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To improve the position tracking efficiency of the upper-limb rehabilitation robot for stroke hemiplegia patients, the optimization Learning rate of the membership function based on the fuzzy impedance controller of the rehabilitation robot is propose.

First, the impaired limb’s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using weighted least squares method based on recursive algorithm. Second, the fuzzy impedance control with the rule has been designed with the optimal impedance parameters. Finally, the membership function learning rate online optimization strategy based on Takagi-Sugeno (TS) fuzzy impedance model was proposed to improve the position tracking speed of fuzzy impedance control.

This method provides a solution for improving the membership function learning rate of the fuzzy impedance controller of the upper limb rehabilitation robot. Compared with traditional TS fuzzy impedance controller in position control, the improved TS fuzzy impedance controller has reduced the overshoot stability time by 0.025 s, and the position error caused by simulating the thrust interference of the impaired limb has been reduced by 8.4%. This fact is verified by simulation and test.

The TS fuzzy impedance controller based on membership function online optimization learning strategy can effectively optimize control parameters and improve the position tracking speed of upper limb rehabilitation robots. This controller improves the auxiliary rehabilitation efficiency of the upper limb rehabilitation robot and ensures the stability of auxiliary rehabilitation training.

Supernumerary robotic limbs (SRLs) are a new type of wearable robot, which improve the user’s operating and perceive the user’s environment by extra robotic limbs. There are some literature reviews about the SRLs’ key technology and development trend, but the design of SRLs has not been fully discussed and summarized. This paper aims to focus on the design of SRLs and provides a comprehensive review of the ontological structure design of SRLs.

In this paper, the related literature of SRLs is summarized and analyzed by VOSviewer. The structural features of different types of SRLs are extracted, and then discuss the design approach and characteristics of SRLs which are different from typical wearable robots.

The design concept of SRLs is different from the conventional wearable robots. SRLs have various reconfiguration and installed positions, and it will influence the safety and cooperativeness performance of SRLs.

This paper focuses on discussing the structural design of SRLs by literature review, and this review will help researchers understand the structural features of SRLs and key points of the ontological design of SRLs, which can be used as a reference for designing SRLs.

The purpose of this paper is to evaluate three categories of four-degrees of freedom (4-DOFs) upper limb rehabilitation exoskeleton mechanisms from the perspective of relative movement offsets between the upper limb and the exoskeleton, so as to provide reference for the selection of exoskeleton mechanism configurations.

According to the configuration synthesis and optimum principles of 4-DOFs upper limb exoskeleton mechanisms, three categories of exoskeletons compatible with upper limb were proposed. From the perspective of human exoskeleton closed chain, through reasonable decomposition and kinematic characteristics analysis of passive connective joints, the kinematic equations of three categories exoskeletons were established and inverse position solution method were addressed. Subsequently, three indexes, which can represent the relative movement offsets of human–exoskeleton were defined.

Based on the presented position solution and evaluation indexes, the joint displacements and relative movement offsets of the three exoskeletons during eating movement were compared, on which the kinematic characteristics were investigated. The results indicated that the second category of exoskeleton was more suitable for upper limb rehabilitation than the other two categories.

This paper has a certain reference value for the selection of the 4-DOFs upper extremity rehabilitation exoskeleton mechanism configurations. The selected exoskeleton can ensure the safety and comfort of stroke patients with upper limb dyskinesia during rehabilitation training.

This paper aims to develop a robotic mirror therapy system for lower limb rehabilitation, which is applicable for different patients with individual movement disability levels.

This paper puts forward a novel system that includes a four-degree-of-freedom sitting/lying lower limb rehabilitation robot and a wireless motion data acquisition system based on mirror therapy principle. The magnetorheological (MR) actuators are designed and manufactured, whose characteristics are detected theoretically and experimentally. The passive training control strategy is proposed, and the trajectory tracking experiments verify its feasibility. Also, the active training controller that is adapt to the human motor ability is designed and evaluated by the comparison experiments.

The MR actuators produce continuously variable and compliant torque for robotic joints by adjusting excitation current. The reference limb joint position data collected by the wireless motion data acquisition system can be used as the motion trajectory of the robot to drive the affected limb. The passive training strategy based on proportional-integral control proves to have great trajectory tracking performance through experiments. In the active training mode, by comparing the real-time parameters adjustment in two phases, it is certified that the proposed fuzzy-based regulated impedance controller can adjust assistance torque according to the motor ability of the affected limb.

The system developed in this paper fulfills the needs of robot-assisted mirror therapy for hemiplegic patients.

This paper aims to propose a new upper limb movement classification with two phases like pre-processing and classification. Investigation of human limb movements is a significant topic in biomedical engineering, particularly for treating patients. Usually, the limb movement is examined by analyzing the signals that occurred by the movements. However, only few attempts were made to explore the correlations among the movements that are recognized by the human brain.

The initial process is the pre-processing that is performed for detecting and removing noisy channels. The artifacts are marked by band-pass filtering that discovers the values below and above thresholds of 200 and –200 µV, correspondingly. It also discovers the trials with unusual joint probabilities, and the trials with unusual kurtosis are also determined using this method. After this, the pre-processed signals are subjected to a classification process, where the neural network (NN) model is used. The model finally classifies six movements like “elbow extension, elbow flexion, forearm pronation, forearm supination, hand open, and hand close,” respectively. To make the classification more accurate, this paper intends to optimize the weights of NN by a new hybrid algorithm known as bypass integrated jaya algorithm (BI-JA) that hybrids the concept of rider optimization algorithm (ROA) and JA. Finally, the performance of the proposed model is proved over other conventional models concerning certain measures like accuracy, sensitivity, specificity, and precision, false positive rate, false negative rate, false discovery rate, F₁-score and Matthews correlation coefficient.

From the analysis, the adopted BI-JA-NN model in terms of accuracy was high at 80th population size was 7.85%, 3.66%, 7.53%, 2.09% and 0.52% better than Levenberg–Marquardt (LM)-NN, firefly (FF)-NN, JA-NN, whale optimization algorithm (WOA)-NN and ROA-NN algorithms. On considering sensitivity, the proposed method was 2%, 0.2%, 5.01%, 0.29% and 0.3% better than LM-NN, FF-NN, JA-NN, WOA-NN and ROA-NN algorithms at 50th population size. Also, the specificity of the implemented BI-JA-NN model at 80th population size was 7.47%, 4%, 7.05%, 2.1% and 0.5% better than LM-NN, FF-NN, JA-NN, WOA-NN and ROA-NN algorithms. Thus, the betterment of the presented scheme was proved.

This paper adopts the latest optimization algorithm called BI-JA to introduce a new upper limb movement classification with two phases like pre-processing and classification. This is the first work that uses BI-JA based optimization for improving the upper limb movement detection using electroencephalography signals.

The purpose of this paper is to design a lower limb exoskeleton to enhance hemiplegic patient’s muscle strength and help the affected side return to normal gait after a long period of training.

A wire rope-driven exoskeleton that combines rigid bracket and flexible driven method was presented to assist the patients with rehabilitative walking training. By using three noncontact cameras, the patient’s gait was captured and the target trajectory of the affected side was analyzed. Meanwhile, a controlling strategy of the affected side, which mimics the gait of the healthy side, was developed to help hemiplegic patients with varying degrees of hemiplegic gait obtain personalized walking rehabilitation training.

The results show that the hemiplegic gait of hip excessive abduction and strephenopodia was prevented. After wearing the exoskeleton, the movement trajectories of both sides of the lower limb were approximately identical. Based on the controlling strategy, the exoskeleton can correct the impaired gait and provide assistance for patients during walking. The exoskeleton has great benefits in walking rehabilitation training for hemiplegic patients.

This work improves the efficiency of the patient’s individualized training in the room. The presented exoskeleton provides great benefits in walking rehabilitation training for hemiplegic patients.

Motion capture system (MoCap) has been used in measuring the human body segments in several applications including film special effects, health care, outer-space and under-water navigation systems, sea-water exploration pursuits, human machine interaction and learning software to help teachers of sign language. The purpose of this paper is to help the researchers to select specific MoCap system for various applications and the development of new algorithms related to upper limb motion.

This paper provides an overview of different sensors used in MoCap and techniques used for estimating human upper limb motion.

The existing MoCaps suffer from several issues depending on the type of MoCap used. These issues include drifting and placement of Inertial sensors, occlusion and jitters in Kinect, noise in electromyography signals and the requirement of a well-structured, calibrated environment and time-consuming task of placing markers in multiple camera systems.

This paper outlines the issues and challenges in MoCaps for measuring human upper limb motion and provides an overview on the techniques to overcome these issues and challenges.

The majority of three‐phase dynamic transformer models used in commercially available electric power system transient simulation programs offer only saturated three‐phase transformer models built from three single‐phase transformer models. This paper sets out to deal with the modelling and transient analysis of a saturated three‐limb core‐type transformer.

Three iron core models I‐III are given by the current‐dependent characteristics of flux linkages. In the first model, these characteristics are given by a set of piecewise linear functions, which include saturation. In the second model, the piecewise linear functions are replaced by the measured nonlinear characteristic. The more complex third model is given by a set of measured flux linkage characteristics.

The behaviour of transformers used in electric power applications depends considerably on the properties of magnetically nonlinear iron core. The best agreement between the calculated and measured results is obtained by use of the most complex iron core model III, which takes into account magnetic cross‐couplings between different limbs, caused by saturation.

Measurement of the current‐dependent flux linkage characteristics of the 0.4 kV, 3.5 kVA laboratory transformer requires corresponding excitation of windings by three independent linear amplifiers. Current‐dependent flux linkage characteristics of the larger power transformer can be determined either by similar measurement with linear amplifiers of an appropriate power or by extracting them from the calculated magnetic field, which is done by the finite element method.

A three‐phase dynamic transformer model with the obtained iron core model III is suitable for the numerical analysis of nonsymmetric transient states in power systems.

This paper presents a three‐phase dynamic transformer model with an original iron core model III, which accounts for magnetic cross‐couplings between different limbs, caused by saturation.

This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.

First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.

Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.

This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.