Classifying human leg motions with uniaxial piezoelectric gyroscopes
Author
Tunçel O.
Altun, K.
Barshan, B.
Date
2009Source Title
Sensors
Print ISSN
14248220
Volume
9
Issue
11
Pages
8508 - 8546
Language
English
Type
ArticleItem Usage Stats
121
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82
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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. © 2009 by the authors.
Keywords
Artificial neural networksBayesian 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