University of Oulu

Pekka Siirtola, Heli Koskimäki and Juha Röning. From user-independent to personal human activity recognition models using smartphone sensors. ESANN 2016 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 27-29 April 2016, publ., ISBN 978-287587027-8. Available from

From user-independent to personal human activity recognition models using smartphone sensors

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Author: Siirtola, Pekka1; Koskimäki, Heli1; Röning, Juha1
Organizations: 1Biomimetics and Intelligent Systems Group, P.O. BOX 4500, FI-90014, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.2 MB)
Persistent link:
Language: English
Published: 2016
Publish Date: 2017-02-14


In this study, a novel method to obtain user-dependent human activity recognition models unobtrusively by using the sensors of a smartphone is presented. The recognition consists of two models: sensor fusion-based user-independent model for data labeling and single sensor-based user-dependent model for final recognition. The functioning of the presented method is tested with human activity data set, including data from accelerometer and magnetometer, and with two classifiers. Comparison of the detection accuracies of the proposed method to traditional user-independent model shows that the presented method has potential, in nine cases out of ten it is better than the traditional method, but more experiments using different sensor combinations should be made to show the full potential of the method.
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ISBN: 978-287587027-8
Pages: 471 - 476
Article number: ES2016-126
Licence condition: Published in this repository with the kind permission of the publisher.