Classifying Human Leg Motions with Uniaxial Piezoelectric GyroscopesReport as inadecuate




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Department of Electrical and Electronics Engineering, Bilkent University, Bilkent 06800 Ankara, Turkey





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Abstract This paper provides a comparative study on the different techniques of classifying human leg motions that are performed using two low-cost uniaxial piezoelectric gyroscopes worn on the leg. A number of feature sets, extracted from the raw inertial sensor data in different ways, are used in the classification process. The classification techniques implemented and compared in this study are: Bayesian decision making BDM, a rule-based algorithm RBA or decision tree, least-squares method LSM, k-nearest neighbor algorithm k-NN, dynamic time warping DTW, support vector machines SVM, and artificial neural networks ANN. A performance comparison of these classification techniques is provided in terms of their correct differentiation rates, confusion matrices, computational cost, and training and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that BDM, in general, results in the highest correct classification rate with relatively small computational cost. View Full-Text

Keywords: gyroscope; inertial sensors; motion classification; Bayesian decision making; rule-based algorithm; least-squares method; k-nearest neighbor; dynamic time warping; support vector machines; artificial neural networks gyroscope; inertial sensors; motion classification; Bayesian decision making; rule-based algorithm; least-squares method; k-nearest neighbor; dynamic time warping; support vector machines; artificial neural networks





Author: Orkun Tunçel, Kerem Altun and Billur Barshan *

Source: http://mdpi.com/



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