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Automated classification of human daily activities in ambulatory environment

conference contribution
posted on 2011-01-01, 00:00 authored by Y Wu, Ronghua Chen, Fenghua She
This paper presents a human daily activity classification approach based on the sensory data collected from a single tri-axial accelerometer worn on waist belt. The classification algorithm was realized to distinguish 6 different activities including standing, jumping, sitting-down, walking, running and falling through three major steps: wavelet transformation, Principle Component Analysis (PCA)-based dimensionality reduction and followed by implementing a radial basis function (RBF) kernel Support Vector Machine (SVM) classifier. Two trials were conducted to evaluate different aspects of the classification scheme. In the first trial, the classifier was trained and evaluated by using a dataset of 420 samples collected from seven subjects by using a k-fold cross-validation method. The parameters σ and c of the RBF kernel were optimized through automatic searching in terms of yielding the highest recognition accuracy and robustness. In the second trial, the generation capability of the classifier was also validated by using the dataset collected from six new subjects. The average classification rates of 95% and 93% are obtained in trials 1 and 2, respectively. The results in trial 2 show the system is also good at classifying activity signals of new subjects. It can be concluded that the collective effects of the usage of single accelerometer sensing, the setting of the accelerometer placement and efficient classifier would make this wearable sensing system more realistic and more comfortable to be implemented for long-term human activity monitoring and classification in ambulatory environment, therefore, more acceptable by users.

History

Event

Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Conference (2011 : 12th : Sydney, Australia)

Series

Studies in computational intelligence ; v. 368

Pagination

157 - 168

Publisher

Springer

Location

Sydney, New South Wales

Place of publication

Berlin, Germany

Start date

2011-07-06

End date

2011-07-08

ISSN

1860-949X

ISBN-13

9783642222887

ISBN-10

3642222889

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2011, Springer-Verlag Berlin Heidelberg

Editor/Contributor(s)

R Lee

Title of proceedings

SNPD 2011 : Proceedings of the 12th Conference Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing Conference