Shape sensors are important for safer and more dexterous manipulation of the medical catheters. Among the electromagnetic based shape sensors, a voice coil shape sensor measures the variation of a mutual inductance between the coils placed along the tube due to the bending of the tube. Owing to the design flexibility of a voice coil, it offers the small size without the external magnetic field generator outside patient body. This paper presents an improved voice coil shape sensor in terms of modeling and measurement. Analytic model that incorporates the bending of an exciter coils is used to improve the accuracy of the sensor and band-pass filter is applied to simplify the measurements. Bending angle from sensors placed in multiple locations can be measured through a single channel from frequency sweep input. The simulation and experimental results verify the improvement and demonstrate that the sensor system can reconstruct a shape of a catheter placed through larynx.

We present a transformable catheter head structure for endoscopic catheter allowing the simultaneous use of a camera module and a large tool channel introduced through a small incision. At the site of interest, the head with a camera can be expanded from the initial straight configuration, which opens a window for advancing a tool that is located behind the camera. Two different designs were proposed and prototyped. One option has flexure joints directly fabricated at the distal end of a polymer catheter by laser micro-machining, while another design employs a hinged metal head assembled at the tip of the same type of catheter. The kinematic behavior of each head was evaluated during the head-up and tip steering motions, and compared with each other to draw a selection guideline between them. Experimental results prove the feasibility of the proposed head structure for smarter endoscopic catheters.

Variable stiffness enables the safe and effective operation of the minimally invasive surgical instruments. In this article, we propose a continuously variable stiffness mechanism of the scalable tubular structure. The mechanism consists of multiple coaxial nitinol tubes, and each tube has an anisotropic distribution of flexural stiffness created by nonuniform through-hole patterning. The stiffness of the mechanism is varied by relative rotation and translation among the tubes, resulting in flexural stiffness difference up to 7.2 times in the direction of load. Its flexural stiffnesses along principal axes are independently controlled by the suggested counterrotation algorithm. The stiffness change is validated through analytical modeling, FEM simulation, and the experiments. Thanks to its physically embodied intelligence, the mechanism has a simple scalable structure and the response time is immediate. We applied this mechanism to control the stiffness of the steerable needle. Varying the stiffness grants the additional degree of freedom to control the needle's trajectory, which can expand the workspace of the steerable needle.

Kinematic models of concentric tube robots have matured from considering only tube bending to considering tube twisting as well as external loading. While these models have been demonstrated to approximate actual behavior, modeling error can be significant for medical applications that often call for positioning accuracy of 1-2mm. As an alternative to moving to more complex models, this paper proposes using sensing to adaptively update model parameters during robot operation. Advantages of this method are that the model is constantly tuning itself to provide high accuracy in the region of the workspace where it is currently operating. It also adapts automatically to changes in robot shape and compliance associated with the insertion and removal of tools through its lumen. As an initial exploration of this approach, a recursive on-line estimator is proposed and evaluated experimentally.

The paper presents a new robot for manipulating a transrectal ultrasound probe for image-guided intervention of the prostate, the TRUS robot. The robot positions and orients the probe for image scanning and needle targeting of the prostate. Image slices and their respective position are mapped three-dimensionally to render volumetric images. The robot is designed to accommodate the constraints of the clinical prostate intervention. The robot includes all 4 degrees-of-freedom that are available in manual handling of the probe. In-vitro studies on pelvic mockups verify 3D imaging capabilities. The robot is clinically used in robot-assisted laparoscopic radical prostatectomy for providing intraoperative ultrasound-based navigation for the surgeon.

This paper presents the development and performance evaluation of a human-computer interface that enables a limb-disabled person to access a computer via neural signals. For this purpose, electromyogram (EMG) signals were extracted from four muscles on the lower arm, and signal statistics (namely the mean and variance) were used for a filtering process. Six patterns were then classified through the application of a supervised multilayer neural network trained by a backpropagation algorithm. To extract the user's intentions, such as cursor movements and a clicking, the authors applied the neural network in the classification of the six patterns. In addition, an on-screen keyboard was developed so that letters of the Roman and Korean alphabets could be keyed into the computer. Finally, to confirm the appropriation of the developed computer interface, the authors applied Fitts' law in an experimental study to evaluate the performance of the computer interface. The experimental results show that the computer interface had an index of performance, or bandwidth, of 1.299. However, although the developed EMG-based human-computer interface had a lower index of performance than a mouse, it provides an alternative means of computer access for those with a disabled upper limb.