Paper Info
Title
Signal processing evaluation of myoelectric sensor placement in low-level gestures: sensitivity analysis using independent component analysis
Abstract
AbstractSurface electromyogram sEMG is a technique in which electrodes are placed on the skin overlying a muscle to detect the electrical activity. Multiple electrical sensors are essential for extracting intrinsic physiological and contextual information from the corresponding sEMG signals. The reason, why more than just one sEMG signal capture has to be used, is as follows: Due to signal propagation inside the human body in terms of an electrical conductor, there cannot be a one-to-one mapping of activities between muscle fibre groups and corresponding sEMG sensing electrodes. Each of such electrodes rather records a composition of many, and widely activity-independent signals, and such kind of raw signal capture cannot be efficiently used for pattern matching due to its linear dependency. On the other hand, Independent component analysis ICA provides the perfect answer of separating skin surface recordings into a set of independent muscle actions. Hence, there is a need for a method that indicates the quality of the sensor placements in sEMG. The purpose of this paper is to describe the use of source separation for sEMG using ICA. The actual use in practical sEMG experiments is demonstrated, when the number of recording channels for electrical muscle activities is varied.
Year
DOI
Venue
2014
10.1111/exsy.12008
Periodicals
Keywords
Field
DocType
bio sensors
Data mining,Signal processing,Gesture,Computer science,Artificial intelligence,Source separation,Contextual information,Pattern recognition,Electrical conductor,Communication channel,Speech recognition,Independent component analysis,Pattern matching
Journal
Volume
Issue
ISSN
31
1
0266-4720
Citations 
PageRank 
References 
4
0.45
2
Authors
3
Name
Order
Citations
PageRank
Ganesh R. Naik129825.37
Dinesh Kant Kumar216828.34
M. Palaniswami34107290.84