Human activity recognition using inertial/magnetic sensor units

Date
2010
Advisor
Instructor
Source Title
Human Behavior Understanding
Print ISSN
0302-9743
Electronic ISSN
Publisher
Springer, Berlin, Heidelberg
Volume
6219
Issue
Pages
38 - 51
Language
English
Type
Conference Paper
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), 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). Daily and sports activities are classified using five sensor units worn by eight subjects on the chest, the arms, and the legs. Each sensor unit comprises a triaxial gyroscope, a triaxial accelerometer, and a triaxial magnetometer. Principal component analysis (PCA) and sequential forward feature selection (SFFS) methods are employed for feature reduction. For a small number of features, SFFS demonstrates better performance and should be preferable especially in real-time applications. The classifiers are validated using different cross-validation techniques. Among the different classifiers we have considered, BDM results in the highest correct classification rate with relatively small computational cost. © 2010 Springer-Verlag Berlin Heidelberg.

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Book Title
Keywords
Classification rates, Classification technique, Comparative studies, Computational costs, Cross-validation technique, Dynamic time warping, Feature reduction, Feature selection, feature selection and reduction, Human activities, Human activity recognition, Inertial sensor, K-nearest neighbor algorithm, Least squares methods, Real-time application, Sensor units, Sports activity, Tri-axial magnetometer, Triaxial accelerometer, Bayesian networks, Behavioral research, Classifiers, Decision making, Inertial navigation systems, Intelligent agents, Magnetometers, Neural networks, Principal component analysis, Sensors, Support vector machines, Feature extraction
Citation