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.

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.

This paper presents a bionic cable-driven mechanism to simulate the motion of human wrist which is suitable for human forearm–wrist rehabilitation. It fulfills workspace of the human forearm–wrist and it can train the joint in active and passive. With three degrees of freedom, it completes the supination/pronation of the forearm, the radial/ulnar deviation, and flexion/extension of the wrist. In addition to the movement of single degree of freedom of the forearm–wrist, it can also complete circumduction of the wrist. The mechanism consists of revolving platform, parallel mechanism, supporting mechanism, and movable table. Especially, in the parallel mechanism, a spring is added between the fixed and moving platform, and the moving platform is designed in the shape of a hemispherical shell. Utilizing the resilient properties of the extension spring and the support of the hemispherical shell, the problem of slack in the cable is solved in this mechanism. Since the spring is a passive component and cannot be calculated directly, a method combining kinematics and statics is proposed to calculate the relationship between the pose of the moving platform and the cable. Meanwhile, the kinematics, statics, and workspace solution of the mechanism are derived. Then, the simulation results demonstrate the accurateness and feasibility of the inverse kinematics and workspace derivation of the mechanism. Finally, the experiments are analyzed to verify the mechanism suitable for forearm–wrist rehabilitation tasks.

This study addresses gait reference trajectory tracking control in a 13-degree-of-freedom lower-limb rehabilitation robot, where patients exhibit nonlinear perturbations in lower-limb muscle groups and gait irregularities during exoskeleton-assisted walking. We propose a novel control strategy integrating iterative learning with RBF neural network-based sliding mode control, featuring a single hidden-layer pre-feedback architecture. The RBF neural network effectively approximates uncertainties arising from lower-limb muscle perturbations, while adaptive regulation through parameter simplification ensures precise torque tracking at each joint, meeting real-time rehabilitation requirements. MATLAB 2021 simulations demonstrate the proposed algorithm’s superior trajectory tracking performance compared to conventional sliding mode control, effectively eliminating control chattering. Experimental results show maximum angular errors of 1.77° (hip flexion/extension), 1.87° (knee flexion/extension), and 0.72° (ankle dorsiflexion/plantarflexion). The integrated motion capture system enables the development of patient-specific skeletal muscle models and optimized gait trajectories, ensuring both training efficacy and safety for spasticity patients.

This paper presents an assisted upper limb rehabilitation robot (ULRR) for patients who have experienced stroke who are in the middle and late stages of rehabilitation and have certain muscle strength. The ULRR can complete adduction and abduction motion of the shoulder joint (SJ) in the frontal plane in one step, which can save time and improve the efficiency of rehabilitation training. Based on the principles of ergonomics and rehabilitation medicine, the freedom degree of the upper limb and the motion range of each joint are determined, and the structure of the shoulder, the elbow, and the wrist joint of ULRR are designed. The kinematics model of the robot is established, and the kinematics equations are derived. Meanwhile, the simulation analysis and the workspace analysis of the robot are carried out, and the different movement forms of SJ adduction and abduction are compared and analyzed. Then, the trajectory of the robot is planned to complete the act of drinking water. Finally, an experimental platform is built to complete the ULRR to help participants complete the experiments of drinking water and active training. The experiments verify that the robot is suitable for rehabilitation tasks.