Decoding of individuated finger movements using surface electromyography

Francesco V.G. Tenore, Ander Ramos, Amir Fahmy, Soumyadipta Acharya, Ralph Etienne-Cummings, Nitish V. Thakor

Research output: Contribution to journalArticlepeer-review

246 Scopus citations


Upper limb prostheses are increasingly resembling the limbs they seek to replace in both form and functionality, including the design and development of multifingered hands and wrists. Hence, it becomes necessary to control large numbers of degrees of freedom (DOFs), required for individuated finger movements, preferably using noninvasive signals.While existing control paradigms are typically used to drive a single-DOF hook-based configurations, dexterous tasks such as individual finger movementswould require more elaborate control schemes.We showthat it is possible to decode individual flexion and extension movements of each finger (tenmovements) with greater than 90% accuracy in a transradial amputee using only noninvasive surface myoelectric signals. Further, comparison of decoding accuracy from a transradial amputee and able-bodied subjects shows no statistically significant difference (p<0.05) between these subjects. These results are encouraging for the development of real-time control strategies based on the surface myoelectric signal to control dexterous prosthetic hands.

Original languageEnglish (US)
Article number4648401
Pages (from-to)1427-1434
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Issue number5
StatePublished - May 2009


  • Electromyography (EMG)
  • Myoelectric signals
  • Neural networks
  • Transradial amputee

ASJC Scopus subject areas

  • Biomedical Engineering


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