In clinical acupuncture practice, needle twirling (NT) and needle retention (NR) are strategically combined to achieve different therapeutic effects, highlighting the importance of distinguishing between different acupuncture states. Scalp EEG has been proven significantly relevant to brain activity and acupuncture stimulation. In this work, we designed an acupuncture paradigm to collect scalp EEG to study the differences in EEG changes during different acupuncture states. Since deep learning (DL) has been increasingly used in EEG analysis, we propose the Acupuncture Transformer Detector (ATD), a model based on Convolutional Neural Networks (CNN) and Transformer technology. ATD encapsulates the local and global features of EEG under the acupuncture states of Zusanli acupoint (ST-36) in an end-to-end classification framework. The experiment results from 28 healthy participants show that the proposed model can efficiently classify the EEG in different states, with an accuracy of $85.47\pm 0.73\%$. In this study, time-frequency analysis revealed that power changes were mainly confined to the delta frequency band under different acupuncture states. Brain topography revealed that ST-36 was activated primarily on the left frontal and parieto-occipital areas. This method provides new ideas for automatic recognition of acupuncture status from the perspective of DL, offering new solutions for standardizing acupuncture procedures.

A major challenge in electroencephalogram (EEG) signal classification is that the EEG signals recorded from different subjects are drawn from different distributions. When the unlabeled EEG data of the new subject arrive, called target domain, classifying them with a classifier trained on prerecorded EEG data of other subjects, called source domain, will greatly decrease the classification accuracy. Being able to use the classifiers trained on data of source domain to accurately classify the data of target domain could reduce the time of the calibration phase in the actual application of the brain-computer interface. This study considers an offline cross-subject classification scenario. We propose a novel manifold trial selection method, which reduces the distribution distance between the source and target domains by manifold transformation and domain adaptation. The proposed method provides a trial selection strategy to suppress negative transfer by removing some abnormal samples. The proposed method is applied to the motor imagery-based brain–computer interface and compared with several existing algorithms. Experimental results show that the proposed method outperforms the state-of-the-art methods.

MRCPs (movement related cortical potentials) are slow negative potentials observed in EEG preceding movement, which represents the processing of the cerebral cortex during planning and preparation. Clinical studies have shown that, rehabilitation therapy with the active participation of the central nervous system can rebuild related nerve function, restore the patient's neural plasticity and characterize the intention to move by means of electroencephalographic activity which can be used in rehabilitation protocols with patients' cortical activity taking an active role during the intervention. We describe our method framework including protocol, data processing and pattern recognition. Then the experimental results are analyzed. The tests were carried out with healthy people and achieved satisfactory performance both in trial and asynchronous detection. Our purpose is to apply to restoration of neural pathway of patients.

In this paper, the static stiffness of cable-driven wrist robotic rehabilitor (CDWRR) in its whole workspace is analyzed. A total stiffness matrix of this device consists of the exoskeleton stiffness model and the human arm stiffness model, which is obtained by using an equivalent stiffness model of exoskeleton and assuming a series of simplification of human arm. The analysis on stiffness was made with the MATLAB simulation results. Herein, the stiffness performance of the device during rehabilitation training was elucidated by the average stiffness index, stabilizability and relative stabilizability. Results show that the exoskeleton just has a great impact on the rotational stiffness. And the stiffness of cables plays a key role in the exoskeleton stiffness model and its relative stabilizability. Since the stiffness is associated with safety and comfort of rehabilitation training, it is essential to analyze the stiffness of rehabilitation device. Also, the analysis process of this paper can be referred by other similar cable-driven rehabilitors.

Parallel surface contact between the template and the substrate is very important in imprint lithography. In this paper, a novel force-decoupled compound parallel alignment stage is proposed for high-resolution imprint lithography. It mainly consists of a high-stiffness spherical air bearing (SAB) and a multi-degree-of-freedom (multi-DOF) flexure-based mechanism that functions for both the active and passive alignments. Apart from the function of the parallel alignment, the proposed stage can also endure a large imprinting force of more than 1000 N but does not cause any damage to the delicate components, which is mainly attributed to its force-decoupled characteristic. Through the stiffness modeling and finite element analysis (FEA), the performance is evaluated to satisfy the design requirement. Finally, experimental tests are conducted on the parallel alignment stage for the hot embossing process, and the grating patterns with linewidth of 2.5 μm are successfully transferred from the silicon template to the polymethy methacrylate (PMMA) substrate. This result demonstrates that the proposed stage can be used in the hot embossing process without degrading its alignment accuracy.

Control accuracy and consistent initial home posture is essential when we compare and analyze control algorithms. In general, in order to reduce the accumulative error of the controlling process or estimate the initial posture, the robot system need to return to its approximate home posture firstly. According to the structural characteristics of the cable-driven parallel robot, this paper describes a novel homing algorithm to a 3-DOF parallel spherical joint. Utilizing incremental encoders and limit switch to detect the home posture, the automatic homing function was realized in three steps by decoupling control to each axis of the spherical joint. Simulation was also performed to show the effectiveness of the homing algorithm.

Kinematic calibration is an effective method for improving the accuracy of the robot motion control. For the cable-driven robot, the flexibility of the cable makes the robot have good compliance and meet safety requirements; however, it brings a greater influence on the movement accuracy of the mechanism. Thus, kinematic calibration alone cannot accurately establish the error model. In view of the error analysis and flexibility compensation of the cable-driven robot, this paper analyzes the effect of flexible rope on kinematic movement accuracy by introducing a flexibility compensation weighting factor. A kind of comprehensive error analysis model of the cable-driven robot was established. By using kinematic calibration algorithm, it organically merges together the kinematic errors caused by the geometric parameters errors and that caused by flexibility of cable. With shoulder joint of the cable-driven humanoid-arm manipulator as the experimental object, the results showed that the algorithm has a better convergence, and it can effectively improve the accuracy of the robot motion control.

Based on the current research results of anthropomorphic-arm bionics and parallel manipulators, a motion control approach for a 7-DOF cable-driven manipulator is proposed. This paper introduces mechanical structure design of this anthropomorphic-arm first. For the inverse kinematics of the 7-DOF manipulator, a hybrid algorithm, based on gradient projection method and Paden-Kahan sub problems, is proposed to make the trajectory tracing of the manipulator in task space with high accuracy; a joint rate redistribution approach is also employed to optimize kinematic performance criterion and improve the fault-tolerance of the manipulator. The coupling cable lengths among the different joint modules, i.e., shoulder, elbow, and wrist joints, are analyzed in detail. Finally, to demonstrate the proposed algorithm, by utilizing ADAMS software, a straight-line trajectory motion simulation for the 7-DOF cable-driven manipulator is realized

A Web mapping system is described based on OpenGIS style layer specification and pipeline pattern. The patterns used to design the system are classified into three levels that are realized in architecting, designing and implementing of the system each. The whole system is constructed on the pipeline pattern. The pattern is divided into four filters to finish the selecting of spatial data, the generating of map feature style, and the rendering of map layer and presentation of map image. Map style data saved in a custom XML document is decoupled from map feature data that associate feature style generator filter. The system runs on Microsoft COM+ distribute implementing pattern.