Classification of leg motions by processing gyroscope signals

Author

Tunçel, Orkun ; Altun, Kerem ; Barshan, Billur

Author_Institution

Elektrik ve Elektron. Muhendisligi Bolumu, Bilkent Univ., Ankara

fYear

2009

fDate

9-11 April 2009

Firstpage

349

Lastpage

352

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

Bayes methods; artificial intelligence; gyroscopes; least squares approximations; neural nets; pattern classification; pattern clustering; pattern recognition; support vector machines; Bayesian decision; artificial neural networks; dynamic time warping; gyroscope signal processing; k-nearest neighbor; least square methods; leg motion classification; motion recognition; pattern recognition techniques; support vector machines; Artificial neural networks; Bayesian methods; Gyroscopes; Least squares methods; Leg; Micromechanical devices; Pattern recognition; Signal processing; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Communications Applications Conference, 2009. SIU 2009. IEEE 17th

Conference_Location

Antalya

Print_ISBN

978-1-4244-4435-9

Electronic_ISBN

978-1-4244-4436-6

Type

conf

DOI

10.1109/SIU.2009.5136404

Filename

5136404