Overall Objectives
Scientific Foundations
Application Domains
New Results

Section: New Results

Function control and synthesis

Correction of drop-foot

Participants : Christine Azevedo Coste, Roger Pissard-Gibollet (SED INRIA), David Andreu, Bernard Espiau (INRIA RA), Jérôme Froger (Rehab. Centre, Grau du Roi, CHU Nîmes).

Hemiplegia is a condition where one side of the body is paretic or paralyzed; it is usually the consequence of a cerebro-vascular accident. One of the main consequences of hemiplegia is the drop-foot syndrome. Due to lack of controllability of muscles involved in flexing the ankle and toes, the foot drops downward and impede the normal walking motion. Today, there are commercially available assistive systems that use surface electrodes to stimulate Tibialis Anterior (TA) muscle and prevent drop-foot. The efficiency of drop-foot stimulators depends on the timing of stimulation and functionality of dorsiflexion motion. Classically, available stimulators use footswitches to detect foot on/off events. These discrete events allow only for triggering the stimulation and/or playing with the duration of the stimulation pattern, but does not allow for precise online modification of the pattern itself. We have developed algorithms to monitor the ongoing walking cycle by observing the valid limb movements. In order to ensure legs coordination during walking, the CPG (Central Pattern Generator) concept was introduced, and we proposed a robust phase estimation method based on the observer of a nonlinear oscillator. Based on these preliminar results we have validated the phase estimation algorithm on hemiplegic subjects data. We have modified a commercial stimulator, ODSTOCK, in order to be able to trigger it using our own wireless sensors and algorithms (Fig. 11 ). The experiments on real-time triggering of drop-foot stimulator will started as soon as ethical comittee will give us the agreement to run tests on patients.

Figure 11. Principle of MASEA approach of drop foot correction using FES

This work is related to the technological developements presented in this report on external device development.

Planning and Fast Re-Planning of Safe Motions for biped systems

Participants : Philippe Fraisse, Sébastien Lengagne, Nacim Ramdani.

Optimal motions are usually used as joint reference trajectories for repetitive or complex motions. In the case of soccer robots, the kicking motion is usually a benchmark motion, computed off-line, without taking into account the current position of the robot or the direction of the goal. Moreover, robots must react quickly to any situation, even if not expected, and cannot spend time to generate a new optimal motion by the classical way. Therefore, we propose a new method for fast motion re-planning based on an off-line computation of a feasible sub-set of the motion parameters, using Interval Analysis (figure 12 ).

Figure 12. new method for fast motion re-planning based on an off-line computation of a feasible sub-set of the motion parameters, using Interval Analysis

Simulation of whole body motion under FES using HuMAnS Toolbox

Participants : Martine Eckert, David Guiraud, Mitsuhiro Hayashibe.

Mathematical models of the skeletal muscle can support the development of neuroprotheses to restore functional movements in individuals with motor deficiencies by the means of Functional Electrical Stimulation (FES). Since many years, numerous skeletal muscle models have been proposed to express the relationship between muscle activation and generated force. DEMAR model integrates the Hill model and the physiological one based on Huxley work allowing the muscle activation especially under FES. This musle model is implemented in a 3D biomechanical model of HuMAnS toolbox. Initially, only 4 muscles had been introduced in the human 36 model: the quadriceps and the hamstrings for knee joint actuation. Recently, we have introduced 10 other muscles: 6 muscles for the ankle and 4 muscles for the hip (right and left legs). For the ankle, we have modelled the tibialis anterior, the soleus and the two head of gastrocnemius and for the hip the iliopsoas and the gluteus maximus.

In future, the aim of this work is to simulate a patient standing up under FES and to compare the obtained results with experimental data in order to contribute for FES stimulation sequence generation by solving inverse dynamics problem. Simulation of standing up motion in HuMAnS toolbox is shown as in Figure 13 . The required torques can be computed for certain task and it would be helpful to estimate the required combination of FES activations in muscles which were recently introduced for hip, knee and ankle joint actuations.

Figure 13. Simulation of standing up motion. The required torques can be computed for certain task and it would be helpful to estimate the required combination of muscle activations in FES.
(a) (b) (c)

Online tremor characterization and FES-based stiffness control

Participants : Antonio P. L. Bo, Philippe Poignet.

The main goal of the TREMOR Project is to evaluate the use of surface FES in the active compensation of pathological tremor. It is the most common movement disorder found in human pathology and its incidence is higher on the upper limbs.

In this scenario, algorithms previously proposed to perform online tremor characterization from inertial measurements were expanded to concurrently filter voluntary motion components also measured by these sensors. Tests were conducted in patients with different pathologies and the results have been compared with other solutions proposed in the literature [15] . The EKF-based algorithm was implemented in real-time and it was used to characterize tremor measured by different sensors, such as inertial sensors and a digitizing tablet.

Another research effort was directed to the development of a musculoskeletal model of the wrist joint actuated by flexor and extensor muscles. So far within the project, this Hill-based model, that describes joint motion dynamics under natural and artificial stimulation, has served different purposes.

In order to study the different effects the neural system may cause in tremor dynamics, a simulation study was conducted [8] . Effects of different levels were considered within the study, from changes on the dynamics of the reflex loops to interaction with higher levels of the neural system, represented by a central oscillator.

The model was also used in the development of new FES-based tremor compensation strategies. In [16] , a strategy based on FES-controlled co-contraction of the antagonist muscles that act on the trembling joint was presented. In particular, the model was used to predict the stimulation pairs that do not interfere with the wrist joint, but allow modulation of the active stiffness that is provided to the wrist.

Identification protocol and validation of quadriceps-shank system: experimental results

Participants : Mourad Benoussaad, Philippe Poignet, David Guiraud, Mitsuhiro Hayashibe, Charles Fattal.

The knowledge and prediction of the behavior of electrically activated musculoskeletal muscles are important prerequisites for the synthesis and control of function by FES. The parameter identification of a physiological musculoskeletal model under FES is investigated in these works [14] [12] . The model represents the knee and its associated quadriceps muscle. The identification protocol is noninvasive and based on the in-vivo experiments on 10 spinal cord injured subjects. The measurements were obtained by stimulating the quadriceps muscles through surface electrodes. The torques were measured in isometric conditions through the Biodex system (Fig. 14 -(a)- part (a)) and the joint angles were recorded in dynamical condition through an electrogoniometer (Fig. 14 -(a)- part (b)).

The identification procedure consists of several steps, in order to identify: the geometrical parameters, the joint mechanical parameters, the force-length relationship, the recruitment function and the mechanical parameters of quadriceps. To perform this experiment, an authorization from the ethical review board and an agreement from each subject were obtained.

Figure 14. Identification protocol and experimental validation. (a) The experimental setup in isometric condition (part a) and dynamic condition (part b). (b) The experimental cross validation.
(a) (b)

A cross validation has been carried out using dynamical data set that has not been used for the identification. The results obtained for one subject is shown on figure 14 -(b) and highlights a good prediction of the leg motion such as Normalized RMS Errors is about 3.5% .


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