Nowadays, patients with mild and moderate upper limb paralysis caused by cerebral apoplexy are uncomfortable with autonomous rehabilitation. In this paper, according to the “rope + toothed belt” generalized rope drive design scheme, we design a utility model for a wearable upper limb rehabilitation robot with a tension mechanism. Owing to study of the human upper extremity anatomy, movement mechanisms, and the ranges of motion, it can determine the range of motion angles of the human arm joints, and design the shoulder joint, elbow joint, and wrist joint separately under the principle of ensuring the minimum driving torque. Then, the kinematics, workspace and dynamics analysis of each structure are performed. Finally, the control system of the rehabilitation robot is designed. The experimental results show that the structure is convenient to wear on the human body, and the robot’s freedom of movement matches well with the freedom of movement of the human body. It can effectively support and traction the front and rear arms of the affected limb, and accurately transmit the applied traction force to the upper limb of the joints. The rationality of the wearable upper limb rehabilitation robot design is verified, which can help patients achieve rehabilitation training and provide an effective rehabilitation equipment for patients with hemiplegia caused by stroke.

To help people with impairment of lower extremity movement regain the ability to stand and walk, and to enhance limb function, this study proposes an anthropomorphic design of an electrically driven, lower-limb exoskeleton rehabilitation robot. The angular range of the robot’s motion was determined according to the characteristics of the targeted lower-limb joints; the robot was given an active–passive anthropomorphic design with 12 degrees of freedom. The multi-degree-of-freedom hip exoskeleton, bionic artificial knee exoskeleton and passive rigid-flexible coupling ankle exoskeleton can assist patients in rehabilitation exercises with better wear comfort and exercise flexibility. A kinetic model of the seven-rod lower-limb exoskeleton rehabilitation robot was built, and data analysis of the dynamically captured motion trajectory was conducted. These provided a theoretical basis for gait planning and the control system of the lower-limb exoskeleton rehabilitation robot. The results show that the lower-limb exoskeleton rehabilitation robot system possesses sound wearing comfort and movement flexibility, and the degree of freedom of movement of the exoskeleton robot matches well with that of human movement. The robot can thus provide effective assistance to patients’ standing and walking rehabilitation training.

This research introduces the knee exoskeleton system that assists in knee joint rehabilitation, which is centered on human wearing comfort. According to the bionic principle, this paper proposes a bionic knee exoskeleton structure based on a cross four-bar linkage mechanism. The cross four-bar linkage mechanism is used to simulate the internal cruciate ligament of the human knee joint to realize the instantaneous rotation center movement of the knee joint. The motor drives the telescopic rod to simulate the movement of the exoskeleton of the knee joint by the thigh muscle of the human body. The auxiliary limit locking structure simulates the knee joint patella to prevent hyperextension of the exoskeleton of the knee joint. The particle swarm-based algorithm is used to optimize the size and position of the connecting rod of the cross four-bar linkage to follow the motion of the human knee joint better. The results show that the optimized and synthesized cross four-bar linkage mechanism has a small average error value, which can better reproduce the anthropomorphic motion characteristics of the human knee joint, achieve an ideal match between the motion form of the human knee joint and the exoskeleton, and improve coordination and adaptability with human joint movement. Through the wearer test, it is found that the structure has a variable instantaneous center of rotation trajectory. Under the condition of satisfying the flexion angle and torque of the human body, the knee joint movement could be simulated with the optimal trajectory to achieve the consistency with the human knee joint movement, so as to alleviate the discomfort of the wear movement of the patients in the auxiliary rehabilitation process, and it provides an advantage for the wear comfort of the human rehabilitation movement.

As a new service-oriented manufacturing paradigm, cloud manufacturing (CMfg) realizes the optimal allocation of resources in the product manufacturing process through the network. Task decomposition is a key problem of the CMfg system for resource scheduling. A high-quality task decomposition method can shorten product development time, reduce costs for resource service providers, and provide technical support for the application of CMfg. However, a cloud manufacturing system has to manage the allocation the correct amount of manufacturing resources, complex production processes, and highly dynamic production environments. At the same time, the tasks issued by service demanders are usually asymmetric and tightly coupled. We solve the complex task decomposition problem by using the traditional methods, that are hard to complete in CMfg. To overcome the shortcomings of CMfg, this paper proposed a task decomposition method based on the cloud platform. For achieving modular production, this approach creatively divides the product production process into four stages: design, manufacturing, transportation, and maintenance. Then a hybrid method, which combines with depth-first search algorithm, fast modular optimization algorithm, and artificial bee colony algorithm, is introduced. The method can obtain a multi-stage task optimization decomposition plan in CMfg. Simulation results demonstrate the proposed method can achieve complex task optimization decomposition in a CMfg environment.

In the process of rehabilitation treatment for stroke patients, rehabilitation evaluation is a significant part in rehabilitation medicine. Researchers intellectualized the evaluation of rehabilitation evaluation methods and proposed quantitative evaluation methods based on evaluation scales, without the clinical background of physiatrist. However, in clinical practice, the experience of physiatrist plays an important role in the rehabilitation evaluation of patients. Therefore, this paper designs a 5 degrees of freedom (DoFs) upper limb (UL) rehabilitation robot and proposes a rehabilitation evaluation model based on Belief Rule Base (BRB) which can add the expert knowledge of physiatrist to the rehabilitation evaluation. The motion data of stroke patients during active training are collected by the rehabilitation robot and signal collection system, and then the upper limb motor function of the patients is evaluated by the rehabilitation evaluation model. To verify the accuracy of the proposed method, Back Propagation Neural Network (BPNN) and Support Vector Machines (SVM) are used to evaluate. Comparative analysis shows that the BRB model has high accuracy and effectiveness among the three evaluation models. The results show that the rehabilitation evaluation model of stroke patients based on BRB could help physiatrists to evaluate the UL motor function of patients and master the rehabilitation status of stroke patients.

Due to insufficient muscle strength, a novel rehabilitation mechanism of the shoulder joint exoskeleton is proposed and analyzed based on gear and rack transmission, which solves the problem that stroke patients need repetitive exercise rehabilitation training. First, the range of motion angle of the shoulder joint is determined, and the mechanical structure of shoulder joint rehabilitation is designed. The arc rack is engaged with the output shaft gear of the reducer, and the servo motor is utilized as the driving force to realize the internal/external rotation movement of the shoulder joint. The motor connects the horizontal connecting rod and the rotating rod through the cross roller bearing to realize the abduction/adduction and flexion/extension movement of the shoulder joint. Second, the kinematics and dynamics of the shoulder joint exoskeleton are analyzed, investigated, and verified for the rehabilitation mechanism. The simulation analysis of the mechanism is completed in the virtual prototype. The changes in the joint angle and end trajectory and joint moment with time and angular velocity under the given working conditions are obtained, which directly reflect the movement of each joint of the rehabilitation mechanism. Finally, aiming at the nonlinear disturbance in the trajectory tracking control of the rehabilitation mechanism of the shoulder joint exoskeleton, the trajectory tracking approach of the mechanism is achieved aided with the closed-loop PD iterative learning control method. The results demonstrate that the actual trajectories are in good agreement with the desired trajectories, which can achieve effective tracking and improve the control quality of the system.