Automated classification of human daily activities in ambulatory environment

Wu, Yuchuan, Chen, Ronghua and She, Mary F.H. 2011, Automated classification of human daily activities in ambulatory environment, in Software engineering, artificial intelligence, networking and parallel/distributed computing 2011, Springer, Berlin, Germany, pp.157-168.

Attached Files
Name Description MIMEType Size Downloads

Title Automated classification of human daily activities in ambulatory environment
Author(s) Wu, Yuchuan
Chen, Ronghua
She, Mary F.H.
Title of book Software engineering, artificial intelligence, networking and parallel/distributed computing 2011
Editor(s) Lee, Roger
Publication date 2011
Series Studies in computational intelligence ; v. 368
Chapter number 13
Total chapters 14
Start page 157
End page 168
Total pages 12
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) artificial intelligence
computer networks
electronic data processing
sensory data
classification
accelerometer
Summary 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.
ISBN 3642222889
9783642222887
ISSN 1860-949X
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 861604 Integrated Systems
HERDC Research category B1 Book chapter
Copyright notice ©2011, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30043174

Document type: Book Chapter
Collection: Centre for Material and Fibre Innovation
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 72 Abstract Views, 3 File Downloads  -  Detailed Statistics
Created: Tue, 13 Mar 2012, 09:51:10 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.