title: EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis.
abstract: BACKGROUND
  Active hand prostheses controlled using electromyography (EMG) signals have been used for decades to restore the grasping function, lost after an amputation. Although myocontrol is a simple and intuitive interface, it is also imprecise due to the stochastic nature of the EMG recorded using surface electrodes. Furthermore, the sensory feedback from the prosthesis to the user is still missing. In this study, we present a novel concept to close the loop in myoelectric prostheses. In addition to conveying the grasping force (system output), we provided to the user the online information about the system input (EMG biofeedback).
  
  
  METHODS
  As a proof-of-concept, the EMG biofeedback was transmitted in the current study using a visual interface (ideal condition). Ten able-bodied subjects and two amputees controlled a state-of-the-art myoelectric prosthesis in routine grasping and force steering tasks using EMG and force feedback (novel approach) and force feedback only (classic approach). The outcome measures were the variability of the generated forces and absolute deviation from the target levels in the routine grasping task, and the root mean square tracking error and the number of sudden drops in the force steering task.
  
  
  RESULTS
  During the routine grasping, the novel method when used by able-bodied subjects decreased twofold the force dispersion as well as absolute deviations from the target force levels, and also resulted in a more accurate and stable tracking of the reference force profiles during the force steering. Furthermore, the force variability during routine grasping did not increase for the higher target forces with EMG biofeedback. The trend was similar in the two amputees.
  
  
  CONCLUSIONS
  The study demonstrated that the subjects, including the two experienced users of a myoelectric prosthesis, were able to exploit the online EMG biofeedback to observe and modulate the myoelectric signals, generating thereby more consistent commands. This allowed them to control the force predictively (routine grasping) and with a finer resolution (force steering). The future step will be to implement this promising and simple approach using an electrotactile interface. A prosthesis with a reliable response, following faithfully user intentions, would improve the utility during daily-life use and also facilitate the embodiment of the assistive system.
authors.affiliation: Department of Neurorehabilitation Engineering, University Medical Center Göttingen (UMG), Georg-August University, 37075, Göttingen, Germany. strahinja.dosen@bccn.uni-goettingen.de.
authors.forename: Strahinja
authors.initials: S
authors.lastname: Dosen
authors.affiliation: Otto Bock HealthCare GmbH, 37115, Duderstadt, Germany. marko.markovic@ottobock.de.
authors.forename: Marko
authors.initials: M
authors.lastname: Markovic
authors.affiliation: Department of Neurorehabilitation Engineering, University Medical Center Göttingen (UMG), Georg-August University, 37075, Göttingen, Germany. somer.kelef@stud.uni-goettingen.de.
authors.forename: Kelef
authors.initials: K
authors.lastname: Somer
authors.affiliation: Otto Bock HealthCare GmbH, 37115, Duderstadt, Germany. bernhard.graimann@ottobock.de.
authors.forename: Bernhard
authors.initials: B
authors.lastname: Graimann
authors.affiliation: Department of Neurorehabilitation Engineering, University Medical Center Göttingen (UMG), Georg-August University, 37075, Göttingen, Germany. dario.farina@bccn.uni-goettingen.de.
authors.forename: Dario
authors.initials: D
authors.lastname: Farina
mesh_terms: D000328:Adult; D000674:Amputees; D001186:Artificial Limbs; D004576:Electromyography; D056228:Feedback, Sensory; D005260:Female; D006225:Hand; D018737:Hand Strength; D006801:Humans; D008297:Male; D008875:Middle Aged; D011597:Psychomotor Performance / Q000502:physiology; D014110:Touch / Q000502:physiology; D014584:User-Computer Interface; D055815:Young Adult