consideration of other information sources is proposed in this article as a basic new way to lead to a new generation of “smart” AR systems. Cooperation of AR systems will take place at several stages of an AR chain: at the level of recognised motion primitives (e.g. arm movement), at the level of detected low-level activities (e.g. writing), and/or at the level of identified high-level activities (e.g. participating in a meeting). This ar
ticle outlines a possible architectural concept, describes the resulting challenges, and proposes a research roadmap towards cooperative AR systems.
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