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Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes
AbstractThis 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.
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Tunçel, O.; Altun, K.; Barshan, B. Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes. Sensors 2009, 9, 8508-8546.View more citation formats
Tunçel O, Altun K, Barshan B. Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes. Sensors. 2009; 9(11):8508-8546.Chicago/Turabian Style
Tunçel, Orkun; Altun, Kerem; Barshan, Billur. 2009. "Classifying Human Leg Motions with Uniaxial Piezoelectric Gyroscopes." Sensors 9, no. 11: 8508-8546.
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