Name
Title | ||
|---|---|---|
Comparative study on classifying human activities with miniature inertial and magnetic sensors |
Abstract | ||
|---|---|---|
This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.patcog.2010.04.019 | Pattern Recognition |
Keywords | Field | DocType |
magnetic sensor,rule-based algorithm,highest correct classification rate,accelerometer,artificial neural networks,raw sensor data,support vector machines,miniature inertial,gyroscope,dynamic time warping,decision tree,k -nearest neighbor,inertial sensors,sensor unit,least-squares method,activity recognition and classification,human activity,classification process,tri-axial accelerometer,feature extraction,feature reduction,tri-axial gyroscope,magnetometer,comparative study,tri-axial magnetometer,bayesian decision making,classification technique,k nearest neighbor,cross validation,least squares method,artificial neural network,rule based,support vector machine,least square method,principal component analysis,activity recognition | k-nearest neighbors algorithm,Decision tree,Pattern recognition,Dynamic time warping,Support vector machine,Feature extraction,Inertial measurement unit,Artificial intelligence,Artificial neural network,Machine learning,Mathematics,Computational complexity theory | Journal |
Volume | Issue | ISSN |
43 | 10 | Pattern Recognition |
Citations | PageRank | References |
76 | 2.64 | 29 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kerem Altun | 1 | 194 | 10.30 |
| Billur Barshan | 2 | 313 | 27.83 |
| Orkun Tunçel | 3 | 98 | 4.69 |