Classification of leg motions by processing gyroscope signals
2009 IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009
349 - 352
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Please cite this item using this persistent URLhttp://hdl.handle.net/11693/28726
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. ©2009 IEEE.