Jian-Long Hao

Design and evaluation of a bio-inspired robotic hand for percutaneous coronary intervention

The percutaneous coronary interventions (PCI) require complex operating skills of the interventional devices and make the surgeons being exposed to heavy X-ray radiation. Accurate delivery of the interventional devices and avoiding the radiation are especially important for the surgeons. This paper presents a novel dedicated dual-finger robotic hand (DRH) and a console to assist the surgeons to deliver the interventional devices in PCIs. The system is designed in the master-slave way which helps the surgeons to reduce the exposure to radiation. The mechanism of the DRH is bio-inspired and motions are decoupled in kinematics. In PCI procedures, the accuracy of the guidewire delivery and the catheter tip placement have significant effects on the surgical results. The performances of the DRH in delivering the guidewire and the balloon/stent catheter were evaluated by three surgical manipulations. The results show that the DRH has the ability to deliver the guidewire and the balloon/stent catheter precisely.

Fast and stable guidewire simulator for minimally invasive vascular surgery.

In recent years, minimally invasive vascular surgery is widely applied in treatment of cardiovascular diseases, and the manipulation of the guidewire is the essential skill for this surgery. Lots of time and money have to be taken to achieve the skill. In this paper, we present a multithreading guidewire simulator which can help the apprentice to gain the skill and modeling the guidewire is the core technique of the simulator. The guidewire is modeled by a fast and stable method based on the Cosserat theory of elastic rods. The method describes the behavior of the guidewire with the Lagrange equations of motion and it uses the penalty method to maintain constraints. We further propose a simplified solving procedure for the guidewire model. Finally, some experiments are conducted to evaluate the effectiveness of this model.In recent years, minimally invasive vascular surgery is widely applied in treatment of cardiovascular diseases, and the manipulation of the guidewire is the essential skill for this surgery. Lots of time and money have to be taken to achieve the skill. In this paper, we present a multithreading guidewire simulator which can help the apprentice to gain the skill and modeling the guidewire is the core technique of the simulator. The guidewire is modeled by a fast and stable method based on the Cosserat theory of elastic rods. The method describes the behavior of the guidewire with the Lagrange equations of motion and it uses the penalty method to maintain constraints. We further propose a simplified solving procedure for the guidewire model. Finally, some experiments are conducted to evaluate the effectiveness of this model.

A 3-DOF compact haptic interface for endoscopic endonasal approach surgery simulation

Endoscopic endonasal approach surgery is now the preferred treatment for most pituitary and related skull base tumors. However, this procedure requires a high level of hands-on skills and rich clinical experience. During the operation, haptic feedback, as the only one sense of bidirectional information interaction, plays an important role in surgical decision-making especially for bone-drilling. Existing surgical simulators provide either no haptic device or multipurpose haptic devices, which is difficult to reproduce the characteristics of surgical tool handling. In this paper, a custom-designed 3-DOF (pitch, yaw, radial) compact haptic interface for this surgery simulation is presented. It is dedicated to mimicking the touch sense of the surgical tools inserted through the nostril. Its main innovation is the mechanism design to maintain as much fidelity of the tool handling in the surgical training as in a real operation. The mechanism design is presented in detail as well as the kinematics and the force transmission. The mechanical characteristics of this haptic interface are also analyzed and presented.

Preliminary study on Wilcoxon-norm-based robust extreme learning machine

The fact that the linear estimators using the rank-based Wilcoxon approach in linear regression problems are usually insensitive to outliers is known in statistics. Outliers are the data points that differ greatly from the pattern set by the bulk of the data. Inspired by this fact, Hsieh et al. introduced the Wilcoxon approach into the area of machine learning. They investigated four new learning machines, such as Wilcoxon neural network (WNN), and developed four gradient descent based backpropagation algorithms to train these learning machines. The performances of these machines are better than ordinary nonrobust neural networks in outliers exist tasks. However, it is hard to balance the learning speed and the stability of these algorithms which is inherently the drawback of gradient descent based algorithms. In this paper, a new algorithm is used to train the output weights of single-layer feedforward neural networks (SLFN) with input weights and biases being randomly chosen. This algorithm is called Wilcoxon-norm based robust extreme learning machine or WRELM for short.

Tracking natural guidewire manipulations with an improved data glove

Natural guidewire manipulations of surgeons in percutaneous coronary intervention (PCI), rotating and translating a guidewire with finger and hand motion, are important and useful references to build a knowledge base (KB) for robotic autonomous intervention. In this paper, guidewire manipulation models are proposed using an improved data glove, in which a 6-DOF position/orientation sensor is installed. The tracking functions between the surgeons manipulations and guidewire motions are established according to the models. The performance of proposed method is analyzed with tracking errors and the statistical significance of tracking trajectories is evaluated with the Kruskal-Wallis test. The results show that radial rotation tracking error is 20.24 ± 8.38 (°) and axial advancement tracking error is 0.99 ± 0.81 (mm) at a medium delivery speed.

Development and evaluation of a 7-DOF haptic interface

With the development of human robot interaction technologies, haptic interfaces are widely used for 3D applications to provide the sense of touch. These interfaces have been utilized in medical simulation, virtual assembly and remote manipulation tasks. However, haptic interface design and control are still critical problems to reproduce the highly sensitive touch sense of humans. This paper presents the development and evaluation of a 7-DOF U+0028 degree of freedom U+0029 haptic interface based on the modified delta mechanism. Firstly, both kinematics and dynamics of the modified mechanism are analyzed and presented. A novel gravity compensation algorithm based on the physical model is proposed and validated in simulation. A haptic controller is proposed based on the forward kinematics and the gravity compensation algorithm. To evaluate the control performance of the haptic interface, a prototype has been implemented. Three kinds of experiments: gravity compensation, static response and force tracking are performed respectively. The experimental results show that the mean error of the gravity compensation is less than 0.7N and the maximum continuous force along the axis can be up to 6N. This demonstrates the good performance of the proposed haptic interface.

Prediction of natural guidewire rotation using an sEMG-based NARX neural network

For the treatment of cardiovascular diseases, clinical success of percutaneous coronary intervention is highly dependent on natural technical skills and dexterous manipulation strategies of surgeons. However, the increasing used robotic surgical systems have been designed without considering manipulation techniques, especially surgical behaviors and motion patterns. This has driven research towards exploitation of natural manipulation skills in recent years. In this paper, natural guidewire manipulations are analyzed and predicted using an sEMG-based nonlinear autoregressive neural network with exogenous inputs. The relationship between natural endovascular manipulation and guidewire rotation is built through the network. Two experiments at different rotational speed were performed to verify the effectiveness and robustness of the applied model. The experimental results show that the average predictive root mean error of five subjects is 15.61° at the low speed and 21.85° at the high speed. These favorable results could be of interest to improve existing robotic surgical systems.

Development of a multi-modal interactive system for Endoscopic Endonasal Approach surgery simulation

Endoscopic Endonasal Approach (EEA) surgery, as a minimally invasive technique, is now the preferred treatment for most pituitary and skull base tumors. This procedure uses the nostrils as natural corridors to remove relevant lesions, which requires a high level hands-on skills and rich clinical experience. With the aid of virtual reality technology, virtual surgical environments can provide a new solution for surgical training, planning, and rehearsal. In this paper, a multi-modal interactive system for EEA surgery simulation is presented. Due to the complex surgical procedures, the bone drilling task is identified as the core part for neurosurgeons to train and build their skills. A multi-modal interactive system providing visual, haptic and audible feedback is developed for bone drilling task. Besides, a customized 3-DOF haptic interface is utilized to control a virtual drill in the virtual environment rendered with patient-specific preoperative image data set. The preliminary experiment shows that this multi-modal interactive system can provide an immersive experience for neurosurgeons to perform the bone drilling task in a cost effective way.

Wilcoxon-norm-based robust extreme learning machine

It is known in statistics that the linear estimators using the rank-based Wilcoxon approach in linear regression problems are usually insensitive to outliers. Outliers are the data points that differ greatly from the pattern set by the bulk of the data. Inspired by this, Hsieh et al introduced the Wilcoxon approach into the area of machine learning. They investigated four new learning machines, such as Wilcoxon neural network (WNN) etc., and developed four descent gradient based backpropagation algorithms to train these learning machines. The performances of these machines are better than the ordinary nonrobust neural networks. However, it is hard to balance the learning speed and the stability of these algorithms which is inherently the drawback of gradient descent based algorithms. In this paper, a new algorithm is used to train the output weights of single-layer feedforward neural networks (SLFN) with its input weights and biases being randomly chosen. This algorithm is called Wilcoxon-norm based robust extreme learning machine or WRELM for short.