Rehabilitation of the Paralyzed Lower Limbs Using Functional Electrical Stimulation: Robust Closed Loop Control

International audienceFunctional electrical stimulation (FES) is used to excite paralyzed muscles that would otherwise be uncontrollable by paraplegic patients. Consequently, the patient could recover partially some of its lower limb functions improving thus the cardiovascular system, increasing oxygen uptake and bettering the whole quality of life. The main challenge that we face when applying FES to the paralyzed lower limbs is to avoid hyperstimulation and to defer the muscular fatigue as much as possible. One of our goals is to compute the needed patterns stimulation (current and/or pulse width) necessary to perform a desired given motion of the knee joint. This later is actuated by two groups of antagonist muscles: quadriceps and hamstrings causing respectively extension and flexion of the knee. The muscle model used in this study is based on a physio-mathematical formulation of the macroscopic Hill and microscopic Huxley concepts. Parameters of the biomechanical model (muscles-knee) were identified based on experimental measures. Afterward, we apply two robust nonlinear control strategies: the High Order Sliding Mode (HOSM) controller and the Model Predictive Controller (MPC) also known as receding horizon controller. These controllers have been evaluated in simulation to hightlight i) their performance in terms of capability of tracking a pre-defined reference trajectory and ii) the robustness against force perturbation and model mismatch. The performances of these controllers have also been compared with a classical pole placement controller

Theme F "medical robotics for training and guidance": Results and future work

International audienceThis paper presents the projects of the Theme F "medical robotics for training and guidance" inside the GdR STIC-Santé. Three scientific meeting days have been organized during the period 2011-2012. They were devoted to physical simulators of behavior for gesture learning, command of hand prostheses by myoelectric signals or brain activity and the manipulation of objects by the artificial hand, and the last to the use of robots for medical gestures. The next event, scheduled for early 2013, will focus on the evaluation of gesture and especially "evaluation of gesture - to do what?"

SICOMAT : a system for SImulation and COntrol analysis of MAchine Tools

International audienceThis paper presents a software package for the simulation and the control analysis of machine tool axes. This package which is called SICOMAT (SImulation and COntrol analysis of MAchine Tools), provides a large variety of toolboxes to analyze the behavior and the control of the machine. The software takes into account several elements such as the exibility of bodies, the interaction between several axes, the effect of numerical control and the availability to reduce models

SICOMAT : a system for SImulation and COntrol analysis of MAchine Tools

International audienceThis paper presents a software package for the simulation and the control analysis of machine tool axes. This package which is called SICOMAT (SImulation and COntrol analysis of MAchine Tools), provides a large variety of toolboxes to analyze the behavior and the control of the machine. The software takes into account several elements such as the flexibility of bodies, the interaction between several axes, the effect of numerical control and the availability to reduce models

Synthesis of optimal electrical stimulation patterns for functional motion restoration: applied to spinal cord-injured patients

We investigated the synthesis of electrical stimulation patterns for functional movement restoration in human paralyzed limbs. We considered the knee joint system, co-activated by the stimulated quadriceps and hamstring muscles. This synthesis is based on optimized functional electrical stimulation (FES) patterns to minimize muscular energy consumption and movement efficiency criteria. This two-part work includes a multi-scale physiological muscle model, based on Huxley’s formulation. In the simulation, three synthesis strategies were investigated and compared in terms of muscular energy consumption and co-contraction levels. In the experimental validation, the synthesized FES patterns were carried out on the quadriceps-knee joint system of four complete spinal cord injured subjects. Surface stimulation was applied to all subjects, except for one FES-implanted subject who received neural stimulation. In each experimental validation, the model was adapted to the subject through a parameter identification procedure. Simulation results were successful and showed high co-contraction levels when reference trajectories were tracked. Experimental validation results were encouraging, as the desired and measured trajectories showed good agreement, with an 8.4 % rms error in a subject without substantial time-varying behavior. We updated the maximal isometric force in the model to account for time-varying behavior, which improved the average rms errors from 31.4 to 13.9 % for all subjects

Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training

International audienceDexterity and procedural knowledge are two critical skills that surgeons need to master to perform accurate and safe surgical interventions. However, current training systems do not allow us to provide an in-depth analysis of surgical gestures to precisely assess these skills. Our objective is to develop a method for the automatic and quantitative assessment of surgical gestures. To reach this goal, we propose a new unsupervised algorithm that can automatically segment kinematic data from robotic training sessions. Without relying on any prior information or model, this algorithm detects critical points in the kinematic data that define relevant spatio-temporal segments. Based on the association of these segments, we obtain an accurate recognition of the gestures involved in the surgical training task. We, then, perform an advanced analysis and assess our algorithm using datasets recorded during real expert training sessions. After comparing our approach with the manual annotations of the surgical gestures, we observe 97.4% accuracy for the learning purpose and an average matching score of 81.9% for the fully automated gesture recognition process. Our results show that trainees workflow can be followed and surgical gestures may be automatically evaluated according to an expert database. This approach tends toward improving training efficiency by minimizing the learning curve

AIR2, un robot parallèle à actionnement pneumatique à deux degrés de liberté pour les applications de prise et dépose d'objets

Un prototype de robot parallèle actionné pneumatiquement à deux degrés de liberté a été développé afin de rendre accessible aux petites et moyennes entreprises la robotisation de tâches qui nécessiteraient le déplacement de charges de 5 à 25 kg en utilisant des vérins standards moins onéreux que les moteurs électriques. Après une présentation de la structure mécanique du robot ainsi que ses modèles, la stratégie de commande innovante (commande prédictive avec boucle interne de force H-infini) est brièvement introduite, puis les résultats obtenus expérimentalement sont présentés et analysés

Evaluation of contactless human–machine interface for robotic surgical training

Purpose Teleoperated robotic systems are nowadays routinely used for specific interventions. Benefits of robotic training courses have already been acknowledged by the community since manipulation of such systems requires dedicated training. However, robotic surgical simulators remain expensive and require a dedicated human–machine interface. Methods We present a low-cost contactless optical sensor, the Leap Motion, as a novel control device to manipulate the RAVEN-II robot. We compare peg manipulations during a training task with a contact-based device, the electro-mechanical Sigma.7. We perform two complementary analyses to quantitatively assess the performance of each control method: a metric-based comparison and a novel unsupervised spatiotemporal trajectory clustering. Results We show that contactless control does not offer as good manipulability as the contact-based. Where part of the metric-based evaluation presents the mechanical control better than the contactless one, the unsupervised spatiotemporal trajectory clustering from the surgical tool motions highlights specific signature inferred by the human–machine interfaces. Conclusions Even if the current implementation of contactless control does not overtake manipulation with high-standard mechanical interface, we demonstrate that using the optical sensor complete control of the surgical instruments is feasible. The proposed method allows fine tracking of the trainee’s hands in order to execute dexterous laparoscopic training gestures. This work is promising for development of future human–machine interfaces dedicated to robotic surgical training systems