Classification of leg motions by processing gyroscope signals
Date
2009Source Title
Proceedings of the IEEE 17th Signal Processing and Communications Applications Conference, SIU 2009
Publisher
IEEE
Pages
349 - 352
Language
Turkish
Type
Conference PaperItem Usage Stats
172
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129
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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 networkBayesian decision
Dynamic time warping
K-nearest neighbors
Least square
Motion recognition
Single-axis
Bayesian networks
Gyroscopes
Neural networks
Pattern recognition