An Operating Stiffness Controller for the Medical Continuum Robot Based on Impedance Control.

Continuum manipulators can conform to curvilinear paths and manipulate objects in complex environments, which makes it emerging to be applied in minimally invasive surgery (MIS). However, different and controllable operating stiffness of the continuum manipulator is required during different stages of surgery to achieve safe access or stable and precise operation. This work proposes an operating stiffness controller (OSC) for the typical tendon-driven continuum manipulator based on the variable impedance control method with Lagrangian dynamic modeling. This controller can adjust the operating stiffness by modifying the driving forces along the driving tendons of the continuum manipulator without changing its material or structure. The proposed OSC converts the damping and stiffness matrices of the impedance control into variable parameters. This merit allows it to dynamically adjust the operating stiffness of the continuum manipulator according to the desired constant or time-varying stiffness. Furthermore, the OSC stability can be proven based on a Lyapunov function, and its stiffness control performances have been analyzed and evaluated in both simulations and experiments. The OSC controller generated average relevant error values of 7.82% and 3.09% for the operating stiffness control experiments with constant and time-varying desired stiffness, respectively. These experimental results indicate that the OSC has high accuracy, stability, and strong robustness in the operating stiffness control tasks.

A Novel Electromagnetic Driving System for 5-DOF Manipulation in Intraocular Microsurgery.

This work presents a novel electromagnetic driving system that consists of eight optimized electromagnets arranged in an optimal configuration and employs a control framework based on an active disturbance rejection controller (ADRC) and virtual boundary. The optimal system configuration enhances the system's compatibility with other ophthalmic surgical instruments, while also improving its capacity to generate magnetic force in the vertical direction. Besides, the optimal electromagnet parameters provide a superior comprehensive performance on magnetic field generation capacity and thermal power. Hence, the presented design achieves a stronger capacity for sustained work. Furthermore, the ADRC controller effectively monitors and further compensates the total disturbance as well as gravity to enhance the system's robustness. Meanwhile, the implementation of virtual boundaries substantially enhances interactive security via collision avoidance. The magnetic and thermal performance tests have been performed on the electromagnet to verify the design optimization. The proposed electromagnet can generate a superior magnetic field of 2.071 mT at a distance of 65 mm with an applied current of 1 A. Moreover, it demonstrates minimal temperature elevation from room temperature (25 °C) to 46 °C through natural heat dissipation in 3 h, thereby effectively supporting prolonged magnetic manipulation of intraocular microsurgery. Furthermore, trajectory tracking experiments with disturbances have been performed in a liquid environment similar to the practical ophthalmic surgery scenarios, to verify the robustness and security of the presented control framework. The maximum root mean square (RMS) error of performance tests in different operation modes remains 35.8 µm, providing stable support for intraocular microsurgery.

Physics-Informed Deep Learning for Muscle Force Prediction With Unlabeled sEMG Signals.

Computational biomechanical analysis plays a pivotal role in understanding and improving human movements and physical functions. Although physics-based modeling methods can interpret the dynamic interaction between the neural drive to muscle dynamics and joint kinematics, they suffer from high computational latency. In recent years, data-driven methods have emerged as a promising alternative due to their fast execution speed, but label information is still required during training, which is not easy to acquire in practice. To tackle these issues, this paper presents a novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method.

A Novel Extensible Continuum Robot with Growing Motion Capability Inspired by Plant Growth for Path-Following in Transoral Laryngeal Surgery.

This article presents a novel extensible continuum robot (ECR) with growing motion capability for improved flexible access in transoral laryngeal procedures. The robot uses an extensible continuum joint with a staggered V-shaped notched structure as the backbone, driven by the pushing and pulling of superelastic Nitinol rods. The notched structure is optimized to achieve a wide range of extension/contraction and bending motion for the continuum joint. The successive and uniform deflection of the notches provides the continuum joint with excellent constant curvature bending characteristics. The bidirectional rod-driven approach expands the robot's extension capabilities with both pushing and pulling operations, and the superelasticity of the driving rods preserves the robot's bending performance. The ECR significantly increases motion dexterity and reachability through its variable length, which facilitates collision-free access to deep lesions by following the anatomy. To further exploit the advantages of the ECR in path-following for flexible access, a growing motion approach inspired by the plant growth process has been proposed to minimize the path deviation error. Characterization experiments are conducted to verify the performances of the proposed ECR. The extension ratio achieves up to 225.92%, and the average distal positioning error and hysteresis error values are 2.87% and 0.51% within the ±120° bending range. Compared with the typical continuum robot with a fixed length, the path-following deviation of this robot is reduced by more than 58.30%, effectively reducing the risk of collision during access. Phantom experiments validate the feasibility of the proposed concept in flexible access procedures.

A Physics-Informed Low-Shot Adversarial Learning for sEMG-Based Estimation of Muscle Force and Joint Kinematics.

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot adversarial learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. A physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

Evolution of High-Index Facets Pt Nanocrystals Induced by N-Defective Sites in the Integrated Electrode for Enhanced Methanol Oxidation.

Highly efficient and durable Pt electrocatalysts are the key to boost the performance of fuel cells. The high-index facets (HIF) Pt nanocrystals are regarded as excellent catalytic activity and stability catalysts. However, nucleation, growth and evolution of high-index facets Pt nanocrystals induced by defective sites is still a challenge. In this work, tetrahexahedron (THH) and hexactahedron (HOH) Pt nanocrystals are synthesized, which are loaded on the nitrogen-doped reduced graphene oxide (N-rGO) support of the integrated electrodes by the square wave pulse method. Experimental investigations and density functional theory (DFT) calculations are conducted to analyze the growth and evolution mechanism of HIF Pt nanocrystals on the graphene-derived carbon supports. It shows that the H adsorption on the N-rGO/CFP support can induce evolution of Pt nanocrystals. Moreover, the N-defective sites on the surface of N-rGO can lead to a slower growth of Pt nanocrystals than that on the surface of reduced graphene oxide (rGO). Pt/N-rGO/CFP (20 min) shows the highest specific activity in methanol oxidation, which is 1.5 times higher than that of commercial Pt/C. This research paves the way on the design and synthesis of HIF Pt nanocrystal using graphene-derived carbon materials as substrates in the future.

SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated in various applications by projecting brain signals to robot control commands. However, the movement direction and speed are generally fixed and prescribed, neglecting the user's requirement for velocity changes during practical implementations. In this study, we proposed a velocity modulation method based on stimulus brightness for controlling the robotic arm in the SSVEP-based BCI system. A stimulation interface was designed, incorporating flickers, target and a cursor workspace. The synchronization of the cursor and robotic arm does not require the subject's eye switch between the stimuli and the robot. The feature vector consists of the characteristics of the signal and the classification result. Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. A brain-actuated speed function was designed by incorporating the posterior probability difference. Finally, the historical velocity was considered to determine the final velocity. To demonstrate the effectiveness of the proposed method, online experiments, including single- and multi-target reaching tasks, were conducted. The extensive experimental results validated the feasibility of the proposed method in reducing reaching time and achieving proximity to the target.

Synthesis, cytotoxicity, and biomacromolecule binding: Three isomers of nitrosylruthenium complexes with bidentate bioactive molecules as co-ligands.

Three isomeric nitrosylruthenium complexes [RuNO(Qn)(PZA)Cl] (P1, P2, and P3) with bioactive small molecules 8-hydroxyquinoline (Qn) and pyrazinamide (PZA) as co-ligands were synthesized, and their crystal structures were determined using X-ray diffraction technique. The cellular toxicity of the isomeric complexes was compared to understand the effects of the geometries on the biological activity of the complexes. Both the complexes and the human serum albumin (HSA) complex adducts affected the extent of proliferation of HeLa cells (IC50: 0.77-1.45 µM). P2 showed prominent activity-induced cell apoptosis and arrested cell cycles at the G1 phase. The binding constants (Kb) of the complex with calf thymus DNA (CT-DNA) and HSA were quantitatively evaluated using fluorescence spectroscopy in the range of 0.17-1.56 × 104 M-1 and 0.88-3.21 × 105 M-1, respectively. The average binding site (n) number was close to 1. Moreover, the structure of HSA and the P2 complex adduct solved at the resolution of 2.48 Å revealed that one PZA-coordinated nitrosylruthenium complex bound at the subdomain I of HSA via a noncoordinative bond. HSA could serve as a potential nano-delivery system. This study provides a framework for the rational design of metal-based drugs.

Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-based BCIs.

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been substantially studied in recent years due to their fast communication rate and high signal-to-noise ratio. The transfer learning is typically utilized to improve the performance of SSVEP-based BCIs with auxiliary data from the source domain. This study proposed an inter-subject transfer learning method for enhancing SSVEP recognition performance through transferred templates and transferred spatial filters. In our method, the spatial filter was trained via multiple covariance maximization to extract SSVEP-related information. The relationships between the training trial, the individual template, and the artificially constructed reference are involved in the training process. The spatial filters are applied to the above templates to form two new transferred templates, and the transferred spatial filters are obtained accordingly via the least-square regression. The contribution scores of different source subjects can be calculated based on the distance between the source subject and the target subject. Finally, a four-dimensional feature vector is constructed for SSVEP detection. To demonstrate the effectiveness of the proposed method, a publicly available dataset and a self-collected dataset were employed for performance evaluation. The extensive experimental results validated the feasibility of the proposed method for improving SSVEP detection.

Enhanced piezotronics by single-crystalline ferroelectrics for uniformly strengthening the piezo-photocatalysis of electrospun BaTiO3@TiO2 nanofibers.

Turning the built-in electric field by modulating the morphology and microstructure of ferroelectric materials is considered a viable approach to enhancing the piezo-photocatalytic activity of the ferroelectric/oxide semiconductor heterojunctions. Here, hydrothermally synthesized single-crystalline BaTiO3 nanoparticles are employed to construct BaTiO3@TiO2 hybrid nanofibers by sol-gel assisted electrospinning of TiO2 nanofibers and annealing. Because of the obvious enhancement of the synergetic piezo-photocatalytic effect under both ultrasonic and ultraviolet (UV) light irradiation, the piezo-photocatalytic degradation rate constant (k) of BaTiO3@TiO2 hybrid nanofibers on methyl orange (MO) reaches 14.84 × 10-2 min-1, which is approximately seven fold that for piezocatalysis and six fold that for photocatalysis. Moreover, BaTiO3@TiO2 core-shell nanoparticles are also synthesized for comparison purposes to assess the influence of microstructure on the piezo-photocatalysis by a wet-chemical coating of TiO2 on BaTiO3 nanoparticles. Such a high piezo-photocatalytic activity is attributed to the enhancement of the piezotronic effect by the single-crystalline ferroelectric nanoparticles and the nanoconfinement effect caused by the one-dimensional boundary of nanofibers with high specific surface areas. The mechanically induced uniform local built-in electric fields originated from the single-crystalline ferroelectric nanoparticles can enhance the separation of photogenerated electron and hole pairs and promote the formation of free hydroxyl radicals, resulting in a strong piezotronic effect boosted photochemical degradation of organic dye. This work introduces the single-crystalline ferroelectrics to construct ferroelectric/oxide semiconductor heterojunctions, and the enhanced local piezotronic effect uniformly strengthens the photochemical reactivity, which offers a new option to design high-efficiency piezo-photocatalysts for pollutant treatment.

Design and Performance Investigation of a Robot-Assisted Flexible Ureteroscopy System.

Flexible ureteroscopy (FURS) has been developed and has become a preferred routine procedure for both diagnosis and treatment of kidney stones and other renal diseases inside the urinary tract. The traditional manual FURS procedure is highly skill-demanding and easily brings about physical fatigue and burnout for surgeons. The improper operational ergonomics and fragile instruments also hinder its further development and patient safety enhancement. A robotic system is presented in this paper to assist the FURS procedure. The system with a master-slave configuration is designed based on the requirement analysis in manual operation. A joint-to-joint mapping strategy and several control strategies are built to realize intuitive and safe operations. Both phantom and animal experiments validate that the robot has significant advantages over manual operations, including the easy-to-use manner, reduced intraoperative time, and improved surgical ergonomics. The proposed robotic system can solve the major drawbacks of manual FURS. The test results demonstrate that the robot has great potential for clinical applications.

Correction to: A High-Precision and Miniature Fiber Bragg Grating-Based Force Sensor for Tissue Palpation During Minimally Invasive Surgery.

The second equation in the section "Definition of Objective Functions and Constraints" was corrected to fix a mismatch between the PDF and HTML versions of the article.

A High-Precision and Miniature Fiber Bragg Grating-Based Force Sensor for Tissue Palpation During Minimally Invasive Surgery.

This paper presents a novel Fiber Bragg Grating (FBG)-based palpation force sensor to explore tissue abnormalities during minimally invasive surgery. The proposed sensor design mainly consists of a miniature force-sensitive flexure, one tightly suspended optical fiber embedded with one FBG element and associated connectors and fixations. The flexure design has been prototyped through the configuration synthesis of Sarrus mechanism by using a rigid-body replacement method to achieve an excellent axial linear force-deformation relationship and a large measurement range. The mounted fiber has been configured at the flexure's central line with its two ends glued, and its tight suspension configuration can achieve improved resolution and sensitivity and avoid the FBG chirping failure compared to the commonly used direct FBG-pasting methods. Finite element method (FEM)-based simulation has been performed to investigate both static and dynamic performance to aid in structural design. Simulation-enabled structural optimization design has also been implemented to further improve the proposed design and the sensor's sensitivity has been increased. The optimized sensor design has been prototyped and calibrated to demonstrate an excellent linearity with a small linearity error of 0.97% and achieve a high resolution of 2.55 mN within a relatively large measurement range of 0-5 N. Dynamic force stimulation experiments, in vitro palpation implementation on a silicone phantom embedded with simulated tumors and ex vivo indentation experiments on a porcine liver have validated the effectiveness of the presented sensor design.

Impacts of Built Environment on Urban Vitality: Regression Analyses of Beijing and Chengdu, China.

The loss of urban vitality is an important problem in the development of urban central areas. Analyzing the correlation between urban built environment and urban vitality supports urban planning and design. However, current research excludes the study of how consistent built environment factors affect urban vitality of cities with different development situations. Therefore, using social media check-in data, this paper measures neighborhood vibrancy in urban central areas in Beijing and Chengdu, China. Four levels of spatial information were used to measure the built environment: regulatory planning management unit (RPMU), land use, road network, and building. Regression model is used to quantify the correlation between urban vitality and the built environment of these two cities. The study found a strong correlation between built environment factors and urban vitality. Among the built environment factors, points of interest (POI) diversity and public transport accessibility indicators were strongly positively correlated with neighborhood vibrancy. However, the density indicators had totally different effects on urban vitality of cities with different development situations, which is excluded in existing studies. This research strengthens the practical understanding of the compact city concept, and can support the design and planning of urban built environment.

Visual Servoing-Based Nanorobotic System for Automated Electrical Characterization of Nanotubes inside SEM.

The maneuvering and electrical characterization of nanotubes inside a scanning electron microscope (SEM) has historically been time-consuming and laborious for operators. Before the development of automated nanomanipulation-enabled techniques for the performance of pick-and-place and characterization of nanoobjects, these functions were still incomplete and largely operated manually. In this paper, a dual-probe nanomanipulation system vision-based feedback was demonstrated to automatically perform 3D nanomanipulation tasks, to investigate the electrical characterization of nanotubes. The XY-position of Atomic Force Microscope (AFM) cantilevers and individual carbon nanotubes (CNTs) were precisely recognized via a series of image processing operations. A coarse-to-fine positioning strategy in the Z-direction was applied through the combination of the sharpness-based depth estimation method and the contact-detection method. The use of nanorobotic magnification-regulated speed aided in improving working efficiency and reliability. Additionally, we proposed automated alignment of manipulator axes by visual tracking the movement trajectory of the end effector. The experimental results indicate the system's capability for automated measurement electrical characterization of CNTs. Furthermore, the automated nanomanipulation system has the potential to be extended to other nanomanipulation tasks.

Three-Dimensional Intravascular Reconstruction Techniques Based on Intravascular Ultrasound: A Technical Review.

Intravascular ultrasound (IVUS) imaging provides two-dimensional (2-D) real-time luminal and transmural cross-sectional images of intravascular vessels with detailed pathological information. It has offered significant advantages in terms of diagnosis and guidance and has been increasingly introduced from coronary interventions into more generalized endovascular surgery. However, IVUS itself does not provide spatial pose information for its generated images, making it difficult to construct a 3-D intravascular visualization. To address this limitation, IVUS imaging-driven 3-D intravascular reconstruction techniques have been developed. These techniques enable accurate diagnosis and quantitative measurements of intravascular diseases to facilitate optimal treatment determination. Such reconstruction extends the IVUS imaging modality from pure diagnostic assistance to intraoperative navigation and guidance and supports both therapeutic options and interventional operations. This paper presents a comprehensive survey of technological advances and recent progress on IVUS imaging-based 3-D intravascular reconstruction and its state-of-the-art applications. Limitations of existing technologies and prospects of new technologies are also discussed.

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks.

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

Shape Sensing Techniques for Continuum Robots in Minimally Invasive Surgery: A Survey.

Continuum robots provide inherent structural compliance with high dexterity to access the surgical target sites along tortuous anatomical paths under constrained environments and enable to perform complex and delicate operations through small incisions in minimally invasive surgery. These advantages enable their broad applications with minimal trauma and make challenging clinical procedures possible with miniaturized instrumentation and high curvilinear access capabilities. However, their inherent deformable designs make it difficult to realize 3-D intraoperative real-time shape sensing to accurately model their shape. Solutions to this limitation can lead themselves to further develop closely associated techniques of closed-loop control, path planning, human-robot interaction, and surgical manipulation safety concerns in minimally invasive surgery. Although extensive model-based research that relies on kinematics and mechanics has been performed, accurate shape sensing of continuum robots remains challenging, particularly in cases of unknown and dynamic payloads. This survey investigates the recent advances in alternative emerging techniques for 3-D shape sensing in this field and focuses on the following categories: fiber-optic-sensor-based, electromagnetic-tracking-based, and intraoperative imaging modality-based shape-reconstruction methods. The limitations of existing technologies and prospects of new technologies are also discussed.

Real-time in vitro intravascular reconstruction and navigation for endovascular aortic stent grafting.

BACKGROUND: Trans-catheter endovascular stent grafting minimizes trauma and increases the benefitting patient population. However, the alignment between stent graft branches and vasculature branches remains time-consuming and challenging, and such techniques require a significant amount of contrast agent for imaging. METHODS: A new framework for intravascular reconstruction based on sensor fusion between intravascular ultrasound (IVUS) imaging and electromagnetic (EM) tracking was proposed. A new image processing method was presented to realize fully automatic processing of IVUS imaging and 3D reconstruction in real time, as well as branch detection for alignment and deployment. Complementary navigation using CT data allows for efficient catheter advancement and assistant clinical judgement. RESULTS: The reconstruction of an in vitro descending aorta phantom with branches was realized at 35 Hz, with cross-section radius average error of 0.64 mm. CONCLUSION: The proposed method demonstrates significant potential for clinical applications, enables navigation for precise alignment and placement for stent grafting to reduce surgical time, and decreases hemorrhagic collisions and the use of contrast agent. Copyright © 2016 John Wiley & Sons, Ltd.

Recent advances in nanorobotic manipulation inside scanning electron microscopes.

A scanning electron microscope (SEM) provides real-time imaging with nanometer resolution and a large scanning area, which enables the development and integration of robotic nanomanipulation systems inside a vacuum chamber to realize simultaneous imaging and direct interactions with nanoscaled samples. Emerging techniques for nanorobotic manipulation during SEM imaging enable the characterization of nanomaterials and nanostructures and the prototyping/assembly of nanodevices. This paper presents a comprehensive survey of recent advances in nanorobotic manipulation, including the development of nanomanipulation platforms, tools, changeable toolboxes, sensing units, control strategies, electron beam-induced deposition approaches, automation techniques, and nanomanipulation-enabled applications and discoveries. The limitations of the existing technologies and prospects for new technologies are also discussed.