BIO Web of Conferences
Volume 1, 2011The International Conference SKILLS 2011
|Number of page(s)||4|
|Published online||15 December 2011|
Can Principal Component Analysis be Applied in Real Time to Reduce the Dimension of Human Motion Signals?
Principal Component Analysis (PCA) is a usual method in multivariate analysis to reduce data dimensionality. PCA relies on the deﬁnition of a linear transformation of the data through an orthonormal matrix that is computed on the basis of the dataset itself. In this work we discuss the application of PCA on a set of human motion data and the cross validation of the result. The cross validation procedure simulates the application of the transformation on real time data. The PCA proved to be suitable to analyze data in real time and showed some interesting behavior on the data used as cross validation.
© Owned by the authors, published by EDP Sciences, 2011
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.