title: Classifying human leg motions with uniaxial piezoelectric gyroscopes.
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.
authors.affiliation: Department of Electrical and Electronics Engineering, Bilkent University, Bilkent 06800 Ankara, Turkey; E-Mails:  orkun@ee.bilkent.edu.tr  (O.T.);  kaltun@ee.bilkent.edu.tr  (K.A.).
authors.forename: Orkun
authors.initials: O
authors.lastname: Tunçel
authors.forename: Kerem
authors.initials: K
authors.lastname: Altun
authors.forename: Billur
authors.initials: B
authors.lastname: Barshan