Towards Cooperative Self-adapting Activity Recognition

Andreas Jahn, Sven Tomforde, Michel Morold, Klaus David, Bernhard Sick

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 article outlines a possible architectural concept, describes the resulting challenges, and proposes a research roadmap towards cooperative AR systems.

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Paper Citation


in Harvard Style

Jahn A., Tomforde S., Morold M., David K. and Sick B. (2018). Towards Cooperative Self-adapting Activity Recognition.In Proceedings of the 8th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PEC, ISBN 978-989-758-322-3, pages 77-84. DOI: 10.5220/0006856100770084


in Bibtex Style

@conference{pec18,
author={Andreas Jahn and Sven Tomforde and Michel Morold and Klaus David and Bernhard Sick},
title={Towards Cooperative Self-adapting Activity Recognition},
booktitle={Proceedings of the 8th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PEC,},
year={2018},
pages={77-84},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006856100770084},
isbn={978-989-758-322-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PEC,
TI - Towards Cooperative Self-adapting Activity Recognition
SN - 978-989-758-322-3
AU - Jahn A.
AU - Tomforde S.
AU - Morold M.
AU - David K.
AU - Sick B.
PY - 2018
SP - 77
EP - 84
DO - 10.5220/0006856100770084