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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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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

  • Online ISBN: 978-3-642-34459-6

  • eBook Packages: Computer ScienceComputer Science (R0)