NECTAR: Knowledge-based collaborative active learning for activity recognition

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

Document Type: Conference or workshop publication
Year of publication: 2018
Book title: 2018 IEEE International Conference on Pervasive Computing and Communications : Athens, Greece, March 19-23, 2018
Page range: 1-10
Conference title: PerCom 2018
Location of the conference venue: Athens, Greece
Date of the conference: 19.-23.03.2018
Place of publication: Piscataway, NJ
Publishing house: IEEE Computer Society
ISBN: 978-1-5386-3228-4 , 978-1-5386-3227-7 , 978-1-5386-3226-0
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: Due to the emerging popularity of pervasive healthcare applications, tools for monitoring activities in smart homes are gaining momentum. Existing methods mainly rely on supervised learning algorithms for recognizing activities based on sensor data. A key issue with those approaches is the acquisition of comprehensive training sets of activities. Indeed, that task incurs relevant costs in terms of manual labeling effort; moreover, labeling by external observers violates the individual's privacy. For these reasons, there is an increasing interest in unsupervised activity recognition methods. A popular approach relies on knowledge-based models expressed by ontologies of activities, environment and sensors. Unfortunately, those models require significant knowledge engineering efforts, and are often limited to a specific application. Our intuition is that a generic knowledge-based model of activities can be refined to target specific individuals and environments by collaboratively acquiring feedback from inhabitants. Specifically, we propose a collaborative active learning method to refine correlations among sensor events and activity types that are initially extracted from a high-level ontology. Generic correlations are personalized to each target smart-home considering the similarity between the feedback target and the feedback provider in terms of environment and inhabitant's profiles. Moreover, thanks to this method, new sensors installed in the home are seamlessly integrated in the recognition framework. In order to reduce the burden of providing feedback, we also propose a technique to carefully select the conditions that trigger a feedback request. We conducted experiments with a real-world dataset and a generic ontology of activities. Results show that our hybrid method outperforms state-of-the-art supervised and unsupervised activity recognition techniques while triggering an acceptable number of feedback queries.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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