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User Daily Activity Classification from Accelerometry Using Feature Selection and SVM

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Bio-Inspired Systems: Computational and Ambient Intelligence (IWANN 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5517))

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Abstract

User daily activity monitoring is useful for physicians in geriatrics and rehabilitation as a indicator of user health and mobility. Real time activities recognition by means of a processing node including a triaxial accelerometer sensor situated in the user’s chest is the main goal for the presented experimental work. A two-phases procedure implementing features extraction from the raw signal and SVM-based classification has been designed for real time monitoring. The designed procedure showed an overall accuracy of 92% when recogninzing experimentation performed in daily conditions.

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References

  1. Bidargaddi, N., Sarela, A., Klingbeil, L., Karunanithi, M.: Detecting walking activity in cardiac rehabilitation by using accelerometer, December 2007, pp. 555–560 (2007)

    Google Scholar 

  2. Giansanti, D.: Does centripetal acceleration affect trunk flexion monitoring by means of accelerometers? Physiological Measurement 27(10), 999–1008 (2006)

    Article  Google Scholar 

  3. Bouten, C.V.C., Koekkoek, K.T.M., Verduin, M., Kodde, R., Janssen, J.D.: A triaxial accelerometer and portable data processing unit for the assessment of daily physical activity. IEEE Transactions on Biomedical Engineering 44(3), 136–147 (1997)

    Article  Google Scholar 

  4. Begg, R.K., Palaniswami, M., Owen, B.: Support vector machines for automated gait classification. IEEE Transactions on Biomedical Engineering 52(5), 828–838 (2005)

    Article  Google Scholar 

  5. Isabelle, G., André, E.: An introduction to variable and feature selection. Journal of Machine Learning Research 3, 1157–1182 (2003)

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. CETpD. Technical Research Centre for Dependency Care and Autonomous Living, UPC. Technical University of Catalonia, Neàpolis. Rambla de l’Exposició, 59-69, 08800, Vilanova i la Geltrú, Spain

    Jordi Parera, Cecilio Angulo & Joan Cabestany

  2. CETpD. Mobility and Gait Lab, FHCSAA. Sant Antoni Abad Hospital, Sant Josep, 21-23, 08800, Vilanova i la Geltrú, Spain

    A. Rodríguez-Molinero

Authors
  1. Jordi Parera
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  2. Cecilio Angulo
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  3. A. Rodríguez-Molinero
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  4. Joan Cabestany
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Editor information

Editors and Affiliations

  1. Departamento de Ingeniería Electrónica, Universitat Politècnica de Catalunya (UPC). E.T.S.I. de Telecomunicación., , , ,, Campus Norte, Edificio C4, C/ Jordi Girona, 1-3, E08034, Barcelona, Spain

    Joan Cabestany

  2. Grupo ISIS, Dpto. Tecnología Electrónica ETSI Telecomunicación, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Francisco Sandoval

  3. Department of Computer Architecture and Computer Technology, University of Granada, Spain

    Alberto Prieto

  4. Department of Informatics, University of Salamanca, Salamanca, Spain

    Juan M. Corchado

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© 2009 Springer-Verlag Berlin Heidelberg

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Parera, J., Angulo, C., Rodríguez-Molinero, A., Cabestany, J. (2009). User Daily Activity Classification from Accelerometry Using Feature Selection and SVM. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds) Bio-Inspired Systems: Computational and Ambient Intelligence. IWANN 2009. Lecture Notes in Computer Science, vol 5517. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02478-8_142

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  • DOI: https://doi.org/10.1007/978-3-642-02478-8_142

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02477-1

  • Online ISBN: 978-3-642-02478-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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