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Human Activity Recognition Using Inertial/Magnetic Sensor Units

  • Kerem Altun
  • Billur Barshan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6219)

Abstract

This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost.

Keywords

inertial sensors magnetometers human activity recognition and classification feature selection and reduction 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kerem Altun
    • 1
  • Billur Barshan
    • 1
  1. 1.Department of Electrical and Electronics EngineeringBilkent UniversityAnkaraTurkey

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