From user-independent to personal human activity recognition models using smartphone sensors
Siirtola, Pekka; Koskimäki, Heli; Röning, Juha (2016-04-27)
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, i6doc.com publ., ISBN 978-287587027-8. Available from http://www.i6doc.com/en/.
© 2016 ESANN and the authors. Published in this repository with the kind permission of the publisher.
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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