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Discriminative Temporal Smoothing for Activity Recognition from Wearable Sensors

  • Jaakko Suutala
  • Susanna Pirttikangas
  • Juha Röning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4836)

Abstract

This paper describes daily life activity recognition using wearable acceleration sensors attached to four different parts of the human body. The experimental data set consisted of signals recorded from 13 different subjects performing 17 daily activities. Furthermore, to attain more general activities, some of the most specific classes were combined for a total of 9 different activities. Simple time domain features were calculated from each sensor device. For the recognition task, we propose a novel sequential learning method that combines discriminative learning of individual input-output mappings using support vector machines (SVM) with generative learning to smooth temporal time-dependent activity sequences with a trained hidden Markov model (HMM) type transition probability matrix. The experiments show that the accuracy of the proposed method is superior to various conventional discriminative and generative methods alone, and it achieved a total recognition rate of 94% and 96% studying 17 and 9 different daily activities, respectively.

Keywords

Support Vector Machine Hide Markov Model Activity Recognition Generative Learning Wearable Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Jaakko Suutala
    • 1
  • Susanna Pirttikangas
    • 1
  • Juha Röning
    • 1
  1. 1.Intelligent Systems Group, Infotech Oulu, Computer Engineering Laboratory, 90014 University of OuluFinland

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