A Feature Set Evaluation for Activity Recognition with Body-Worn Inertial Sensors

  • Syed Agha Muhammad
  • Bernd Niklas Klein
  • Kristof Van Laerhoven
  • Klaus David
Part of the Communications in Computer and Information Science book series (CCIS, volume 277)

Abstract

The automatic and unobtrusive identification of user activities is a challenging goal in human behavior analysis. The physical activity that a user exhibits can be used as contextual data, which can inform applications that reside in public spaces. In this paper, we focus on wearable inertial sensors to recognize physical activities. Feature set evaluation for 5 typical activities is performed by measuring accuracy for combinations of 6 often-used features on a set of 11 well-known classifiers. To verify significance of this analysis, a t-test evaluation was performed for every combination of these feature subsets. We identify an easy-to-compute feature set, which has given us significant results and at the same time utilizes a minimum of resources.

Keywords

Support Vector Machine Fast Fourier Transform Activity Recognition Feature Subset Human Activity Recognition 
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 2012

Authors and Affiliations

  • Syed Agha Muhammad
    • 1
  • Bernd Niklas Klein
    • 2
  • Kristof Van Laerhoven
    • 1
  • Klaus David
    • 2
  1. 1.TU DarmstadtDarmstadtGermany
  2. 2.University of KasselKasselGermany

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