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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Classification of leg motions by processing gyroscope signals

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      Author(s)
      Tunçel, Orkun
      Altun, Kerem
      Barshan, Billur
      Date
      2009
      Source Title
      Proceedings of the IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009
      Publisher
      IEEE
      Pages
      349 - 352
      Language
      Turkish
      Type
      Conference Paper
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      Abstract
      In this study, eight different leg motions are classified using two single-axis gyroscopes mounted on the right leg of a subject with the help of several pattern recognition techniques. The methods of least squares, Bayesian decision, k-nearest neighbor, dynamic time warping, artificial neural networks and support vector machines are used for classification and their performances are compared. This study comprises the preliminary work for our future studies on motion recognition with a much wider scope.
      Keywords
      Artificial neural network
      Bayesian decision
      Dynamic time warping
      K-nearest neighbors
      Least square
      Motion recognition
      Single-axis
      Bayesian networks
      Gyroscopes
      Neural networks
      Pattern recognition
      Permalink
      http://hdl.handle.net/11693/28726
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2009.5136404
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      • Department of Electrical and Electronics Engineering 3702
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