An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control

Adenike A. Adewuyi, Levi J. Hargrove, Todd A. Kuiken

Research output: Contribution to journalArticlepeer-review

104 Scopus citations


Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for control of multiple prosthetic functions for transradial amputees. There is, however, a need to adapt this control method when implemented for partial-hand amputees, who possess both a functional wrist and information-rich residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 classes of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and 85% for partial-hand amputees. We evaluated real-time pattern recognition control of three hand motions in seven different wrist positions. We found that a system trained with both intrinsic and extrinsic muscle EMG data, collected while statically and dynamically varying wrist position increased completion rates from 73% to 96% for partial-hand amputees and from 88% to 100% for non-amputees when compared to a system trained with only extrinsic muscle EMG data collected in a neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion can significantly improve the robustness of pattern recognition control for application to partial-hand prosthetic control.

Original languageEnglish (US)
Article number7102763
Pages (from-to)485-494
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number4
StatePublished - Apr 2016


  • Electromyography (EMG)
  • intrinsic hand muscle
  • myoelectric control
  • partial-hand amputee
  • pattern recognition

ASJC Scopus subject areas

  • Rehabilitation
  • General Neuroscience
  • Internal Medicine
  • Biomedical Engineering


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