Classifying human leg motions with uniaxial piezoelectric gyroscopes
Sensors, vol.9, no.11, pp.8508-8546, 2009 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 9 Issue: 11
- Publication Date: 2009
- Doi Number: 10.3390/s91108508
- Journal Name: Sensors
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.8508-8546
- 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
- Open Archive Collection: AVESIS Open Access Collection
- Yıldız Technical University Affiliated: No
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.