Recognizing Interleaved and Concurrent Activities: A Statistical-Relational Approach

Helaoui, Rim ; Niepert, Mathias ; Stuckenschmidt, Heiner

Document Type: Conference or workshop publication
Year of publication: 2011
Book title: 2011 IEEE International Conference on Pervasive Computing and Communications : proceedings
Page range: 1-9
Conference title: PerCom 2011
Location of the conference venue: Seattle, Wash.
Date of the conference: 21-25 March 2011
Place of publication: New York, NY [u.a.]
Publishing house: IEEE Press
ISBN: 978-1-4244-9530-6
Publication language: English
Institution: Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
School of Business Informatics and Mathematics > Praktische Informatik II (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: A majority of the approaches to activity recognition in sensor environments are either based on manually constructed rules for recognizing activities or lack the ability to incorporate complex temporal dependencies. Furthermore, in many cases, the rather unrealistic assumption is made that the subject carries out only one activity at a time. In this paper, we describe the use of Markov logic as a declarative framework for recognizing interleaved and concurrent activities incorporating both input from pervasive light-weight sensor technology and common-sense background knowledge. In particular, we assess its ability to learn statistical-temporal models from training data and to combine these models with background knowledge to improve the overall recognition accuracy. To this end, we propose two Markov logic formulations for inferring the foreground activity as well as each activities’ start and end times. We evaluate the approach on an established dataset. where it outperforms state-of-the-art algorithms for activity recognition.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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