Signal processing evaluation of myoelectric sensor placement in low-level gestures: Sensitivity analysis using independent component analysis

Ganesh R. Naik, Dinesh K. Kumar, Marimuthu Palaniswami

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)

Abstract


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.

Original languageEnglish
Pages (from-to)91-99
Number of pages9
JournalExpert Systems
Volume31
Issue number1
DOIs
Publication statusPublished - Feb 2014
Externally publishedYes

Keywords

  • bio-sensors
  • bio-signal analysis
  • blind source separation (BSS)
  • hand gesture sensing
  • independent component analysis (ICA)
  • surface electromyography (sEMG)

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