Authors:
Jun Akazawa
1
;
Takaharu Ikeuchi
1
;
Takemasa Okamoto
2
;
Ryuhei Okuno
3
;
Masaki Yoshida
4
;
Tetsuo Sato
5
and
Kotaro Minato
5
Affiliations:
1
Meiji University of Integrative Medicine, Japan
;
2
Meiji University of Integrative Medicin, Japan
;
3
Setsunan University, Japan
;
4
Osaka Electro-Communication University, Japan
;
5
Nara Institute of Science and Technology, Japan
Keyword(s):
Electromyogram, Motor Unit, Model, Independent Component Analysis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Education and Training
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Simulation and Modeling
;
Simulation Tools and Platforms
;
Soft Computing
Abstract:
Recently, application of Independent Component Analysis (ICA) has been reported for effective decomposi-tion of surface electromyogram (SEMG) signals into a train of surface motor unit action potentials (SMUAPs) of a single motor unit (MU). Results of ICA were not always sufficient as the feature extraction of SMUAP at first dorsal interosseous muscle (FDI). The purpose of this study is to propose an effective method for feature extraction of SMUAP by simulation study of focusing on the effects of electrode orientation. SEMG signals were created with the model and application of ICA was applied to the signals. The present study showed that the useful and actual method of ICA application was to repeat measurement of SEMG signals with varying the electrode orientation, and then to select the better signals for the feature extraction by executing ICA algorithm.