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This paper aims to explore and review the potential of robotic rehabilitation as a treatment approach for Alzheimer’s disease (AD) and its impact on the health and quality of life of AD patients.

The present discourse endeavors to provide a comprehensive overview of extant scholarly inquiries that have examined the salience of inhibitory mechanisms vis-à-vis robotic interventions and their impact on patients with AD. Specifically, this review aims to explicate the contemporary state of affairs in this realm by furnishing a detailed explication of ongoing research endeavors. With the objective of elucidating the significance of inhibitory processes in robotic therapies for individuals with AD, this analysis offers a critical appraisal of extant literature that probes the intersection of cognitive mechanisms and assistive technologies. Through a meticulous analysis of diverse scholarly contributions, this review advances a nuanced understanding of the intricate interplay between inhibitory processes and robotic interventions in the context of AD.

According to the review papers, it appears that implementing robot-assisted rehabilitation can serve as a pragmatic and effective solution for enhancing the well-being and overall quality of life of patients and families engaged with AD. Besides, this new feature in the robotic area is anticipated to have a critical role in the success of this innovative approach.

Due to the nascent nature of this cutting-edge technology and the constrained configuration of the mechanized entity in question, further protracted analysis is imperative to ascertain the advantages and drawbacks of robotic rehabilitation vis-à-vis individuals afflicted with Alzheimer’s ailment.

The potential for robots to serve as indispensable assets in the provision of care for individuals afflicted with AD is significant; however, their efficacy and appropriateness for utilization by caregivers of AD patients must be subjected to further rigorous scrutiny.

This paper reviews the current robotic method and compares the current state of the art for the AD patient.

The purpose of this paper is to design an exoskeleton robot and present a corresponding rehabilitation training method for patients in different rehabilitation stages.

This paper presents a lightweight seven-degrees-of-freedom (DOF) cable-driven exoskeleton robot that is wearable and adjustable. After decoupling joint movement caused by a cable-driven mechanism, active rehabilitation training mode and passive rehabilitation training mode are proposed to improve the effect of rehabilitation training.

Simulations and experiments have been carried out, and the results validated the feasibility of the proposed mechanism and methods by a fine rehabilitative effect with different persons.

This paper designed a 7-DOF cable-driven exoskeleton robot that is suitable for patients of different body measurements and proposed the active rehabilitation training mode and passive rehabilitation training mode based on the cable-driven exoskeleton robot.

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.

The purpose of this paper is to introduce a new design for a finger and wrist rehabilitation robot. Furthermore, a fuzzy sliding mode controller has been designed to control the system.

Following an introduction regarding the hand rehabilitation, this paper discusses the conceptual and detailed design of a novel wrist and finger rehabilitation robot. The robot provides the possibility of rehabilitating each phalanx individually which is very important in the finger rehabilitation process. Moreover, due to the model uncertainties, disturbances and chattering in the system, a fuzzy sliding mode controller design method is proposed for the robot.

With the novel design for moving the DOFs of the system, the rehabilitation for the wrist and all phalanges of fingers is done with only two actuators which are combined in one device. These features make the system a good choice for home rehabilitation. To control the robot, a fuzzy sliding mode controller has been designed for the system. The fuzzy controller does not affect the coefficient of the sliding mode controller and uses the overall error of the system to make a control signal. Thus, the dependence of the controller to the model decreases and the system is more robust. The stability of the system is proved by the Lyapunov theorem.

The paper provides a novel design of a hand rehabilitation robot and a controller which is used to compensate the effects of the uncertain parameters and chattering phenomenon.

The purpose of this paper is to design and develop a new robotic device for the rehabilitation of the upper limbs. The authors are focusing on a new symmetrical robot which can be used to rehabilitate the right upper limb and the left upper limb. The robotic arm can be automatically extended or reduced depending on the measurements of the patient's arm. The main idea is to integrate electrical stimulation into motor rehabilitation by robot. The goal is to provide automatic electrical stimulation based on muscle status during the rehabilitation process.

The developed robotic arm can be automatically extended or reduced depending on the measurements of the patient's arm. The system merges two rehabilitation strategies: motor rehabilitation and electrical stimulation. The goal is to take the advantages of both approaches. Electrical stimulation is often used for building muscle through endurance, resistance and strength exercises. However, in the proposed approach the electrical stimulation is used for recovery, relaxation and pain relief. In addition, the device includes an electromyography (EMG) muscle sensor that records muscle activity in real time. The control architecture provides the ability to automatically activate the appropriate stimulation mode based on the acquired EMG signal. The system software provides two modes for stimulation activation: the manual preset mode and the EMG driven mode. The program ensures traceability and provides the ability to issue a patient status monitoring report.

The developed robotic device is symmetrical and reconfigurable. The presented rehabilitation system includes a muscle stimulator associated with the robot to improve the quality of the rehabilitation process. The integration of neuromuscular electrical stimulation into the physical rehabilitation process offers effective rehabilitation sessions for neuromuscular recovery of the upper limb. A laboratory-made stimulator is developed to generate three modes of stimulation: pain relief, massage and relaxation. Through the control software interface, the physiotherapist can set the exercise movement parameters, define the stimulation mode and record the patient training in real time.

There are certain constraints when applying the proposed method, such as the sensitivity of the acquired EMG signals. This involves the use of professional equipment and mainly the implementation of sophisticated algorithms for signal extraction.

Functional electrical stimulation and robot-based motor rehabilitation are the most important technologies applied in post-stroke rehabilitation. The main objective of integrating robots into the rehabilitation process is to compensate for the functions lost in people with physical disabilities. The stimulation technique can be used for recovery, relaxation and drainage and pain relief. In this context, the idea is to integrate electrical stimulation into motor rehabilitation based on a robot to obtain the advantages of the two approaches to further improve the rehabilitation process. The introduction of this type of robot also makes it possible to develop new exciting assistance devices.

The proposed design is symmetrical, reconfigurable and light, covering all the joints of the upper limbs and their movements. In addition, the developed platform is inexpensive and a portable solution based on open source hardware platforms which opens the way to more extensions and developments. Electrical stimulation is often used to improve motor function and restore loss of function. However, the main objective behind the proposed stimulation in this paper is to recover after effort. The novelty of the proposed solution is to integrate the electrical stimulation powered by EMG in robotic rehabilitation.

This paper aims to propose a bilateral robotic system for lower extremity hemiparesis rehabilitation. The hemiplegic patients can complete rehabilitation exercise voluntarily with the assistance of the robot. The reinforcement learning is included in the robot control system, enhancing the muscle activation of the impaired limbs (ILs) efficiently with ensuring the patients’ safety.

A bilateral leader–follower robotic system is constructed for lower extremity hemiparesis rehabilitation, where the leader robot interacts with the healthy limb (HL) and the follow robot is worn by the IL. The therapeutic training is transferred from the HL to the IL with the assistance of the robot, and the IL follows the motion trajectory prescribed by the HL, which is called the mirror therapy. The model reference adaptive impedance control is used for the leader robot, and the reinforcement learning controller is designed for the follower robot. The reinforcement learning aims to increase the muscle activation of the IL and ensure that its motion can be mastered by the HL for safety. An asynchronous algorithm is designed by improving experience relay to run in parallel on multiple robotic platforms to reduce learning time.

Through clinical tests, the lower extremity hemiplegic patients can rehabilitate with high efficiency using the robotic system. Also, the proposed scheme outperforms other state-of-the-art methods in tracking performance, muscle activation, learning efficiency and rehabilitation efficacy.

Using the aimed robotic system, the lower extremity hemiplegic patients with different movement abilities can obtain better rehabilitation efficacy.

The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training.

An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions.

Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode.

Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles.

The purpose of this paper is to implement a passive compliance training strategy for our newly designed 2-UPS/RRR parallel ankle rehabilitation robot (PARR) to enhance its rehabilitation training safety.

First, a kinematic analysis of the PARR is introduced, and the mechanism ensures that the rotation centre of the ankle joint complex (AJC) coincides with robot’s rotation centre. Then, a passive compliance training strategy based on admittance control is described in detail and is implemented on our PARR.

Experiments involving healthy subjects were conducted, and the performance of trajectory tracking was quantitatively evaluated, with the results showing excellent compliance and trajectory tracking accuracy, which can ensure that a secondary injury to the AJC during passive rehabilitation training is avoided. The influence of different admittance parameters was also simulated and analysed, which can contribute to the development of adaptive parameter adjustment research.

The paper can be used to improve the effectiveness of ankle rehabilitation, to alleviate manual therapy problems in terms of labour intensiveness, precision and subjectivity and to ensure safety and comfort during rehabilitation sessions.

The main purpose of this paper is to enhance the control performance of the robotic arm by the controller of fuzzy neural network (FNN).

The robot system has characters of high order, time delay, time variation and serious nonlinearity. The classical PID controller cannot achieve satisfactory performance in control of such a complex system. This paper combined the fuzzy control with neural networks and established the FNN controller and applied it in control of the robot.

The experimental results showed that the FNN controller had excellent performances in position control of the rehabilitation robotic arm such as fast response, small overshoot and small vibration.

This work is focused on the static FNN algorithm by updating the second and fifth layers of the membership functions. The performance can be improved further if the third layer (reasoning layer) can be updated online.

Based on a hierarchical structure of the FNN controller, this paper designed the FNN controller and applied it in control of the rehabilitation robot driven by pneumatic muscles.

This paper aims to provide an insight into the emerging use of robots in the rehabilitation of sufferers from strokes and other neurological impediments.

This considers research, clinical trials and commercial products. Following an introduction, it explains brain neuroplasticity and its role in rehabilitation and then discusses the use of robots in the restoration of upper limb and hand movement in stroke and traumatic injury patients. Robotic techniques aimed at restoring ambulatory ability are then discussed, followed by examples of the application of brain–computer interface technology to robotic rehabilitation. Finally, concluding comments are drawn.

Research has shown that robotic techniques can assist in the restoration of functionality to partially or fully paralysed upper and lower limbs. A growing number of commercial exoskeleton and end-effector robotic products have been launched which are augmenting conventional rehabilitation therapies. These systems frequently include interactive computer games and tasks which encourage repetitive use and allow patients to monitor their progress. Trials which combine robotics with brain–computer interface technology have yielded encouraging and unexpectedly positive results.

This provides details of the increasingly important role played by robots in the rehabilitation of patients suffering from strokes and other neurological disorders.