Paper Info
Title
A Multivariate Statistical Analysis of Muscular Biopotential for Human Arm Movement Characterization
Abstract
Pattern recognition of electromyographic signals consists of a hard task due to the high dimensionality of the data and noise presence on the acquired signals. This work intends to study the data set as a multivariate pattern recognition problem by applying linear transformations to reduce the data dimensionality. Five volunteers contributed in a previous experiment that acquired the myoelectrical signals using surface electrodes. Attempts to analyse the groups of acquired data by means of descriptive statistics have shown to be inconclusive. This works shows that the use of multivariate statistical techniques such as Principal Components Analysis (PCA) and Maximum uncertainty Linear Discriminant Analysis (MLDA) to characterize the acquired set of signals through low dimensional scatter plots provides a new understanding of the data spread, making easier its analysis. Considering the arm horizontal movement and the acquired set of data used in this research, a multivariate linear separation between the patterns of interest quantified by the distance of Bhattacharyya suggests that it's possible not only to characterize the angular joint position, but also to confirm that different movements recruit similar amounts of energy to be executed.
Year
Venue
Keywords
2009
BIOSIGNALS 2009: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON BIO-INSPIRED SYSTEMS AND SIGNAL PROCESSING
Electromyography,Biceps,Triceps,Linear Transformation,PCA,MLDA,Bhattacharyya Distance
Field
DocType
Citations 
Descriptive statistics,Bhattacharyya distance,Pattern recognition,Computer science,Multivariate statistics,Curse of dimensionality,Artificial intelligence,Linear map,Linear discriminant analysis,Scatter plot,Principal component analysis
Conference
0
PageRank 
References 
Authors
0.34
4
3
Name
Order
Citations
PageRank
Giuliano Alves Da Silva100.34
Maria Cláudia Ferrari de Castro211.08
Carlos E. Thomaz336931.65