A model-based FES would be very helpful for the adaptive movement synthesis of spinal-cord-injured patients. For the fulfillment, we need a precise skeletal muscle model to predict the force of each muscle. Thus, we have to estimate many unknown parameters in the nonlinear muscle system. The identification process is essential for the realistic force prediction. We previously proposed a mathematical muscle model of skeletal muscle which describes the complex physiological system of skeletal muscle based on the macroscopic Hill-Maxwell and microscopic Huxley concepts. It has an original skeletal muscle model to enable consideration for the muscular masses and the viscous frictions caused by the muscle-tendon complex. In this paper, we present an experimental identification method of biomechanical parameters using Sigma-Point Kalman Filter applied to the nonlinear skeletal muscle model. Result of the identification shows its effective performance. The evaluation is provided by comparing the estimated isometric force with experimental data with the stimulation of the rabbit medial gastrocnemius muscle. This approach has the advantage of fast and robust computation, that can be implemented for online application of FES control.

Functional electrical stimulation (FES) is used to excite paralysed muscles that would otherwise be uncontrollable by paraplegic patients. Consequently, the patient could recover partially some of lower limb functions improving the cardiovascular system, increasing oxygen uptake and bettering the whole quality of life. In this paper, we apply a control design based on a higher order sliding mode to a complex physio-mathematical muscle model. This model is based on macroscopic Hill and microscopic Huxley concepts. The main goal concerns the prediction of the needed pattern stimulation (current and pulse width), which will extend the overall performances and defer the muscle fatigue as much as possible. The controller is mathematically computed and shown to provide satisfactory stability and tracking errors. Its efficiency is illustrated with the control of the knee joint angle under a co-contraction approach.

In paraplegic patients with upper motor neuron lesions the signal path from the central nervous system to muscles is interrupted. Functional Electrical Stimulation (FES) applied to the lower motor neurons can replace the lacking signals. A neuroprosthesis may be used to restore motor function in paraplegic patients on the basis of FES. The neuroprosthesic implant allows muscles to be controlled with high accuracy, high selectivity and the repeatability of the muscle's response can be achieved. The SUAW project succeeded in the implantation of an advanced neuroprosthetic device on two patients, but the movement generation remains open loop and is tuned empirically. The system is thus insufficient to enhance significantly the daily-life of the patient, nevertheless, the good results obtained give us the opportunity to envisage the system evolves towards the automatic synthesis of the stimulation patterns generating the desired movement and closed loop control. To achieve this goal, some preliminary researches have to be carried out; starting with a specific modeling that can be used in the contest of FES. The main issues concern muscle modeling including FES parameters as inputs, fatigue, the interaction with the skeleton, and the identification of parameters. This paper describes the mathematical modeling of the skeletal muscle.

The new generation of implanted neuroprostheses allows muscles to be controlled with fine accuracy, high selectivity and the repeatability of the muscle's response can be achieved. Thus, the closed loop control of such systems becomes possible. The SUAW project succeeded in the implantation of an advanced neuroprosthetic device on two patients, but the movement generation remains open loop and is tuned empirically. Nevertheless, the good results obtained give us the opportunity to envisage the system evolves towards closed loop control and automatic synthesis of the stimulation patterns generating the desired movement. To achieve this goal, some preliminary researches have to be carried out, beginning with a specific modelling that can be used in the context of functional electrical stimulation (FES). The main issues concern muscle modelling including the interaction with the skeleton, fatigue, FES parameters as inputs, and the identification of dynamic parameters, and afterwards, the motion synthesis and the closed loop control based on this model. Besides, the scientific approach is the same as in robotics so that the theoretical tools used in the control theory are the same and directly applicable. This paper describes the results obtained in the previous project SUAW and how we attempt, through the new project DEMAR, to enhance the global performances of the system.