Online personalization of cross-subjects based activity recognition models on wearable devices

Sztyler, Timo ; Stuckenschmidt, Heiner

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Document Type: Conference or workshop publication
Year of publication: 2017
Book title: PerCom 2017 : The Fifteenth Annual IEEE International Conference on Pervasive Computing and Communications : March 13-17, 2017, Big Island, HI, USA
Page range: 180-189
Conference title: IEEE International Conference on Pervasive Computing and Communications 2017 (PerCom)
Location of the conference venue: Kona, Big Island, HI
Date of the conference: 13.-17.03.2017
Publisher: Marchiori, Alan
Place of publication: Piscataway, NJ
Publishing house: IEEE Computer Society
ISBN: 978-1-5090-4328-6 , 978-1-5090-4327-9
ISSN: 2474-249X
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Subject: 004 Computer science, internet
Abstract: Human activity recognition using wearable devices is an active area of research in pervasive computing. In our work, we address the problem of reducing the effort for training and adapting activity recognition approaches to a specific person. We focus on the problem of cross-subjects based recognition models and introduce an approach that considers physical characteristics. Further, to adapt such a model to the behavior of a new user, we present a personalization approach that relies on online and active machine learning. In this context, we use online random forest as a classifier to continuously adapt the model without keeping the already seen data available and an active learning approach that uses user-feedback for adapting the model while minimizing the effort for the new user. We test our approaches on a real world data set that covers 15 participants, 8 common activities, and 7 different on-body device positions. We show that our cross-subjects based approach performs constantly +3% better than the standard approach. Further, the personalized cross-subjects models, gained through user-feedback, recognize dynamic activities with an F-measure of 87% where the user has significantly less effort than collecting and labeling data.

Dieser Eintrag ist Teil der Universitätsbibliographie.

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