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This paper presents a contribution for restoring standing in paraplegia while using functional electrical stimulation (FES). Movement generation induced by FES remains mostly open looped and stimulus intensities are tuned empirically. To design an efficient closed-loop control, a preliminary study has been carried out to investigate the relationship between body posture and voluntary upper body movements. A methodology is proposed to estimate body posture in the sagittal plane using force measurements exerted on supporting handles during standing. This is done by setting up constraints related to the geometric equations of a two-dimensional closed chain model and the hand-handle interactions. All measured quantities are subject to an uncertainty assumed unknown but bounded. The set membership estimation problem is solved via interval analysis. Guaranteed uncertainty bounds are computed for the estimated postures. In order to test the feasibility of our methodology, experiments were carried out with complete spinal cord injured patients.

We present a new system for functional electrical stimulation (FES) applications based on networked stimulation units. They embed an advanced analog circuit, which provides multipolar and multiphasic stimulation profiles, and digital circuits, which ensure safety, locally executed programmed profiles, and communication with the master controller. This architecture is thus based on distributed stimulation units (DSU) that need only a two-wire bus to communicate, regardless of the number of poles of each DSU-driven electrode. This structure minimizes the required bandwidth between master and distributed units, increases the safety and stimulation features and decreases the complexity of the surgical approach. We have successfully tested this network-based stimulation architecture on benchtop stimulators. This original approach allows broad exploration of all possible methods to stimulate peripheral nerves, particularly in the goal of restoring the motor function. It provides a powerful research device to determine the optimal, least aggressive and the most efficient way to activate the peripheral nervous system using an implanted FES system that is less invasive than other existing devices.

The knowledge and prediction of the behavior of electrically activated muscles are important requisites for the movement restoration by FES in spinal cord injured subjects. The whole parameter's identification of a physiological musculoskeletal model for FES is investigated in this work. The model represents the knee and its associated quadriceps muscle. The identification protocol is noninvasive and based on the in-vivo experiments on paraplegic subjects. The isometric and nonisometric data was obtained by stimulating the quadriceps muscles of 3 paraplegic subjects through surface electrodes. A cross validation has been carried out using nonisometric data set. The normalized RMS errors between the identified model and the measured knee response are presented for each subject.

EMG-based muscle model has many applications in human-machine interface and rehabilitation robotics. For the muscular force estimation, so-called Hill-type model has been used in most of the cases. It has already shown its promising performance, however it is known as a phenomenological model considering only macroscopic physiology. In this paper, we discuss EMG-force estimation with the full physiology based muscle model in voluntary contraction. In addition to Hill macroscopic representation, a microscopic physiology description as stated by Huxley and Zahalak is integrated. It has significant meaning to realize the same kind of EMG-force estimation with multiscale physiology based model not with a phenomenological Hill model, because it brings the understanding of the internal biophysical dynamics and new insights about neuromuscular activations.

When contracting a muscle using NFES (Neural Functional Electrical Stimulation), the stimulus always activates the axons of greater diameter first. Also selective activation of given fascicle inside a nerve is not possible with classical cuff electrode as the recruitment is performed uniformly around the nerve. These limits lead to poorly selective muscle recruitment, inducing fatigue and possible pain. To overcome this, selective stimulation strategies can be used. We propose a toolchain to investigate, simulate and tune selective stimulation strategies. It consists of a conduction volume model to compute the electric field generated in the nerve by a cuff electrode surrounding it; an axon model to predict the effect of the field on the nerve fibre - the generation, propagation and possible block of action potentials; and an interface script that links the two models and generates the code of the input function for the nerve fibre model. We present some simulation results to illustrate the possibilities of the toolchain to simulate such strategies. Ongoing experimental validations are also discussed. They will enable us to tune the model and may lead to further improvements.

In this paper, we present a smooth muscle model which enables simulation of functional electrical stimulation (FES) induced contraction. The major novelty of this new version of our model is that it includes muscle cell calcium dynamics. We establish the relationship between calcium concentration and electrical potential of the membrane cell. Calcium concentration drives the microscopic mechanics of the muscle inducing contraction. We also refined the mechanical model: smooth muscles have slower dynamics than striated ones, it is therefore possible to neglect the second order terms in the differential equations. Validation of the model is achieved through the simulation of a well known example: the bladder electrical stimulation with a Finetech / Brindley implant. The obtained results are compared qualitatively with the experimental data available in the literature and show good consistency both in shape and time course. In order to obtain quantitative simulations, we need to perform the identification of the model through in-vivo testing on animals. Once this validation step is done, it will be possible to apply our methodology to human bladder neural stimulation.

We present a new model of smooth muscle that can be used to simulate the effect of Functional Electrical Stimulation (FES). It is composed of a set of differential equations based on the physiological reality so that parameter's values are meaningful and can be used for quantitative and objective evaluation of the muscle state. Moreover, the model has an input controlled by an FES signal so that it can simulate the behavior of the muscle under artificial stimulation. We apply this model to the simulation of the bladder contraction through the detrusor stimulation. It shows that the model is able to predict the time response, the intravesical pressure and the necessary time to empty the bladder. Simulations show consistent data with the literature. These preliminary results, after in vivo validations, will be used to caracterise bladders that need to be stimulated, for instance for paraplegics, and then to optimize the needed stimulation when neuroprosthesis are used to restore the emptying function.

The aim is to investigate the activation conditions of the different nerves which control the bladder. The selective stimulation of the nerve fibers depends on electrode configuration and intensity of applied current. The goal of this study is to compute the electrical potential inside the nerve due to an applied boundary currents. A symmetrically cylindrical model, representing the geometry and electrical conductivity of a nerve surrounded by a connective tissue and a cuff is used. In the quasistatic approximation, the problem can be modeled by a Poisson equation with Neumann boundary conditions. A symmetric boundary integral formulation is discretized using mixed finite elements. We can thus compute an electrical potential distribution depending on the electrode configuration and the applied current inside a nerve. Our results show that the distribution of the electrical potential inside a nerve or a fascicle depends on the geometry of the electrode and the shape of the applied current.

This paper presents an original electronic architecture to manage epimysial and neural stimulation using the same implantable device. All the muscles needed to achieve lower limb movements such as standing and walking can thus be activated. Mainly for surgical reasons, some muscles need to be stimulated through different inputs: epimysium or motor nerve. We developed an electronic solution, including the design of an application-specific integrated circuit, to meet the requirements of both types of stimulation. Five years after the successful implantation of the system, we were able to evaluate the system's performance. The patient is still using the system at home and no failure occurred during this 5-year period. We conclude that the electronic design not only provides a unique investigative tool for research, but that it can also be used to restore the motor function of the lower limb. This technology has an advantage over external stimulation because the patient can safely use the system at home. However, improvements such as lower power consumption, and thus greater autonomy, are needed. We further conclude that the modelling of the electrical behaviour of the electrodes is reliable and the estimated parameter values are homogeneous and consistent for the same type of electrode. Thus, the three parameters of the first-order model can be identified from an acute animal experiment and provide a means to optimize the design of the output stage of implanted stimulators.

We present the results of a 5-year patient follow-up after implantation of an original neuroprosthesis. The system is able to stimulate both epimysial and neural electrodes in such a way that the complete flexor-extensor chain of the lower limb can be activated without using the withdrawal reflex. We demonstrate that standing and assisted walking are possible, and the results have remained stable for 5 years. Nevertheless, some problems were noted, particularly regarding the muscle response on the epimysial channels. Analysis of the electrical behaviour and thresholds indicated that the surgical phase is crucial because of the sensitivity of the functional responses to electrode placement. Neural stimulation proved to be more efficient and more stable over time. This mode requires less energy and provides more selective stimulation. This FES system can be improved to enable balanced standing and less fatiguing gait, but this will require feedback on event detection to trigger transitions between stimulation sequences, as well as feedback to the patient about the state of his lower limbs.

The object of this paper is to present a real-time application of an artificial neural network (ANN). The application for which this network is demonstrated is a motorised orthosis with six degrees-of-freedom for use by a paraplegic; a 'walking machine'. Theoretical networks and training methods need modification to function correctly with a real application. Several complex phenomena that are very difficult to model have to be accommodated; the starting threshold of the activators, non-linearity, noise, and the non-biunivocity between successive system states (position, velocity, actuator controls). The modifications made to the network and the associated training method partially alleviate these difficulties.

The creation of muscle fatigue using surface electrical stimulation represents a highly reproducible phenomenon in spinal cord injured patients. The torque output was recorded as a function of time. The fatigue curves recorded over 110s exhibited three main parts: first, a plateau of short duration, followed by a more or less steep slope and then a second plateau which was maintained for a long time. This phenomenon was fitted using an exponential equation which had been developed and four parameters introduced that outlined to the muscle behaviour. A set of fatigue indices was defined to characterize the asymptotic value, the slope, the coordinates of the inflexion point, the time constant and the vertical amplitude of the curve recorded. Two populations were studied; a group of 11 thoracic level of injury paraplegic patients and a group of 10 able-bodied control subjects. The computed coefficients of determination, r2, were of very high values (0.99). Therefore, fatigue indices gave reliable information. Torque output did not differ between the two populations until 25 s had elapsed, but from 30 s onwards it was markedly lower in paraplegics. The residual torque output was 21.1 +/- 10.6% in the paraplegic group while it was 58.5 +/- 8.9% in the control group. The effect of blood supply in the production of fatigue was also studied by repeating the same tests using a tourniquet at the groin level. The residual torque became 14.7 +/- 2.3% in the paraplegic group and 42.9 +/- 6.3% in the control group.(ABSTRACT TRUNCATED AT 250 WORDS)

The authors determined the in vivo displacement of the instantaneous center of rotation (ICR) of the shoulder from data supplied by an optoelectronic system (ELITE System) which uses a specifically designed software program to achieve 3-dimensional analysis of abduction. The study involved 10 control subjects and 20 patients suffering from a periarticular disorder of the shoulder capsule retraction in 10 cases and rotator cuff tears before and after rehabilitation in 10 cases. This method provides an objective assessment of humeral head misalignment in relation to the glenoid cavity induced by the periarticular shoulder disorder and of the realignment achieved through rehabilitation. The proposed ICR calculation technique is entirely harmless for the patient in (particular because it requires no radiation analysis).