Robustness and reliability of synergy-based myocontrol of a multiple degree of freedom robotic arm

Francesca Lunardini, Claudia Casellato, Andrea D'Avella, Terence D. Sanger, Alessandra Pedrocchi

Research output: Contribution to journalArticlepeer-review


In this study, we test the feasibility of the synergy- based approach for application in the realistic and clinically oriented framework of multi-degree of freedom (DOF) robotic control. We developed and tested online ten able-bodied subjects in a semi-supervised method to achieve simultaneous, continuous control of two DOFs of a robotic arm, using muscle synergies extracted from upper limb muscles while performing flexion-extension movements of the elbow and shoulder joints in the horizontal plane. To validate the efficacy of the synergy-based approach in extracting reliable control signals, compared to the simple muscle-pair method typically used in commercial applications, we evaluated the repeatability of the algorithm over days, the effect of the arm dynamics on the control performance, and the robustness of the control scheme to the presence of co-contraction between pairs of antagonist muscles. Results showed that, without the need for a daily calibration, all subjects were able to intuitively and easily control the synergy-based myoelectric interface in different scenarios, using both dynamic and isometric muscle contractions. The proposed control scheme was shown to be robust to co-contraction between antagonist muscles, providing better performance compared to the traditional muscle-pair approach. The current study is a first step toward user-friendly application of synergy-based myocontrol of assistive robotic devices.

Original languageEnglish
Article number7284702
Pages (from-to)940-950
Number of pages11
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number9
Publication statusPublished - Sep 1 2016


  • Assistive robotics
  • muscle synergies
  • myoelectric control
  • myoelectric signal processing

ASJC Scopus subject areas

  • Neuroscience(all)
  • Biomedical Engineering
  • Computer Science Applications


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