This paper proposes a fiber Bragg grating (FBG)-based high-precision sensor for cardiovascular pulse monitoring in real-time. The sensor prototype mainly consists of a force-sensitive flexure in a parallel structure configuration, a suspended optical fiber inscribed with an FBG element, a contact pad, and a wearable elastic band. The proposed flexure develops from a six-bar parallel mechanism based on the rigid body replacement method and achieves a compact, miniatured, and wearable design. This flexure converts the longitudinal pulse input into a horizontal deformation/force output, supports the force amplification with a simplified bridge-type amplified mechanism, and achieves improved sensitivity of pulse waveform measurement. The FBG optical fiber has been horizontally suspended and assembled on the flexure with a two-point pasting configuration and sensed the horizontal stretching and compression-induced strain variation. The parallel flexure based on a dual design has been introduced to depress the crosstalk among the lateral directions and the influences of external measurement disturbances. Design optimization has been performed based on the finite element method (FEM) simulation to improve the sensor sensitivity. Both static and dynamic experiments verify the performances of the optimized wearable sensor design. The sensor sensitivity achieves an excellent sensitivity of 1547.3 pm/N with a small linearity error of 0.38%, and negligible crosstalk of less than 2% in radial directions, validating its negligible anti-turbulence capability. The proposed sensor design also supports the easy implementation of the handheld sensor form. The carotid pulse measurement experiments for both the wearable and handheld sensor forms were carried out to validate the effectiveness of the proposed sensor design.

This work proposes a novel flexible manipulator consisting of a series of 2-DOF vertebrae based on a ball-andsocket joint that is connected by a ball-shaped surface and a cupshaped socket and constrained by pins for circumferential rotation. This manipulator can demonstrate outstanding torsional stiffness since the circumferential rotation between the vertebrae is constrained by four ball pins. The point contact between ball pins and guideways effectively reduces the friction between the vertebrae, thus allowing the designed manipulator to yield a smooth bending shape with constant curvature. This manipulator features high axial and torsional stiffness, excellent bending performance, sufficient loading capacity, and convenient integration with surgical instruments. Moreover, the excellent torsional stiffness enables this manipulator to efficiently transfer torque and be applied in in-situ torsional motion, effectively addressing the typical issue of limited dexterity for torsional motion. The kinematic modeling of the proposed manipulator under in-situ torsional motion has been derived, and its workspace has been analyzed. A robotic system has been assembled, and experiments have verified the proposed design and modeling validity. The results show that the maximum position errors in bending motion are 2.39% (horizontal direction) and 1.98% (vertical direction), and its torsional stiffness is 21.13N∙mm/deg, which is 46 times higher than that of a typical spherical flexible manipulator (SFM). Such merits support this manipulator excellently performing the in-situ torsional motion with a maximum average position error of 3.58%. Furthermore, a phantom test of the larynx has been performed to verify the potential of clinical feasibility.

Continuum manipulators can manipulate objects in complex environments and conform to curvilinear paths, which makes it emerging to be applied in minimally invasive surgery. However, due to their critical nonlinearities and distinct timesequential characteristics of motion states, the modeling of inverse kinematics remains challenging. This work proposes a model-free method based on a timing input neural network (TINN) model to obtain the inverse kinematics mapping relationship of tendonactuated medical continuum manipulators. The new TINN model improves the traditional fully connected neural network (FNN) model’s ability to process time-sequential information through a sampling layer consisting of a modified window function positioned in front of the core layers. The lightweight fully connected structure of TINNs core layers can be trained effectively with fewer epochs or less time compared with the long short-term memory (LSTM) model. Furthermore, this lightweight structure maintains the robustness and accuracy of the TINN model when the training data volume decreases. Through experimental validation on two kinds of tendon-actuated continuum manipulators, this TINN-based model-free method shows high accuracy and strong robustness against the decrease of training data volume, as well as high transferability. Meanwhile, the TINN model’s effective utilization of feedback data results in higher precision in closed-loop control compared to traditional model-based PID controllers in free space and under various payload conditions.

Fiber-reinforced thermoplastics (FRTPs) have become a new generation of lightweight materials due to their superior mechanical properties, good weldability and environmental resistance, potential for recycling, etc. The market for FRTPs is expected to grow at a compound annual growth rate (CAGR) of 7.8% from 2022 to 2030. Many researchers have been trying to solve the problems in their processing and joining process, and gradually expand their application. Resistance welding is one of the most suitable techniques to join FRTPs. This paper summarizes the research progress of FRTP resistance welding in terms of the basic process of FRTP resistance welding, factors affecting joint performance, joint failure behavior, numerical simulation, weld quality control, and resistance welding of thermoplastic/thermoset composites. The objective of this paper is to provide a deeper insight into the knowledge of FRTP resistance welding and provide reference for the further development and application of FRTP resistance welding.