The motor imagery electroencephalography (MI-EEG) reflects the subjective motor intention, which has received increasing attention in rehabilitation. How to extract the features of MI-EEG accurately and quickly is the key to its successful application. Based on the analysis and comparison of the existing feature extraction algorithms, a feature extraction method based on principal component analysis (PCA) and deep belief networks (DBN) is proposed, namely PCA-DBN. Firstly, the second-order moment is used to analyze the time-domain of MI-EEG, select the effective time interval. Secondly, PCA is used to analyze the selected time-domain interval and obtain the principal component feature points. Then, feature points are imported into DBN to realize the final feature extraction. Finally, use the softmax classifier to complete task classification. Perform algorithm validation on the BCI Competition II Data set III and BCI Competition IV Data sets 2b, classification accuracies are 96.25% and 91.71%, kappa values are 0.925 and 0.8342. The paired-sample t-test with FDR correction is carried out on the verification results, and the comparison with some better classification algorithms shows that the algorithm has better performance. In the end, this method is used to extract the features of laboratory data, the optimal classification accuracy is 97.69% and kappa value is 0.9538, the validity of the method is further verified.

To solve the problem of optimal wavelet basis function selection in feature extraction of motor imagery electroencephalogram (MI-EEG) by wavelet packet transformation, based on the analysis of wavelet packet transformation and wavelet basis parameters, combine with the characteristics of MI-EEG, the characteristics of wavelet basis function suitable for feature extraction of MI-EEG are summarized. On the basis of processing and analyzing of two BCI competition data sets, signal to noise ratio (SNR), root mean squared error (RMSE), classification accuracy, and kappa value are introduced as evaluation criteria for feature extraction effect, it is concluded that the rbio2.2 wavelet basis function is the optimal wavelet basis function for feature extraction of MI-EEG. Finally, the MI-EEG collected in the laboratory is processed and analyzed, further proving that the rbio2.2 wavelet basis function is the optimal wavelet basis function for feature extraction of MI-EEG.

With the increasingly more extensive application of the medical surgical robot in the clinic, higher requirements have been put forward for medical robots. The bone-attached robot, a popular orthopedic robot in recent years, has a tendency of miniaturization and refinement. Thus, a bone-attached parallel manipulator (PM) based on 6-UCU (universal-cylindrical-universal) configuration is proposed, which is characterized by small volume, compact structure, high precision and six-dimensional force feedback. To optimize the structure and make it more compact, the workspace of the 6-UCU PM is analyzed based on the analysis of three kinds of constraint, and workspace model is established through spherical coordinate search method. This study also analyzes the influence of structural parameters on workspace, which may contribute to improving the efficiency of design and ensuring small-sized robots possess relatively large workspace. Moreover, to improve the motion accuracy, an error modeling method is developed based on the structure of 6-UCU PMs. According to this established error model, the output pose error curves are drawn using MATLAB software when the structure parameters change, and the influence of the structure and pose parameters change on the output pose error of PMs is analyzed. The proposed research provides the instruction to design and analysis of small PMs such as bone-attached robots.

The design and analysis of continuum robots have consistently been a prominent research focus in the field of mechanics. However, portable continuum robots with minimal spatial occupancy, which have great potential for applications such as search and rescue, are scarcely available. This paper presents a novel helical-coiled multi-segment flexible continuum robot featuring helical deployment and compact design, with an integrated framework for structural design, kinematic modeling, and experimental validation. The design of the helical-coiled multi-segment flexible continuum robot for unstructured environment detection, including a flexible body, an actuation module, a feed module, and a sensing module, is presented systematically. Kinematic models of both single- and multi-segment continuum robots were established based on the constant curvature model to analyze the parameter mapping relationship from the end-effector position and orientation to the driving inputs. Furthermore, the feedforward motion of the robot was examined, and an uncoiling strategy based on S-curve compensation was employed to complete the kinematic analysis. Finally, the accuracy of the kinematic model considering the active uncoiling feed motion was validated through experimental analysis, demonstrating the motion characteristics of the continuum robot. Altogether, this study provides a framework for the design and analysis of helical-coiled continuum robots.

In this paper, a kind of hybrid kinematics chain with variable constraints is constructed by using a 6R spatial bifurcated chain and 4-DOF serial limbs, by using them, a 3-DOF Reconfigurable Parallel Mechanisms (RPM) was constructed, which has four motion modes: 3T, 2T1R, 2R1T, and 3R motion modes. Using screw theory, the change of wrench system in the hybrid kinematics chain caused by the reconfiguration of the 6R bifurcated chain is analyzed, which affects the wrench system of the moving platform (MP), and thus causes the reconfiguration of the parallel mechanism (PM). The triangular pyramid fixed to the MP was found as auxiliary geometry and used to intuitively describe the constraint relationship between joint axes in limbs. The latter was used to establish a unified forward kinematics model of the RPM in four motion modes. The general forward kinematics solution procedure for this type of RPM is given. The overall Jacobian matrix was obtained by screw theory for singularity analysis. The condition number of the Jacobian matrix measures the degree to which the mechanism is close to the singular configuration. Based on the Monte Carlo simulation, point cloud images representing the workspace of RPM were obtained.