The individual difference of motor imagery ability evoked by visual stimulus and its personality manifestation.

Motor imagery has been commonly studied as a means of motor rehabilitation but, the individual differences limit its practical application. Visually evoked motor imagery has been widely highlighted by researchers because of its vivid stimulus. However, this modality is still not applicable to all persons. In this study, we studied the different performances of the visually evoked motor imagery between subjects and tried to explore the personality manifestation which can result in this performance. We found that conscientiousness and openness have negative connections with the performance of visually evoked motor imagery. To compare with spontaneous motor imagery, the visually evoked motor imagery reflects less personality difference between subjects with good and bad performances on motor imagery. This indicate that visually stimulus may increase the pervasive application of motor imagery. This study may provide benefits to predict the rehabilitation effect and to rapidly select the suitable motor rehabilitation methods.

Technologies evolution in robot-assisted fracture reduction systems: a comprehensive review.

Background: Robot-assisted fracture reduction systems can potentially reduce the risk of infection and improve outcomes, leading to significant health and economic benefits. However, these systems are still in the laboratory stage and not yet ready for commercialization due to unresolved difficulties. While previous reviews have focused on individual technologies, system composition, and surgical stages, a comprehensive review is necessary to assist future scholars in selecting appropriate research directions for clinical use. Methods: A literature review using Google Scholar identified articles on robot-assisted fracture reduction systems. A comprehensive search yielded 17,800, 18,100, and 16,700 results for "fracture reduction," "computer-assisted orthopedic surgery," and "robot-assisted fracture reduction," respectively. Approximately 340 articles were selected, and 90 highly relevant articles were chosen for further reading after reviewing the abstracts. Results and Conclusion: Robot-assisted fracture reduction systems offer several benefits, including improved reduction accuracy, reduced physical work and radiation exposure, enhanced preoperative planning and intraoperative visualization, and shortened learning curve for skill acquisition. In the future, these systems will become integrated and practical, with automatic preoperative planning and high intraoperative safety.

An Integrated Method of Biomechanics Modeling for Pelvic Bone and Surrounding Soft Tissues.

The pelvis and its surrounding soft tissues create a complicated mechanical environment that greatly affects the success of fixing broken pelvic bones with surgical navigation systems and/or surgical robots. However, the modeling of the pelvic structure with the more complex surrounding soft tissues has not been considered in the current literature. The study developed an integrated finite element model of the pelvis, which includes bone and surrounding soft tissues, and verified it through experiments. Results from the experiments showed that including soft tissue in the model reduced stress and strain on the pelvis compared to when it was not included. The stress and strain distribution during pelvic loading was similar to what is typically seen in research studies and more accurate in modeling the pelvis. Additionally, the correlation with the experimental results from the predecessor's study was strong (R2 = 0.9627). The results suggest that the integrated model established in this study, which includes surrounding soft tissues, can enhance the comprehension of the complex biomechanics of the pelvis and potentially advance clinical interventions and treatments for pelvic injuries.

Dual enzyme-like Co-FeSe2 nanoflowers with GSH degradation capability for NIR II-enhanced catalytic tumor therapy.

Nanozymes mediated catalytic therapy can produce toxic reactive oxygen species (ROS) and destroy the metabolic balance of tumor cells, providing a new direction for cancer treatment. However, the catalytic efficiency of a single nanozyme is limited by the complexity of the tumor microenvironment (hypoxia, GSH overexpression, etc.). In order to overcome these problems, we designed flower-like Co-doped FeSe2 (Co-FeSe2) nanozymes by a simple wet chemistry method. Co-FeSe2 nanozymes not only exhibit high POD and OXD-mimicking activities for facile kinetics, but also effectively consume over-expressed glutathione (GSH), inhibiting the consumption of generated ROS and destroying the metabolic balance of the tumor microenvironment. These catalytic reactions trigger cell death through apoptosis and ferroptosis dual pathways. More importantly, under the NIR II laser irradiation, the catalytic activities of Co-FeSe2 nanozymes are boosted, confirming the photothermal and catalytic synergistic tumor therapy. This study takes advantage of self-cascading engineering that offers new ideas for designing efficient redox nanozymes and promoting their clinical translation.

An Auxiliary Synthesis Framework for Enhancing EEG-Based Classification With Limited Data.

While deep learning algorithms significantly improves the decoding performance of brain-computer interface (BCI) based on electroencephalogram (EEG) signals, the performance relies on a large number of high-resolution data for training. However, collecting sufficient usable EEG data is difficult due to the heavy burden on the subjects and the high experimental cost. To overcome this data insufficiency, a novel auxiliary synthesis framework is first introduced in this paper, which composes of a pre-trained auxiliary decoding model and a generative model. The framework learns the latent feature distributions of real data and uses Gaussian noise to synthesize artificial data. The experimental evaluation reveals that the proposed method effectively preserves the time-frequency-spatial features of the real data and enhances the classification performance of the model using limited training data and is easy to implement, which outperforms the common data augmentation methods. The average accuracy of the decoding model designed in this work is improved by (4.72±0.98)% on the BCI competition IV 2a dataset. Furthermore, the framework is applicable to other deep learning-based decoders. The finding provides a novel way to generate artificial signals for enhancing classification performance when there are insufficient data, thus reducing data acquisition consuming in the BCI field.

An automatic extraction method on medical feature points based on PointNet++ for robot-assisted knee arthroplasty.

BACKGROUND: 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. METHODS: 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++. RESULTS: 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. CONCLUSION: 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.

A Novel Two-Stage Refine Filtering Method for EEG-Based Motor Imagery Classification.

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.

Stereotactic technology for 3D bioprinting: from the perspective of robot mechanism.

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.

Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery.

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.

A topology optimization method of robot lightweight design based on the finite element model of assembly and its applications.

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.

Detecting self-paced walking intention based on fNIRS technology for the development of BCI.

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.

A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification.

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.

Intelligent Control for Human-Robot Cooperation in Orthopedics Surgery.

Cooperation between surgeon and robot is one of the key technologies that limit the robot to be widely used in orthopedic clinics. In this study, the evolution of human-robot cooperation methods and the control strategies for typical human-robot cooperation in robot-assisted orthopedics surgery were reviewed at first. Then an intelligent admittance control method, which combines the fuzzy model reference learning control with the virtual constraint control, is proposed to solve the requirements of intuitive human-robot interaction during orthopedics surgery. That is, a variable damping parameter model of the admittance control based on fuzzy model learning control algorithm is introduced to make the robot move freely by using the reference model of surgeon's motion equation with the minimum jerk trajectory. And the virtual constraint control method based on the principle of virtual fixture is adopted to make the robot move within the pre-defined area so as to perform more safe surgery. The basic principle and its realization of this intelligent control method are described in details. At last, a test platform is built based on our designed 6 DOF articulated robot. Experiments of safety and precision on acrylic model with this method show that the robot has the ability of better intuitive interaction and the high precision. And the pilot experiment of bone tumor resection on sawbone model shows the effectiveness of this method.

A novel passive/active hybrid robot for orthopaedic trauma surgery.

BACKGROUND: Image guided navigation systems (IGNS) have been implemented successfully in orthopaedic trauma surgery procedures because of their ability to help surgeons position and orient hand-held drills at optimal entry points. However, current IGNS cannot prevent drilling tools or instruments from slipping or deviating from the planned trajectory during the drilling process. A method is therefore needed to overcome such problems. METHODS: A novel passive/active hybrid robot (the HybriDot) for positioning and supporting surgical tools and instruments while drilling and/or cutting in orthopaedic trauma surgery is presented in this paper. This new robot, consisting of a circular prismatic joint and five passive/active back-drivable joints, is designed to fulfill clinical needs. In this paper, a system configuration and three operational modes are introduced and analyzed. Workspace and layout in the operating theatre (OT) are also analyzed in order to validate the structure design. Finally, experiments to evaluate the feasibility of the robot system are described. RESULTS: Analysis, simulation, and experimental results show that the novel structure of the robot can provide an appropriate workspace without risk of collision within OT environments during operation. The back-drivable joint mechanism can provide surgeons with more safety and flexibility in operational modes. The mean square value of the positional accuracy of this robot is 0.811 mm, with a standard deviation (SD) of 0.361 mm; the orientation is accurate to within 2.186º, with a SD of 0.932º. Trials on actual patients undergoing surgery for distal locking of intramedullary nails were successfully conducted in one pass using the robot. CONCLUSION: This robot has the advantages of having an appropriate workspace, being well designed for human-robot cooperation, and having high accuracy, sufficient rigidity, and easy deployability within the OT for use in common orthopaedic trauma surgery tasks such as screw fixation and drilling assistance.