Proposal of a health care network based on big data analytics for PDs

Health care networks for Parkinson's disease (PD) already exist and have been already proposed in the literature, but most of them are not able to analyse the vast volume of data generated from medical examinations and collected and organised in a pre-defined manner. In this work, the authors propose a novel health care network based on big data analytics for PD. The main goal of the proposed architecture is to support clinicians in the objective assessment of the typical PD motor issues and alterations. The proposed health care network has the ability to retrieve a vast volume of acquired heterogeneous data from a Data warehouse and train an ensemble SVM to classify and rate the motor severity of a PD patient. Once the network is trained, it will be able to analyse the data collected during motor examinations of a PD patient and generate a diagnostic report on the basis of the previously acquired knowledge. Such a diagnostic report represents a tool both to monitor the follow up of the disease for each patient and give robust advice about the severity of the disease to clinicians

MR-conditional Robotic Actuation of Concentric Tendon-Driven Cardiac Catheters

Atrial fibrillation (AF) and ventricular tachycardia (VT) are two of the sustained arrhythmias that significantly affect the quality of life of patients. Treatment of AF and VT often requires radiofrequency ablation of heart tissues using an ablation catheter. Recent progress in ablation therapy leverages magnetic resonance imaging (MRI) for higher contrast visual feedback, and additionally utilizes a guiding sheath with an actively deflectable tip to improve the dexterity of the catheter inside the heart. This paper presents the design and validation of an MR-conditional robotic module for automated actuation of both the ablation catheter and the sheath. The robotic module features a compact design for improved accessibility inside the MR scanner bore and is driven by piezoelectric motors to ensure MR-conditionality. The combined catheter-sheath mechanism is essentially a concentric tendon-driven continuum robot and its kinematics is modeled by the constant curvature model for closed-loop position control. Path following experiments were conducted to validate the actuation module and control scheme, achieving < 2 mm average tip position error.Comment: 7 pages, 7 figures, submitted to IEEE ISMR 202

Design, Development and Force Control of a Tendon-driven Steerable Catheter with a Learning-based Approach

In this research, a learning-based force control schema for tendon-driven steerable catheters with the application in robot-assisted tissue ablation procedures was proposed and validated. To this end, initially a displacement-based model for estimating the contact force between the catheter and tissue was developed. Afterward, a tendon-driven catheter was designed and developed. Next, a software-hardware-integrated robotic system for controlling and monitoring the pose of the catheter was designed and developed. Also, a force control schema was developed based on the developed contact force model as a priori knowledge. Furthermore, the position control of the tip of the catheter was performed using a learning-based inverse kinematic approach. By combining the position control and the contact model, the force control schema was developed and validated. Validation studies were performed on phantom tissue as well as excised porcine tissue. Results of the validation studies showed that the proposed displacement-based model was 91.5% accurate in contact force prediction. Also, the system was capable of following a set of desired trajectories with an average root-mean-square error of less than 5%. Further validation studies revealed that the system could fairly generate desired static and dynamic force profiles on the phantom tissue. In summary, the proposed force control system did not necessitate the utilization of force sensors and could fairly contribute in automatizing the ablation task for robotic tissue ablation procedures

Data-Driven Methods Applied to Soft Robot Modeling and Control: A Review

Soft robots show compliance and have infinite degrees of freedom. Thanks to these properties, such robots can be leveraged for surgery, rehabilitation, biomimetics, unstructured environment exploring, and industrial grippers. In this case, they attract scholars from a variety of areas. However, nonlinearity and hysteresis effects also bring a burden to robot modeling. Moreover, following their flexibility and adaptation, soft robot control is more challenging than rigid robot control. In order to model and control soft robots, a large number of data-driven methods are utilized in pairs or separately. This review first briefly introduces two foundations for data-driven approaches, which are physical models and the Jacobian matrix, then summarizes three kinds of data-driven approaches, which are statistical method, neural network, and reinforcement learning. This review compares the modeling and controller features, e.g., model dynamics, data requirement, and target task, within and among these categories. Finally, we summarize the features of each method. A discussion about the advantages and limitations of the existing modeling and control approaches is presented, and we forecast the future of data-driven approaches in soft robots. A website (https://sites.google.com/view/23zcb) is built for this review and will be updated frequently. Note to Practitioners —This work is motivated by the need for a review introducing soft robot modeling and control methods in parallel. Modeling and control play significant roles in robot research, and they are challenging especially for soft robots. The nonlinear and complex deformation of such robots necessitates specific modeling and control approaches. We introduce the state-of-the-art data-driven methods and survey three approaches widely utilized. This review also compares the performance of these methods, considering some important features like data amount requirement, control frequency, and target task. The features of each approach are summarized, and we discuss the possible future of this area

A Platform for Robot-Assisted Intracardiac Catheter Navigation

Steerable catheters are routinely deployed in the treatment of cardiac arrhythmias. During invasive electrophysiology studies, the catheter handle is manipulated by an interventionalist to guide the catheter's distal section toward endocardium for pacing and ablation. Catheter manipulation requires dexterity and experience, and exposes the interventionalist to ionizing radiation. Through the course of this research, a platform was developed to assist and enhance the navigation of the catheter inside the cardiac chambers. This robotic platform replaces the interventionalist's hand in catheter manipulation and provides the option to force the catheter tip in arbitrary directions using a 3D input device or to automatically navigate the catheter to desired positions within a cardiac chamber by commanding the software to do so. To accomplish catheter navigation, the catheter was modeled as a continuum manipulator, and utilizing robot kinematics, catheter tip position control was designed and implemented. An electromagnetic tracking system was utilized to measure the position and orientation of two key points in catheter model, for position feedback to the control system. A software platform was developed to implement the navigation and control strategies and to interface with the robot, the 3D input device and the tracking system. The catheter modeling was validated through in-vitro experiments with a static phantom, and in-vivo experiments on three live swines. The feasibility of automatic navigation was also veri ed by navigating to three landmarks in the beating heart of swine subjects, and comparing their performance with that of an experienced interventionalist using quasi biplane fluoroscopy. The platform realizes automatic, assisted, and motorized navigation under the interventionalist's control, thus reducing the dependence of successful navigation on the dexterity and manipulation skills of the interventionalist, and providing a means to reduce the exposure to X-ray radiation. Upon further development, the platform could be adopted for human deployment

Macrobend optical sensing for pose measurement in soft robot arms

This paper introduces a pose-sensing system for soft robot arms integrating a set of macrobend stretch sensors. The macrobend sensory design in this study consists of optical fibres and is based on the notion that bending an optical fibre modulates the intensity of the light transmitted through the fibre. This sensing method is capable of measuring bending, elongation and compression in soft continuum robots and is also applicable to wearable sensing technologies, e.g. pose sensing in the wrist joint of a human hand. In our arrangement, applied to a cylindrical soft robot arm, the optical fibres for macrobend sensing originate from the base, extend to the tip of the arm, and then loop back to the base. The connectors that link the fibres to the necessary opto-electronics are all placed at the base of the arm, resulting in a simplified overall design. The ability of this custom macrobend stretch sensor to flexibly adapt its configuration allows preserving the inherent softness and compliance of the robot which it is installed on. The macrobend sensing system is immune to electrical noise and magnetic fields, is safe (because no electricity is needed at the sensing site), and is suitable for modular implementation in multi-link soft continuum robotic arms. The measurable light outputs of the proposed stretch sensor vary due to bend-induced light attenuation (macrobend loss), which is a function of the fibre bend radius as well as the number of repeated turns. The experimental study conducted as part of this research revealed that the chosen bend radius has a far greater impact on the measured light intensity values than the number of turns (if greater than five). Taking into account that the bend radius is the only significantly influencing design parameter, the macrobend stretch sensors were developed to create a practical solution to the pose sensing in soft continuum robot arms. Henceforward, the proposed sensing design was benchmarked against an electromagnetic tracking system (NDI Aurora) for validation

imaged-based tip force estimation on steerable intracardiac catheters using learning-based methods

Minimally invasive surgery has turned into the most commonly used approach to treat cardiovascular diseases during the surgical procedure; it is hypothesized that the absence of haptic (tactile) feedback and force presented to surgeons is a restricting factor. The use of ablation catheters with the integrated sensor at the tip results in high cost and noise complications. In this thesis, two sensor-less methods are proposed to estimate the force at the intracardiac catheter’s tip. Force estimation at the catheter tip is of great importance because insufficient force in ablation treatment may result in incomplete treatment and excessive force leads to damaging the heart chamber. Besides, adding the sensor to intracardiac catheters adds complexity to their structures. This thesis is categorized into two sensor-less approaches: 1- Learning-Based Force Estimation for Intracardiac Ablation Catheters, 2- A Deep-Learning Force Estimator System for Intracardiac Catheters. The first proposed method estimates catheter-tissue contact force by learning the deflected shape of the catheter tip section image. A regression model is developed based on predictor variables of tip curvature coefficients and knob actuation. The learning-based approach achieved force predictions in close agreement with experimental contact force measurements. The second approach proposes a deep learning method to estimate the contact forces directly from the catheter’s image tip. A convolutional neural network extracts the catheter’s deflection through input images and translates them into the corresponding forces. The ResNet graph was implemented as the architecture of the proposed model to perform a regression. The model can estimate catheter-tissue contact force based on the input images without utilizing any feature extraction or pre-processing. Thus, it can estimate the force value regardless of the tip displacement and deflection shape. The evaluation results show that the proposed method can elicit a robust model from the specified data set and approximate the force with appropriate accuracy

csi and csi7 current source inverters for modular transformerless pv inverters

n/