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Recognizing interleaved and concurrent activities using qualitative and quantitative temporal relationships
Helaoui, Rim
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Niepert, Mathias
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Stuckenschmidt, Heiner
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DOI:
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https://doi.org/10.1016/j.pmcj.2011.08.004
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URL:
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https://www.sciencedirect.com/science/article/pii/...
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Weitere URL:
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https://www.researchgate.net/publication/220310536...
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Dokumenttyp:
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Zeitschriftenartikel
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Erscheinungsjahr:
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2011
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Titel einer Zeitschrift oder einer Reihe:
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Pervasive and Mobile Computing
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Band/Volume:
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7
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Heft/Issue:
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6
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Seitenbereich:
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660-670
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Ort der Veröffentlichung:
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Amsterdam [u.a.]
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Verlag:
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Elsevier
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ISSN:
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1574-1192
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
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Fachgebiet:
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004 Informatik
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Freie Schlagwörter (Englisch):
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Pervasive computing; Activity recognition; Statistical relational learning; Markov logic networks; RFID; Temporal reasoning
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Abstract:
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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.
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Suche Autoren in
BASE:
Helaoui, Rim
;
Niepert, Mathias
;
Stuckenschmidt, Heiner
Google Scholar:
Helaoui, Rim
;
Niepert, Mathias
;
Stuckenschmidt, Heiner
ORCID:
Helaoui, Rim, Niepert, Mathias and Stuckenschmidt, Heiner  ORCID: https://orcid.org/0000-0002-0209-3859
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