Fast Optimization Scheme for the Muscular Response to FES Stimulation to Design a Smart Electrostimulator

In this article, we present the design of a smart electrostimulator for muscle rehabilitation or reinforcement, using fast computations, in order to control the muscular force. The Ding and al. model allows to predict and to optimize the muscular force response to functional electrical stimulation. [1]. We analyze the estimation of the Ding and al. parameters using an approximation of the force response [5] which depends upon the 6 parameters of the Ding’s model and we derive optimization scheme which bypass the time computational expensive integration of the dynamics of the Ding and al. equations.

I. Introduction

The aim of the study project is the design of a smart electrostimulator, in the isometric case (without displacement of the muscle), for muscle rehabilitation or reinforcement based, in particular, on modern sensors and fast computations using embedded electronics. The idea is to design training programs to track a force of reference [4] or to maximize the muscular force. Each training program can be converted into an optimization problem. The mathematical equations presented below are the tools to the design of this smart electrostimulator.