The effects of electrode size and orientation on the sensitivity of myoelectric pattern recognition systems to electrode shift

IEEE Trans Biomed Eng. 2011 Sep;58(9):2537-44. doi: 10.1109/TBME.2011.2159216. Epub 2011 Jun 9.


Myoelectric pattern recognition systems for prosthesis control are often studied in controlled laboratory settings, but obstacles remain to be addressed before they are clinically viable. One important obstacle is the difficulty of maintaining system usability with socket misalignment. Misalignment inevitably occurs during prosthesis donning and doffing, producing a shift in electrode contact locations. We investigated how the size of the electrode detection surface and the placement of electrode poles (electrode orientation) affected system robustness with electrode shift. Electrodes oriented parallel to muscle fibers outperformed electrodes oriented perpendicular to muscle fibers in both shift and no-shift conditions (p < 0.01). Another finding was the significant difference (p < 0.01) in performance for the direction of electrode shift. Shifts perpendicular to the muscle fibers reduced classification accuracy and real-time controllability much more than shifts parallel to the muscle fibers. Increasing the size of the electrode detection surface was found to help reduce classification accuracy sensitivity to electrode shifts in a direction perpendicular to the muscle fibers but did not improve the real-time controllability of the pattern recognition system. One clinically important result was that a combination of longitudinal and transverse electrodes yielded high controllability with and without electrode shift using only four physical electrode pole locations.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adult
  • Artificial Limbs*
  • Electrodes
  • Electromyography / instrumentation*
  • Electromyography / methods
  • Female
  • Humans
  • Male
  • Models, Biological
  • Muscle Fibers, Skeletal / physiology
  • Pattern Recognition, Automated / methods*
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted*