Daxing Zeng

Compared to traditional therapist-assisted rehabilitation training, ankle rehabilitation robots (ARRs) can offer a wider variety of active and passive training strategies for stroke and fracture patients. This enhances patient recovery outcomes and efficiency while reducing physical exertion for therapists, thereby conserving rehabilitation medical resources. This paper first summarizes the current research status on the equivalent models of the human ankle, aiming to better reproduce its motion functions in the development of ARRs. Secondly, it systematically retrieves and screens literature on the configuration design of ARRs from databases including Web of Science, Engineering Village, and IEEE Xplore. Thirdly, from the perspective of configuration design, the existing ARRs are divided into five categories: based on classical parallel mechanisms, with spherical pair or universal joint limb, based on remote center-of-motion mechanisms, human-machine compatible types, and other types. Finally, the characteristics of each ARR category are summarized, the current challenges and future prospects faced by ARRs are highlighted, and the limitations of this paper are also discussed. This paper systematically reviews the research status of configuration design of ARRs, pointing out that shortcomings still exist in areas such as human-machine compatibility. Future efforts should further integrate the biomechanical characteristics of the ankle to deepen innovative design of human-machine compatible configurations.

When applying continuous motion estimation (CME) model based on sEMG to human-robot system, it is inevitable to encounter scenarios in which the motions performed by the user are different from the motions in the training stage of the model. It has been demonstrated that the prediction accuracy of the currently effective approaches on untrained motions will be significantly reduced. Therefore, we proposed a novel CME method by introducing muscle synergy as feature to achieve better prediction accuracy on untrained motion tasks. Specifically, deep non-smooth NMF (Deep-nsNMF) was firstly introduced on synergy extraction to improve the efficiency of synergy decomposition. After obtaining the activation primitives from various training motions, we proposed a redundancy classification algorithm (RC) to identify shared and task-specific synergies, optimizing the original redundancy segmentation algorithm (RS). NARX neural network was set as the regression model for training. Finally, the model was tested on prediction tasks of eight untrained motions. The prediction accuracy of the proposed method was found to perform better than using time-domain feature as input of the network. Through Deep-nsNMF with RS, the highest accuracy reached 99.7%. Deep-nsNMF with RC performed similarly well and its stability was relatively higher among different motions and subjects. Limitation of the approach is that the problem of positive correlation between the prediction error and the absolute value of real angle remains to be further addressed. Generally, this research demonstrates the potential for CME models to perform well in complex scenarios, providing the feasibility of dedicating CME to real-world applications.

Percutaneous vertebroplasty (PVP) has become the mainstream method for the treatment of osteoporotic vertebral compression fractures (OVCF). Generally, surgeons manually plan the puncture path by themselves. This is time-consuming and laborious, which increases the working pressure of surgeons. Image processing algorithm is used to automatically segment contour of vertebral body, and plan the puncture path before operation. In order to obtain the contours of vertebral body and skin, binarization and contour extraction algorithm were performed. The connecting algorithm we proposed was used to connect the discontinuous contour of vertebral foramen. We determined the centerline of vertebral body by inscribed circle center of the contours of vertebral body and vertebral foramen, located the narrowest part of vertebral pedicle based on rotating segment algorithm and calculated medical parameters. The experimental results show that the segmentation success rate of algorithm is 95.92%, the average relative error of extracted parameters was no more than 4.18%. This method realizes automatic and accurate planning of puncture path before PVP, and it is of great significance to reduce the workload of surgeons, the number of intraoperative fluoroscopy and the harm of radiation to surgeons.

The Rubik’s Cube mechanism (RCM) is a kind of reconfigurable mechanism with multiple characteristics such as multiple configurations, variable topology, strong coupling, and reconfigurability. Crossover research on the RCM with mathematics, chemistry, cryptography, and other disciplines has led to important breakthroughs and progress. It is obvious that the invention and creation of a new RCM can provide important ideological inspiration and theoretical guidance for the accelerated iterative updating of Rubik’s Cube products and the expansion of their applications. This paper investigates the type synthesis method for a regular hexagonal prism (RHP) RCM (RHPRCM). Through analysis of the reconfigurable movement process of the RCM, two mechanism factors are abstracted, a type synthesis process for the RHPRCM is proposed, a symmetry layout method for the RCM’s revolute axis based on the RHP space polyhedron is proposed, and an analysis method for the intersection of the revolute pair contact surfaces (RPCSs) based on the adjacency matrix is proposed. Taking a revolute axis passing through the center of an RHP and having only one RPCS for each revolute axis as an example, an RHPRCM with different topological structures is synthesized. The relevant research in this paper can provide methodological guidance for the synthesis of other spatial RCMs.

In this letter, a human–machine compatible ankle rehabilitation robot (HMCARR) is proposed to help stroke patients with motion dysfunction recover their motor function. The HMCARR can make the human ankle center-of-rotation (H-CoR) and the ankle rehabilitation robot center-of-rotation (R-CoR) coincide in real-time. The overall idea is to analyze from the “human” perspective to the “human–machine” perspective, and then to the “machine” perspective. Firstly, from the “human” perspective: a spherical-pair-with-clearance equivalent human ankle model and a H-CoR estimation model are proposed, and the motion range of the H-CoR is analyzed. Secondly, from the “ human–machine” perspective: to obtain the mechanical design principles of the HMCARR, degree-of-freedom (DOF) and kinematic analyses of the human–machine closed chain model are conducted. Finally, from the “machine” perspective: DOF, kinematics, singularity, sensitivity, and workspace analysis are performed for the HMCARR based on a 3-RRCRR parallel mechanism, where R and C represent a revolute pair and a cylindrical pair, respectively. This study indicates that the mechanical design principles of the HMCARR include three requirements for the DOFs, kinematic independence, and position workspace. The results suggest that the HMCARR based on the 3-RRCRR parallel mechanism can achieve coincidence between H-CoR and R-CoR in real-time.