By Topic

A motion sequence fusion technique based on PCA for activity analysis in body sensor networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Ghassemzadeh, H. ; Dept. of Electr. Eng., Univ. of Texas at Dallas, Richardson, TX, USA ; Guenterberg, E. ; Ostadabbas, S. ; Jafari, R.

Human movement analysis by means of mobile sensory platforms is an ever-growing area with promise to revolutionize delivery of healthcare services. An effective data fusion technique is essential for understanding the inertial information obtained from distributed sensor nodes. In this paper, we develop a data fusion model based on the concept of principal component analysis. Unlike traditional fusion techniques which deal with statistical feature space, our model operates on motion transcripts, where each movement is represented as a sequence of basic building blocks called primitives. We describe how our model transforms transcripts of different nodes into a unified transcript by integrating the most relevant primitives of movements. Finally, we demonstrate the performance of our transcript fusion model for action recognition using real data collected from three subjects.

Published in:

Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Date of Conference:

3-6 Sept. 2009