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Brazilian Symposium on Artificial Intelligence

SBIA 2012: Advances in Artificial Intelligence - SBIA 2012 pp 52–61Cite as

Wearable Computing: Accelerometers’ Data Classification of Body Postures and Movements

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7589)

Abstract

During the last 5 years, research on Human Activity Recognition (HAR) has reported on systems showing good overall recognition performance. As a consequence, HAR has been considered as a potential technology for e-health systems. Here, we propose a machine learning based HAR classifier. We also provide a full experimental description that contains the HAR wearable devices setup and a public domain dataset comprising 165,633 samples. We consider 5 activity classes, gathered from 4 subjects wearing accelerometers mounted on their waist, left thigh, right arm, and right ankle. As basic input features to our classifier we use 12 attributes derived from a time window of 150ms. Finally, the classifier uses a committee AdaBoost that combines ten Decision Trees. The observed classifier accuracy is 99.4%.

Keywords

  • Human Activity Recognition
  • Wearable Computing
  • Machine Learning
  • Accelerometer

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  • DOI: 10.1007/978-3-642-34459-6_6
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Author information

Authors and Affiliations

  1. Informatics Department, Pontifical Catholic University of Rio de Janeiro, Brazil

    Wallace Ugulino, Débora Cardador, Katia Vega, Ruy Milidiú & Hugo Fuks

  2. School of Computing and Communications, Lancaster University, UK

    Eduardo Velloso

Authors
  1. Wallace Ugulino
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  2. Débora Cardador
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  3. Katia Vega
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  4. Eduardo Velloso
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  5. Ruy Milidiú
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  6. Hugo Fuks
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Editor information

Editors and Affiliations

  1. IME, Department of Computer Science, University of São Paulo, Brazil

    Leliane N. Barros

  2. IME, Department of Computer Science, University of São Paulo, São Paulo, Brazil

    Marcelo Finger

  3. DInf - Federal University of Paraná, CEP 19031-970, Curitiba, Brazil

    Aurora T. Pozo

  4. Federal University of Technology, Paraná, Brazil

    Gustavo A. Gimenénez-Lugo

  5. Department of Inforumatics, Federal University of Paraná, Curitiba, Brazil

    Marcos Castilho

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© 2012 Springer-Verlag Berlin Heidelberg

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Ugulino, W., Cardador, D., Vega, K., Velloso, E., Milidiú, R., Fuks, H. (2012). Wearable Computing: Accelerometers’ Data Classification of Body Postures and Movements. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds) Advances in Artificial Intelligence - SBIA 2012. SBIA 2012. Lecture Notes in Computer Science(), vol 7589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34459-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-34459-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34458-9

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