Email updates

Keep up to date with the latest news and content from JNER and BioMed Central.

Open Access Research

A state-based, proportional myoelectric control method: online validation and comparison with the clinical state-of-the-art

Ning Jiang1, Thomas Lorrain2 and Dario Farina1*

Author Affiliations

1 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

2 RMS Signal & Innovation, BP 40054, Aix en Provence, Cedex 3 13792, France

For all author emails, please log on.

Journal of NeuroEngineering and Rehabilitation 2014, 11:110  doi:10.1186/1743-0003-11-110

Published: 10 July 2014

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.

Keywords:
Prosthetic control; Electromyography; Signal processing; Proportional control; Pattern recognition