Decoding individuated finger flexions with Implantable MyoElectric Sensors.

Justin J. Baker, Dimitri Yatsenko, Jack F. Schorsch, Glenn A. DeMichele, Phil R. Troyk, Douglas T. Hutchinson, Richard F ff Weir, Gregory Clark, Bradley Greger

Research output: Contribution to journalArticlepeer-review

Abstract

We trained a rhesus monkey to perform randomly cued, individuated finger flexions of the thumb, index, and middle finger. Nine Implantable MyoElectric Sensors (IMES) were then surgically implanted into the finger muscles of the monkey's forearm, without any observable adverse chronic effects. Using an inductive link, we wirelessly recorded EMG from the IMES as the monkey performed a finger flexion task. A principal components analysis (PCA) based algorithm was used to decode which finger switch was pressed based on the recorded EMG. This algorithm correctly decoded which finger was moved 89% of the time. These results demonstrate that IMES offer a safe and highly promising approach for providing intuitive, dexterous control of artificial limbs and hands after amputation.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

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