Authors:
Giuliano Alves da Silva
1
;
Maria Cláudia Ferrari de Castro
2
and
Carlos Eduardo Thomaz
1
Affiliations:
1
Centro Universitário da FEI, Brazil
;
2
Centro Universitario da FEI, Brazil
Keyword(s):
Electromyography, Biceps, Triceps, Linear Transformation, PCA, MLDA, Bhattacharyya Distance.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
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 sep
aration 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.
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