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Signal processing evaluation of myoelectric sensor placement in low‐level gestures: sensitivity analysis using independent component analysis
Author(s) -
Naik Ganesh R.,
Kumar Dinesh K.,
Palaniswami Marimuthu
Publication year - 2014
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/exsy.12008
Subject(s) - independent component analysis , computer science , signal (programming language) , sensitivity (control systems) , pattern recognition (psychology) , artificial intelligence , signal processing , blind signal separation , gesture , component (thermodynamics) , set (abstract data type) , speech recognition , computer vision , channel (broadcasting) , electronic engineering , digital signal processing , computer hardware , physics , engineering , thermodynamics , programming language , computer network
Surface 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.