Recognizing interleaved and concurrent activities using qualitative and quantitative temporal relationships


Helaoui, Rim ; Niepert, Mathias ; Stuckenschmidt, Heiner



DOI: https://doi.org/10.1016/j.pmcj.2011.08.004
URL: https://www.sciencedirect.com/science/article/pii/...
Additional URL: https://www.researchgate.net/publication/220310536...
Document Type: Article
Year of publication: 2011
The title of a journal, publication series: Pervasive and Mobile Computing
Volume: 7
Issue number: 6
Page range: 660-670
Place of publication: Amsterdam [u.a.]
Publishing house: Elsevier
ISSN: 1574-1192
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
Keywords (English): Pervasive computing; Activity recognition; Statistical relational learning; Markov logic networks; RFID; Temporal reasoning
Abstract: The majority of 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 lightweight 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. We also show the viability and the benefit of exploiting both qualitative and quantitative temporal relationships like the duration of the activities and their temporal order. 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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Helaoui, Rim ; Niepert, Mathias ; Stuckenschmidt, Heiner ORCID: 0000-0002-0209-3859 (2011) Recognizing interleaved and concurrent activities using qualitative and quantitative temporal relationships. Pervasive and Mobile Computing Amsterdam [u.a.] 7 6 660-670 [Article]


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