Towards the control of individual fingers of a prosthetic hand using surface EMG signals.

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

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


The fast pace of development of upper-limb prostheses requires a paradigm shift in EMG-based controls. Traditional control schemes are only capable of providing 2 degrees of freedom, which is insufficient for dexterous control of individual fingers. We present a framework where myoelectric signals from natural hand and finger movements can be decoded with a high accuracy. 32 surface-EMG electrodes were placed on the forearm of an able-bodied subject while performing individual finger movements. Using time-domain feature extraction methods as inputs to a neural network classifier, we show that 12 individuated flexion and extension movements of the fingers can be decoded with an accuracy higher than 98%. To our knowledge, this is the first instance in which such movements have been successfully decoded using surface-EMG. These preliminary findings provide a framework that will allow the results to be extended to non-invasive control of the next generation of upper-limb prostheses for amputees.

ASJC Scopus subject areas

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


Dive into the research topics of 'Towards the control of individual fingers of a prosthetic hand using surface EMG signals.'. Together they form a unique fingerprint.

Cite this