Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning

Riboni, Daniele ; Sztyler, Timo ; Civitarese, Gabriele ; Stuckenschmidt, Heiner

DOI: https://doi.org/10.1145/2971648.2971691
URL: http://ubicomp.org/ubicomp2016/program/online-proc...
Additional URL: http://publications.wim.uni-mannheim.de/informatik...
Document Type: Conference or workshop publication
Year of publication: 2016
Book title: UbiComp '16 : proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing : Sep. 12-16, co-located with the International Symposium on Wearable Computers (ISWC) 2016
Page range: 1-12
Conference title: UbiComp/ISWC 2016
Location of the conference venue: Heidelberg, Germany
Date of the conference: 12.-16.09.2016
Publisher: Lukowicz, Paul
Place of publication: New York, NY
Publishing house: ACM
ISBN: 978-1-4503-4461-6
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. In this field, most activity recognition systems rely on supervised learning methods to extract activity models from labeled datasets. An inherent problem of that approach consists in the acquisition of comprehensive activity datasets, which is expensive and may violate individuals’ privacy. The problem is particularly challenging when focusing on complex ADLs, which are characterized by large intra- and inter-personal variability of execution. In this paper, we propose an unsupervised method to recognize complex ADLs exploiting the semantics of activities, context data, and sensing devices. Through ontological reasoning, we derive semantic correlations among activities and sensor events. By matching observed sensor events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of state of the art supervised approaches.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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