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Distributed Computing and Artificial Intelligence pp 617–627Cite as

User-Independent Human Activity Recognition Using a Mobile Phone: Offline Recognition vs. Real-Time on Device Recognition

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Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 151))

Abstract

Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemeted to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject’s trousers. Two classifiers were compared, knn (k nearest neighbours) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates. Real-time recognition on the device was tested by seven subjects, three of which were subjects who had not collected data to train the models. Activity recognition rates on the smartphone were encouracing, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. The real-time recognition accuracy using QDA was as high as 95.8%, while using knn it was 93.9%.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Oulu, P.O. BOX 4500, FI-90014, Finland, Oulu

    Pekka Siirtola & Juha Röning

Authors
  1. Pekka Siirtola
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  2. Juha Röning
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Corresponding author

Correspondence to Pekka Siirtola .

Editor information

Editors and Affiliations

  1. Dept. Computer Science &, Intelligent Systems, Osaka Prefecture University, Gakuen-chu 1-1, Sakai, Osaka, 599-8531, Japan

    Sigeru Omatu

  2. , Department of Computing Science, University of Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Juan F. De Paz Santana

  3. , Department of Computing Science, University of Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Sara Rodríguez González

  4. Escuela Politécnica Superior (EPS), Depto. Informática, Universidad Carlos III de Madrid, Avenida de la Universidad Carloss III 22, Madrid, 28270, Spain

    Jose M. Molina

  5. , Data Processing and Simulation Group, Universidad Politécnica de Madrid, Calle de Ramiro de Maeztu, 7, Madrid, 28040, Spain

    Ana M. Bernardos

  6. Faculty of Science, Department of Computing Science, University of Salamanca, Plaza de la Merced S/N, Salamanca, 37008, Spain

    Juan M. Corchado Rodríguez

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© 2012 Springer-Verlag Berlin Heidelberg

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Siirtola, P., Röning, J. (2012). User-Independent Human Activity Recognition Using a Mobile Phone: Offline Recognition vs. Real-Time on Device Recognition. In: Omatu, S., De Paz Santana, J., González, S., Molina, J., Bernardos, A., Rodríguez, J. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28765-7_75

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  • DOI: https://doi.org/10.1007/978-3-642-28765-7_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28764-0

  • Online ISBN: 978-3-642-28765-7

  • eBook Packages: EngineeringEngineering (R0)

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