Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.3390/s91108508 | SENSORS |
Keywords | Field | DocType |
gyroscope,inertial sensors,motion classification,Bayesian decision making,rule-based algorithm,least-squares method,k-nearest neighbor,dynamic time warping,support vector machines,artificial neural networks | Data mining,Decision tree,Gyroscope,Dynamic time warping,Computer science,Electronic engineering,Inertial measurement unit,Artificial intelligence,Artificial neural network,k-nearest neighbors algorithm,Pattern recognition,Support vector machine,Bayesian probability | Journal |
Volume | Issue | ISSN |
9 | 11 | 1424-8220 |
Citations | PageRank | References |
22 | 2.05 | 35 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Orkun Tunçel | 1 | 98 | 4.69 |
| Kerem Altun | 2 | 194 | 10.30 |
| Billur Barshan | 3 | 313 | 27.83 |