Classifying human leg motions with uniaxial piezoelectric gyroscopes

Date
2009
Authors
Tunçel O.
Altun, K.
Barshan, B.
Advisor
Instructor
Source Title
Sensors
Print ISSN
14248220
Electronic ISSN
Publisher
Volume
9
Issue
11
Pages
8508 - 8546
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
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. © 2009 by the authors.

Course
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Book Title
Keywords
Artificial neural networks, Bayesian decision making, Dynamic time warping, Gyroscope, Inertial sensors, K-nearest neighbor, Least-squares method, Motion classification, Rule-based algorithm, Support vector machines, Bayesian decision makings, Dynamic time warping, Inertial sensor, K-nearest neighbors, Least squares methods, Motion classification, Rule based algorithms, Algorithms, Costs, Decision trees, Digital storage, Gyroscopes, Inertial navigation systems, Neural networks, Pattern recognition, Piezoelectricity, Support vector machines, Decision making
Citation
Published Version (Please cite this version)