Sequential estimation of intramuscular EMG model parameters for prosthesis control

Jonathan Monsifrot (1) , Eric Le Carpentier (1) , Dario Farina (2) , Yannick Aoustin (1)

1 IRCCyN - Institut de Recherche en Communications et en Cybernétique de Nantes
2 Department of Neurorehabilitation Engineering, Bernstein Center for Computational Neuroscience

EMG signals are an image of the control from the central nervous system transmitted to muscles. Intramuscular EMG signals are collected directly in muscles. The collected data contain information on the neural control of the muscle. This information can be used for controlling external devices (myo- electric control), however realtime processing of intramuscular EMG signals is complex. The aim of this paper is to present a sequential method to estimate parameters which can lead to an active drive of an upper limb prosthesis. A system model will be presented and then an algorithm detailed. Results of the proposed algorithm applied to simulated and experimental data will be discussed.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Jonathan Monsifrot, Eric Le Carpentier, Dario Farina, Yannick Aoustin. Sequential estimation of intramuscular EMG model parameters for prosthesis control. IEEE/RSJ International Conference on Intelligent Robots and Systems, Workshop on Robotics for Neurology and Rehabilitation, Sep 2011, San Francisco, Ca. ⟨hal-00670265⟩