Classification of leg motions by processing gyroscope signals
Proceedings of the IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009
349 - 352
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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.
KeywordsArtificial neural network
Dynamic time warping