Tunçel O.Altun, K.Barshan, B.2016-02-082016-02-08200914248220http://hdl.handle.net/11693/22581This 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. © 2009 by the authors.EnglishArtificial neural networksBayesian decision makingDynamic time warpingGyroscopeInertial sensorsK-nearest neighborLeast-squares methodMotion classificationRule-based algorithmSupport vector machinesBayesian decision makingsDynamic time warpingInertial sensorK-nearest neighborsLeast squares methodsMotion classificationRule based algorithmsAlgorithmsCostsDecision treesDigital storageGyroscopesInertial navigation systemsNeural networksPattern recognitionPiezoelectricitySupport vector machinesDecision makingArticle10.3390/s91108508