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Co–FeSe2 nanozymes were designed which exhibited synergistic catalytic-photothermal anti-tumor performance. A ‘ferroptosis–apoptosis’ dual pathway was found for Co–FeSe2 nanozymes to promote cancer cell death.

Image registration is a crucial technology in robot-assisted knee arthroplasty, which provides real-time patient information by registering the pre-operative image data with data acquired during the operation. The existing registration method requires surgeons to manually pick up medical feature points (i.e. anatomical points) in pre-operative images, which is time-consuming and relied on surgeons experience. Moreover, different doctors have different preferences in preoperative planning, which may influence the consistency of surgical results.A medical feature points automatic extraction method based on PointNet++ named Point_RegNet is proposed to improve the efficiency of preoperative preparation and ensure the consistency of surgical results. The proposed method replaces the classification and segmentation layer of PointNet++ with a regression layer to predict the position of feature points. The comparative experiment is adopted to determine the optimal set of abstraction layers in PointNet++.The proposed network with three set abstraction layers is more suitable for extracting feature points. The feature points predictions mean error of our method is less than 5 mm, which is 1 mm less than the manual marking method. Ultimately, our method only requires less than 3 s to extract all medical feature points in practical application. It is much faster than the manual extraction way which usually requires more than half an hour to mark all necessary feature points.Our deep learning-based method can improve the surgery accuracy and reduce the preoperative preparation time. Moreover, this method can also be applied to other surgical navigation systems.

As an emerging multifunctional material, Gallium-based room temperature liquid metal has attracted a lot of attention for a variety of applications due to its mobility and deformability. However, controlling the motion of a liquid metal droplet accurately still remains unrevealed, which restricts its application in many fields. In this article, we propose a hybrid framework that would control the motion of a liquid metal droplet in a one-dimensional (1-D) fluidic channel. A dynamic model of a liquid metal droplet immersed in the electrolyte when an electrical field is applied to each end of the channel is discussed first, followed by a setpoint controller designed to calculate the current input needed to drive the liquid metal droplet to its destination with vision feedback. To obtain the desired high-resolution current output, a fast and high-resolution current output power supply will be established by integrating a fast PID controller and a simple programmable dc power supply. The effectiveness of this proposed approach will be verified by controlling a liquid metal droplet so that it reaches its destination inside the polymethyl methacrylate channel. In this article, the proposed approach may lead to the development of tiny soft robots, or microfluidic systems that can be driven accurately by the liquid metal droplets.

Since more and more elderly people suffer from lower extremity movement problems, it is of great social significance to assist persons with motor dysfunction to walk independently again and reduce the burden on caregivers. The self-paced walking intention, which could increase the ability of self-control on the start and stop of motion, was studied by applying brain-computer interface (BCI) technology, a novel research field. The cerebral hemoglobin signal, which was obtained from 30 subjects by applying functional near-infrared spectroscopy (fNIRS) technology, was processed to detect self-paced walking intention in this paper. Teager-Kaiser energy was extracted at each sampling point for five sub-bands (0.0095~0.021 Hz, 0.021~0.052 Hz, 0.052~0.145 Hz, 0.145~0.6 Hz, and 0.6~2.0 Hz). Gradient boosting decision tree (GBDT) was then utilized to establish the detecting model in real-time. The proposed method had a good performance to detect the walking intention and passed the pseudo-online test with a true positive rate of 100% (80/80), a false positive rate of 2.91% (4822/165171), and a detection latency of 0.39 ± 1.06 s. GBDT method had an area under the curve value of 0.944 and was 0.125 (p 0.001) higher than linear discriminant analysis (LDA). The results reflected that it is feasible to decode self-paced walking intention by applying fNIRS technology. This study lays a foundation for applying fNIRS-based BCI technology to control walking assistive devices practically. Graphical abstract Graphical representation of the detecting process for pseudo-online test. The lower figure is a partial enlargement of the upper figure. In the lower figure, the blue line represents the probability of walking predicted by GBDT without smoothing and the orange-red line represents the smoothed probability. The dark-red ellipse shows the effect of the smoothing-threshold method.

In chest and abdomen robotic radiosurgery, due to the motion delay of the robotic manipulator, the tumor position tracking process has a period of delay. This delay ultimately affects the accuracy of radiosurgery treatment. To address the influence of the delay in robotic radiosurgery, a Long-and-Short-Term Memory (LSTM) network as a deep Recurrent Neural Network (RNN) has been applied in a prediction network model for respiratory motion tracking in recent years. However, patients’ respiratory state may change in the process of treatment, which may influence the accuracy of prediction. Therefore, it is necessary to update the prediction network through additional data, such as the actual position of the tumor obtained by X-ray imaging. However, the LSTM network has a long update time, and it may not be able to complete the prediction model update in a cycle of X-ray acquisition. To solve this problem, a fast prediction model based on Bidirectional Gated Recurrent Unit (Bi-GRU), is proposed in this paper. This method can reduce the average updating time of the network model by 30%.

A biopsy needle is a tool employed to fetch a portion of tissue at a point in the volume of diseased tissue, i.e., a Single Sample with a Single Spoon (SS-SS). Using this tool, the pathological status of the unhealthy tissue is generally represented by that of the specimen. However, for the tumor growing heterogeneously, like brainstem glioma, its inner constituent is heterogeneous. Therefore, the histological status of the specimen cannot precisely represent that of other points except the sampled point itself. The common principle of the existing solutions to this problem is to design the needle that fulfills biopsy operation by obtaining Multiple Samples with a Single Spoon at different points (MS-SS). The drawback of the designs following this principle is that during one biopsy operation, the latter samples are contaminated by their formers. The reason for the shortcoming is that the same spoon grabs all samples. To solve this problem, we design a novel biopsy tool that implements a single insertion to obtain Multiple Samples in multiple desired points with Multiple Spoons (MS-MS). The detailed system structure design, the motion procedure of the needle spoon, and the critical stress on the crucial mechanical parts are explicitly presented. Experiments using a built prototype is carried out on agar phantom and ex vivo porcine tissue, respectively. The results demonstrate the feasibility of the novel position-adjustable multi-point synchronizing biopsy tool for investigating intratumoral heterogeneity.

The calibration of binocular infrared camera is a key step to realize the positioning of infrared camera. The existing methods of binocular camera calibration are not suitable for infrared camera, so it is necessary to study the methods of binocular infrared camera calibration and positioning. In this paper, a calibration and positioning method of binocular infrared camera is proposed, and the infrared positioning of two kinds of objects is completed by using this method. Firstly, a special temperature calibration system is used to collect the images for binocular calibration, then the center point detection algorithm and Zhang’s calibration method are used to process the infrared calibration image to obtain the internal and external parameters of the binocular infrared camera. The calibration error is compared with that of binocular visible camera to prove the feasibility of the binocular infrared camera calibration. Secondly, according to the calibrated parameter matrix of the binocular infrared camera, symmetrical circle calibration object detection algorithm and coordinate transformation are carried out for the distortion correction images to achieve infrared positioning of different objects, and the positioning error is obtained. The results show that the calibration method of binocular infrared camera is feasible, and it has more functions than the traditional calibration method.

Anal center detection is of great significance for the diagnosis of anorectal diseases, for accurate anal center detection can help gastrointestinal (GI) robot enter the human body automatically and check the patient’s anal and lower intestinal diseases, which is the first step to realize autonomous diagnosis. However, there is no available result on the anal center detection. In this work, the superpixel method is employed to find the anal center. In the first step, the collected image dataset is expanded through the data augmentation method. In the second step, we use the superpixel segmentation method, a machine learning algorithm, to segment the image by pixels with similar features in the image. Then we determine the region of interest (ROI) based on the threshold and the size of the connected region. After that, the gray barycenter method is used to determine the center of gravity of the ROI i.e., the anal center. The ground-truth anal center is obtained by the average of the anal center coordinates determined by ten anorectal surgeons. By the proposed algorithm, it is found that the ROI detected in 70.59% of the images in the dataset includes the anal center, and the positioning accuracy of the anal center is 88.87% averagely. Thus, the method can provide the anal center for GI robot.

It is easy for human body to contact with objects for a long time to cause fatigue of corresponding parts. How to use robot technology to reduce or eliminate this fatigue in order to improve work efficiency, ensure human health and improve comfort is an important problem in the field of robot. In this paper, combined with the characteristics of animal tissues (puffer’s air bag, etc.), through the application of soft robot related technology, a set of program-controlled active flexible massage system is made. The system is mainly composed of support frame, air bag placement module and electrical system. The system can be freely deformed and combined, soft and comfortable, and has strong expansibility. In addition, we propose a control method to calculate the relationship between the air pressure inside the airbag and the surface tension of the airbag, set four working modes for the system, and measure the air pressure inside the airbag under the four modes. The results show that the proposed control method is feasible and will have a very broad application prospect in medical rehabilitation.

Motion control strategies of currently available bioprinters are mainly based on the Cartesian coordinate system to perform layer-by-layer stacking of materials along the vertical direction through the axial-aligned horizontal slicing method. The stacked bioprinting strategy that is inconsistent with the growing pattern of natural biological tissues/organs with anisotropic property in some sense, is stimulating new bioprinting techniques such as the stereotactic bioprinting. This paper analytically calculates the forward and inverse kinematics of a four degree-of-freedom (4-DOF) robotic bioprinter prototype developed in our group which is featured with a 3-DOF manipulator and a 1-DOF circular rail for stereotactic bioprinting. Numerical simulations of working space and motion characteristics using MATLAB and V-REP software have validated the performance and feasibility of the developed prototype, which provides a robot-assisted solution reference for the stereotactic bioprinting.

Cerebral stroke is a common disease across the world, and it is a promising method to recognize the intention of stroke patients with the help of brain–computer interface (BCI). In the field of motor imagery (MI) classification, appropriate filtering is vital for feature extracting of electroencephalogram (EEG) signals and consequently influences the accuracy of MI classification. In this case, a novel two-stage refine filtering method was proposed, inspired by Gradient-weighted Class Activation Mapping (Grad-CAM), which uses the gradients of any target concept flowing into the final convolutional layer to highlight the important part of training data for predicting the concept. In the first stage, MI classification was carried out and then the frequency band to be filtered was calculated according to the Grad-CAM of the MI classification results. In the second stage, EEG was filtered and classified for a higher classification accuracy. To evaluate the filtering effect, this method was applied to the multi-branch neural network proposed in our previous work. Experiment results revealed that the proposed method reached state-of-the-art classification kappa value levels and acquired at least 3% higher kappa values than other methods This study also proposed some promising application scenarios with this filtering method.

Hard tissue defects have always been a major problem in clinical practices that plague humans because of population aging or injury. In the meantime, 3D bioprinting has increasingly gained tremendous progresses over the past decade in the field of biomedical engineering because of the ability to fabricate rapidly individualized tissue constructs and precise geometry designability. With the development of solvent-free, water-based systems, biomaterials, especially hydrogel-based bioink, could be directly bioprinted into scaffolds or tissue constructs utilizing 3D bioprinting, and cells could be cultivated and proliferated on the bioprinted scaffolds, further for the subsequent transplantation in clinical practice. And, the foremost biocompatible materials employed in existing 3D bioprinting method are hydrogels, which could be laden with different cell types and easily synthesized from natural polymers. In this chapter, the properties, structures and applications of 3D bioprinted hydrogel scaffolds for hard tissue including cartilage and bone are reviewed in conjunction with recent advances. And the current limitations such as low mechanical properties, poor stabilities and printability are presented, as well as the corresponding improved methods. We finally conclude with prospects about 3D bioprinting in hard tissue regeneration.

One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled electroencephalogram (EEG) representation method which can preserve not only temporal features but also spatial ones. To fully utilize the features on various dimensions of EEG, a novel MI classification framework is first introduced in this paper, including a new 3D representation of EEG, a multi-branch 3D convolutional neural network (3D CNN) and the corresponding classification strategy. The 3D representation is generated by transforming EEG signals into a sequence of 2D array which preserves spatial distribution of sampling electrodes. The multi-branch 3D CNN and classification strategy are designed accordingly for the 3D representation. Experimental evaluation reveals that the proposed framework reaches state-of-the-art classification kappa value level and significantly outperforms other algorithms by 50% decrease in standard deviation of different subjects, which shows good performance and excellent robustness on different subjects. The framework also shows great performance with only nine sampling electrodes, which can significantly enhance its practicality. Moreover, the multi-branch structure exhibits its low latency and a strong ability in mitigating overfitting issues which often occur in MI classification because of the small training dataset.

This paper aims at realizing upper limb rehabilitation training by using an fNIRS-BCI system. This article mainly focuses on the analysis and research of the cerebral blood oxygen signal in the system, and gradually extends the analysis and recognition method of the movement intention in the cerebral blood oxygen signal to the actual brain-computer interface system. Fifty subjects completed four upper limb movement paradigms: Lifting-up, putting down, pulling back, and pushing forward. Then, their near-infrared data and movement trigger signals were collected. In terms of the recognition algorithm for detecting the initial intention of upper limb movements, gradient boosting tree (GBDT) and random forest (RF) were selected for classification experiments. Finally, RF classifier with better comprehensive indicators was selected as the final classification algorithm. The best offline recognition rate was 94.4% (151/160). The ReliefF algorithm based on distance measurement and the genetic algorithm proposed in the genetic theory were used to select features. In terms of upper limb motion state recognition algorithms, logistic regression (LR), support vector machine (SVM), naive Bayes (NB), and linear discriminant analysis (LDA) were selected for experiments. Kappa coefficient was used as the classification index to evaluate the performance of the classifier. Finally, SVM classification got the best performance, and the four-class recognition accuracy rate was 84.4%. The results show that RF and SVM can achieve high recognition accuracy in motion intentions and the upper limb rehabilitation system designed in this paper has great application significance.

Three-dimensional (3D) bioprinting has been widely applied in the field of biomedical engineering because of its rapidly individualized fabrication and precisely geometric designability. The emerging demand for bioprinted tissues/organs with bio-inspired anisotropic property is stimulating new bioprinting strategies. Stereotactic bioprinting is regarded as a preferable strategy for this purpose, which can perform bioprinting at the target position from any desired orientation in 3D space. In this work, based on the motion characteristics analysis of the stacked bioprinting technologies, mechanism configurations and path planning methods for robotic stereotactic bioprinting were investigated and a prototype system based on the double parallelogram mechanism was introduced in detail. Moreover, the influence of the time dimension on stereotactic bioprinting was discussed. Finally, technical challenges and future trends of stereotactic bioprinting within the field of biomedical engineering were summarized.

In robotic radiosurgery, motion tracking is crucial for accurate treatment planning of tumor inside the thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers that are placed on the surface of human chest. Nevertheless, limited markers are not capable of expressing the comprehensive motion feature of the human chest and abdomen. In this paper, we proposed a method of respiratory motion characterization based on the voxel modeling of the thoracoabdominal torso. Point cloud data from depth cameras were used to achieve three-dimensional modeling of the chest and abdomen surface during respiration, and a dimensionality reduction algorithm was proposed to extract respiratory features from the established voxel model. Finally, experimental results including the accuracy of voxel model and correlation coefficient were compared to validate the feasibility of the proposed method, which provides enhanced accuracy of target motion correlation than traditional methods that utilized external markers.

Topology optimization is a widely used lightweight design method for structural design of the collaborative robot. In this article, a topology optimization method for the robot lightweight design is proposed based on finite element analysis of the assembly so as to get the minimized weight and to avoid the stress analysis distortion phenomenon that compared the conventional topology optimization method by adding equivalent confining forces at the analyzed part’s boundary. For this method, the stress and deformation of the robot’s parts are calculated based on the finite element analysis of the assembly model. Then, the structure of the parts is redesigned with the goal of minimized mass and the constraint of maximum displacement of the robot’s end by topology optimization. The proposed method has the advantages of a better lightweight effect compared with the conventional one, which is demonstrated by a simple two-linkage robot lightweight design. Finally, the method is applied on a 5 degree of freedom upper-limb exoskeleton robot for lightweight design. Results show that there is a 10.4% reduction of the mass compared with the conventional method.

The existing topology optimization for robot lightweight design is carried out under a given extreme working pose (ie, working con-dition) when doing the finite element analysis (FEA). As the working pose of the robot is time-varying, the results with this method may not satisfy all the working conditions of the robot. In this paper, a new topology optimization method under mul-ti-working conditions(MWC) is proposed for robot lightweight design. First, the different typical working conditions are selected according to the application situations of the robot, and then the final extreme working conditions for optimization are obtained by using of FEA method. Next, the structure of the parts is rede-signed with topology optimization method based on the extreme working conditions by the constraints of maximum end dis-placement and the goal of minimized mass. Then, the method is applied to the lightweight design of a 5 DOF upper-limb powered exoskeleton. The result shows that this method can reduce 9.92% maximum stress compared to the existing one. It is concluded that the proposed method in this paper is suitable for the light-weight design of the robot with time-varying pose.