Comparative study on classifying human activities with miniature inertial and magnetic sensors

Date
2010
Authors
Altun, K.
Barshan, B.
Tunçel, O.
Advisor
Instructor
Source Title
Pattern Recognition
Print ISSN
0031-3203
Electronic ISSN
Publisher
Elsevier
Volume
43
Issue
10
Pages
3605 - 3620
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
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.

Course
Other identifiers
Book Title
Keywords
Accelerometer, Activity recognition and classification, Artificial neural networks, Bayesian decision making, Decision tree, Dynamic time warping, Feature extraction, Feature reduction, Gyroscope, Inertial sensors, k-Nearest neighbor, Least-squares method, Magnetometer, Rule-based algorithm, Support vector machines
Citation
Published Version (Please cite this version)