title: A state-based, proportional myoelectric control method: online validation and comparison with the clinical state-of-the-art.
abstract: BACKGROUND
  Current clinical myoelectric systems provide unnatural prosthesis control, with limited functionality. In this study, we propose a proportional state-based control method, which allows switching between functions in a more natural and intuitive way than the traditional co-contraction switch method.
  
  
  METHODS
  We validated the ability of the proposed system to provide precise control in both position and velocity modes. Two tests were performed with online visual feedback, involving target reaching and direct force control in grasping. The performance of the system was evaluated both on a subject with limb deficiency and in 9 intact-limbed subjects, controlling two degrees of freedom (DoF) of the hand and wrist.
  
  
  RESULTS
  The system allowed completion of the tasks involving 1-DoF with task completion rate >96% and of those involving 2-DoF with completion rate >91%. When compared with the clinical/industrial state-of-the-art approach and with a classic pattern recognition approach, the proposed method significantly improved the performance in the 2-DoF tasks. The completion rate in grasping force control was >97% on average.
  
  
  CONCLUSIONS
  These results indicate that, using the proposed system, subjects were successfully able to operate two DoFs, and to achieve precise force control in grasping. Thus, the proposed state-based method could be a suitable alternative for commercial myoelectric devices, providing reliable and intuitive control of two DoFs.
authors.forename: Ning
authors.initials: N
authors.lastname: Jiang
authors.forename: Thomas
authors.initials: T
authors.lastname: Lorrain
authors.affiliation: Department of Neurorehabilitation Engineering, Berstein Focus Neurotechnology Göttingen, Bernstein Center for Computational Neuroscience, University Medical Center Göttingen, Georg-August University, Von-Siebold-Str, 6, Göttingen 37075, Germany. dario.farina@bccn.uni-goettingen.de.
authors.forename: Dario
authors.initials: D
authors.lastname: Farina
mesh_terms: D000328:Adult; D000465:Algorithms; D001186:Artificial Limbs; D004576:Electromyography; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D011474:Prosthesis Design; D034941:Upper Extremity