Towards a model-based estimator of muscle length and force using muscle afferent signals for real time FES control

The present work is given in the context of motor function rehabilitation via implanted functional electro-stimulation (FES). Our ultimate objective is to use natural muscle sensitive fiber information as feedback for the FES artificial controller. This implies online extraction of information from neural activity in a form usable by a closed-loop controller. In this study, the in-vivo viscoelastic responses of the rabbit skeletal muscle Medial Gastrocnemius (MG) were investigated. Two anesthetized New Zealand white rabbits were surgically exposed and tested under in-vivo conditions. A longitudinal intrafascicular electrode (LIFE) was implanted in the tibial branch innervating MG muscle. Muscle state (length variation and force) as well as electroneurogram (ENG) from LIFEs were recorded while applying external mechanical stretches to the muscle. A series of sinusoidal stretch profiles ranging in rate from 0.01 up to 1Hz were imposed on the muscle. Mechanical properties were expressed through a viscoelastic model and ENG signals were analyzed in terms of the single unit responses using simple threshold detection. This approach has the advantage of being a fast and easy method for unit separation with the big advantage that it could be implemented for online application