Nabil Zemiti

Viscoelastic model based force control for soft tissue interaction and its application in physiological motion compensation

Controlling the interaction between robots and living soft tissues has become an important issue as the number of robotic systems inside the operating room increases. Many researches have been done on force control to help surgeons during medical procedures, such as physiological motion compensation and tele-operation systems with haptic feedback. In order to increase the performance of such controllers, this work presents a novel force control scheme using Active Observer (AOB) based on a viscoelastic interaction model. The control scheme has shown to be stable through theoretical analysis and its performance was evaluated by in vitro experiments. In order to evaluate how the force control scheme behaves under the presence of physiological motion, experiments considering breathing and beating heart disturbances are presented. The proposed control scheme presented a stable behavior in both static and moving environment. The viscoelastic AOB presented a compensation ratio of 87% for the breathing motion and 79% for the beating heart motion.

Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training

Dexterity 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.

Soft tissue force control using active observers and viscoelastic interaction model

Controlling the interaction between the robot and living soft tissues has became an important issue as the number of robots inside the operating room increases. Many research works have been done in order to control this interaction. Nowadays, researches are running in force control for helping surgeons in medical procedures such as motion compensation in beating heart surgeries and tele-operation systems with haptic feedback. The viscoelasticity property of the interaction between organ tissue and robotic instrument further complicates the force control design which is much easier in other applications by assuming the interaction model to be elastic (industry, stiff object manipulation, etc.). In order to increase the performance of a model based force control, this work presents a force control scheme using Active Observer (AOB) based on a viscoelastic interaction model. The control scheme has shown to be stable through theoretical analysis and its performance was evaluated and compared with a control scheme based on a classical elastic model through experiments, showing that a more realistic model can increases the performance of the force control.

Comparative Assessment of a Novel Optical Human-Machine Interface for Laparoscopic Telesurgery

This paper introduces a novel type of human-machine interface for laparoscopic telesurgery that employs an optical sensor. A Raven-II laparascopic robot (Applied Dexterity Inc) was teleoperated using two different human-machine interfaces, namely the Sigma 7 electro-mechanical device (Force Dimension Sarl) and the Leap Motion (Leap Motion Inc) infrared stereoscopic camera. Based on this hardware platform, a comparative study of both systems was performed through objective and subjective metrics, which were obtained from a population of 10 subjects. The participants were asked to perform a peg transferring task and to answer a questionnaire. Obtained results allow to confirm that fine tracking of the hand could be performed with the Leap Motion sensor. Such tracking comprises accurate finger motion acquisition to control the robot’s laparoscopic instrument jaws. Furthermore, the observed performance of the optical interface proved to be comparable to that of traditional electro-mechanical devices, such as the Sigma 7, during adequate execution of highly-dexterous laparascopic gestures.

Adaptive path planning for steerable needles using duty-cycling

This paper presents an adaptive approach for 2D motion planning of steerable needles. It combines duty-cycled rotation of the needle with the classic Rapidly-Exploring Random Tree (RRT) algorithm to obtain fast calculation of feasible trajectories. The motion planning is used intraoperatively at each cycle to compensate for system uncertainties and perturbations. Simulation results demonstrate the performance of the proposed motion planner on a workspace based on ultrasound images.

Needle deflection prediction using adaptive slope model

Thin and long (semi-rigid) needles are well known to bend during percutaneous insertions because of needle-tissue interactions. Robotized needle insertions have been proposed to improve the efficacy of Interventional Radiology (IR) procedures such as radiofrequency ablation (RFA) of kidney tumors. However, the success of treatments and diagnosis depends on accurate prediction of needle deflection. This work aims to demonstrate the feasibility of merging needle-tissue properties, tip asymmetry and needle tip position updates to assist needle placement. In this paper we proposed a needle-tissue interaction model that matches the observations of transversal and axial resultant forces acting in the system. Analysis of a slope parameter between needle and tissue provides online and offline needle deflections predictions. Online updates of the needle tip position allow adaptive corrections of the slope parameter. Moreover, promising results were observed while evaluating the models performance under uncertainties conditions such as tissue deformation, tissue inhomogeneity, needle-tissue friction, topological changes of the tissue and other modeling approximations. The system is evaluated by experiments in soft (homogeneous) PVC and multilayer tissue phantoms. Experiment results of needle placement into soft tissues presented average error of 1.04 mm. Meanwhile, online corrections decreased the error of offline predictions of 25%. The system shows an encouraging ability to predict semi-rigid needle deflection during interactions with elastic medium.

Geometric and mechanical evaluation of 3D-printing materials for skull base anatomical education and endoscopic surgery simulation – A first step to create reliable customized simulators

Introduction Endoscopic skull base surgery allows minimal invasive therapy through the nostrils to treat infectious or tumorous diseases. Surgical and anatomical education in this field is limited by the lack of validated training models in terms of geometric and mechanical accuracy. We choose to evaluate several consumer-grade materials to create a patient-specific 3D-printed skull base model for anatomical learning and surgical training. Methods Four 3D-printed consumer-grade materials were compared to human cadaver bone: calcium sulfate hemihydrate (named Multicolor), polyamide, resin and polycarbonate. We compared the geometric accuracy, forces required to break thin walls of materials and forces required during drilling. Results All materials had an acceptable global geometric accuracy (from 0.083mm to 0.203mm of global error). Local accuracy was better in polycarbonate (0.09mm) and polyamide (0.15mm) than in Multicolor (0.90mm) and resin (0.86mm). Resin and polyamide thin walls were not broken at 200N. Forces needed to break Multicolor thin walls were 1.6–3.5 times higher than in bone. For polycarbonate, forces applied were 1.6–2.5 times higher. Polycarbonate had a mode of fracture similar to the cadaver bone. Forces applied on materials during drilling followed a normal distribution except for the polyamide which was melted. Energy spent during drilling was respectively 1.6 and 2.6 times higher on bone than on PC and Multicolor. Conclusion Polycarbonate is a good substitute of human cadaver bone for skull base surgery simulation. Thanks to short lead times and reasonable production costs, patient-specific 3D printed models can be used in clinical practice for pre-operative training, improving patient safety.

Evaluation of Contactless Human-Machine Interface for Robotic Surgical Training

PurposeTeleoperated 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.MethodsWe 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.ResultsWe 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.ConclusionsEven 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.