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The purpose of this review paper is to address the substantial challenges of the outdated exoskeletons used for rehabilitation and further study the current advancements in this field. The shortcomings and technological developments in sensing the input signals to enable the desired motions, actuation, control and training methods are explained for further improvements in exoskeleton research.

Search platforms such as Web of Science, IEEE, Scopus and PubMed were used to collect the literature. The total number of recent articles referred to in this review paper with relevant keywords is filtered to 143.

Exoskeletons are getting smarter often with the integration of various modern tools to enhance the effectiveness of rehabilitation. The recent applications of bio signal sensing for rehabilitation to perform user-desired actions promote the development of independent exoskeleton systems. The modern concepts of artificial intelligence and machine learning enable the implementation of brain–computer interfacing (BCI) and hybrid BCIs in exoskeletons. Likewise, novel actuation techniques are necessary to overcome the significant challenges seen in conventional exoskeletons, such as the high-power requirements, poor back drivability, bulkiness and low energy efficiency. Implementation of suitable controller algorithms facilitates the instantaneous correction of actuation signals for all joints to obtain the desired motion. Furthermore, applying the traditional rehabilitation training methods is monotonous and exhausting for the user and the trainer. The incorporation of games, virtual reality (VR) and augmented reality (AR) technologies in exoskeletons has made rehabilitation training far more effective in recent times. The combination of electroencephalogram and electromyography-based hybrid BCI is desirable for signal sensing and controlling the exoskeletons based on user intentions. The challenges faced with actuation can be resolved by developing advanced power sources with minimal size and weight, easy portability, lower cost and good energy storage capacity. Implementation of novel smart materials enables a colossal scope for actuation in future exoskeleton developments. Improved versions of sliding mode control reported in the literature are suitable for robust control of nonlinear exoskeleton models. Optimizing the controller parameters with the help of evolutionary algorithms is also an effective method for exoskeleton control. The experiments using VR/AR and games for rehabilitation training yielded promising results as the performance of patients improved substantially.

Robotic exoskeleton-based rehabilitation will help to reduce the fatigue of physiotherapists. Repeated and intention-based exercise will improve the recovery of the affected part at a faster pace. Improved rehabilitation training methods like VR/AR-based technologies help in motivating the subject.

The paper describes the recent methods for signal sensing, actuation, control and rehabilitation training approaches used in developing exoskeletons. All these areas are key elements in an exoskeleton where the review papers are published very limitedly. Therefore, this paper will stand as a guide for the researchers working in this domain.

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.

This systematic review, following preferred reporting items for systematic reviews and meta-analysis guidelines, rigorously investigates the emergent role of virtual reality (VR) technology in human movement training. The purpose of this study is to explore the effectiveness and evolution of VR in enhancing movement training experiences.

Acknowledging its pivotal role in diverse applications, such as sports and rehabilitation, human movement training is currently experiencing accelerated evolution, facilitated by the proliferation of wearable devices and mobile applications. This review conducted an exhaustive search across five different electronic databases, such as Web of Science, PubMed and ProQuest, resulting in the selection of 69 eligible articles published within the past five years. It also integrates 40 studies into a narrative summary, categorized based on the level of immersion offered by respective VR systems.

Enhanced immersion in VR potentially augments the effectiveness of movement training by engendering more realistic and captivating experiences for users. The immersive and interactive environments provided by VR technology enable tailored training experiences accompanied by precise, objective feedback. This review highlights the benefits of VR in human movement training and its potential to revolutionize the way training is conducted.

This systematic review contributes significantly to the existing literature by providing a comprehensive examination of the efficacy and evolution of VR in human movement training. By organizing the findings based on the level of immersion offered by VR systems, it provides valuable insights into the importance of immersion in enhancing training outcomes. In addition, this study identifies the need for future research focusing on the impacts of VR on learning and performance, as well as strategies to optimize its effectiveness and improve accessibility.

This study aims to offer a comprehensive exploration of the potential and challenges associated with sensor fusion-based virtual reality (VR) applications in the context of enhanced physical training. The main objective is to identify key advancements in sensor fusion technology, evaluate its application in VR systems and understand its impact on physical training.

The research initiates by providing context to the physical training environment in today’s technology-driven world, followed by an in-depth overview of VR. This overview includes a concise discussion on the advancements in sensor fusion technology and its application in VR systems for physical training. A systematic review of literature then follows, examining VR’s application in various facets of physical training: from exercise, skill development and technique enhancement to injury prevention, rehabilitation and psychological preparation.

Sensor fusion-based VR presents tangible advantages in the sphere of physical training, offering immersive experiences that could redefine traditional training methodologies. While the advantages are evident in domains such as exercise optimization, skill acquisition and mental preparation, challenges persist. The current research suggests there is a need for further studies to address these limitations to fully harness VR’s potential in physical training.

The integration of sensor fusion technology with VR in the domain of physical training remains a rapidly evolving field. Highlighting the advancements and challenges, this review makes a significant contribution by addressing gaps in knowledge and offering directions for future research.

Hand motor dysfunction has seriously reduced people’s quality of life. The purpose of this paper is to solve this problem; different soft exoskeleton robots have been developed because of their good application prospects in assistance. In this paper, a new soft hand exoskeleton is designed to help people conduct rehabilitation training.

The proposed soft exoskeleton is an under-actuated cable-driven mechanism, which optimizes the force transmission path and many local structures. Specifically, the path of force transmission is optimized and cables are wound around cam-shaped spools to prevent cables lose during fingers movement. Besides, a pre-tightening system is presented to adjust the preload force of the cable-tube. Moreover, a passive brake mechanism is proposed to prevent the cables from falling off the spools when the remote side is relaxed.

Finally, three control strategies are proposed to assist in rehabilitation training. Results show that the average correlation coefficient of trajectory tracking is 90.99% and this exoskeleton could provide steady clamping force up to 35 N, which could meet the demands of activities in daily living. Surface electromyography (sEMG)-based intention recognition method is presented to complete assistance and experiments are conducted to prove the effectiveness of the assisted grasping method by monitoring muscle activation, finger angle and interactive force.

However, the system should be further optimized in terms of hardware and control to reduce delays. In addition, more clinical trials should be conducted to evaluate the effect of the proposed rehabilitation strategies.

May improve the ability of hemiplegic patients to live independently.

A novel under-actuated soft hand exoskeleton structure is proposed, and an sEMG-based auxiliary grasping control strategy is presented to help hemiplegic patients conduct 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.

The purpose of this paper is to describe the development of a device to support rehabilitation of a patient's upper limb motion.

The device has five degrees of freedom by virtue of its link mechanism. It consists of Joints 1‐5. Apparatus for use in so‐called welfare applications, such as this device, must be safe, flexible, and lightweight. A pneumatic cylinder, arranged and integrated with the device, was used to operate it. The device has two rehabilitation modes corresponding to different rehabilitation contents. The first mode is the muscular recovery and movable region expansion mode (Mode A). The second mode is a practical function recovery mode (Mode B). A compliance control and a position control system are applied for those modes.

By arranging the pneumatic cylinder optimally, results show that the device has compact and wide operating range and compliance‐control performance for Mode A. Position‐control performance for Mode B was verified experimentally. Moreover, the paper evaluates the effectiveness of the device and its control system through electromyography, which confirms that the developed device can support a patient's rehabilitation training.

The device has a simple link mechanism and an attached pneumatic cylinder, thereby constituting a lightweight and compact mechanism. The device has two rehabilitation modes corresponding to different rehabilitation contents. Using the device, a patient can conduct muscular power recovery training, movable region expansion training, and upper limb practical function recovery training.

This paper aims to propose an innovative adaptive control method for lower-limb rehabilitation robots.

Despite carrying out various studies on the subject of rehabilitation robots, the flexibility and stability of the closed-loop control system is still a challenging problem. In the proposed method, surface electromyography (sEMG) and human force-based dual closed-loop control strategy is designed to adaptively control the rehabilitation robots. A motion analysis of human lower limbs is performed by using a wavelet neural network (WNN) to obtain the desired trajectory of patients. In the outer loop, the reference trajectory of the robot is modified by a variable impedance controller (VIC) on the basis of the sEMG and human force. Thenceforward, in the inner loop, a model reference adaptive controller with parameter updating laws based on the Lyapunov stability theory forces the rehabilitation robot to track the reference trajectory.

The experiment results confirm that the trajectory tracking error is efficiently decreased by the VIC and adaptively correct the reference trajectory synchronizing with the patients’ motion intention; the model reference controller is able to outstandingly force the rehabilitation robot to track the reference trajectory. The method proposed in this paper can better the functioning of the rehabilitation robot system and is expandable to other applications of the rehabilitation field.

The proposed approach is interesting for the design of an intelligent control of rehabilitation robots. The main contributions of this paper are: using a WNN to obtain the desired trajectory of patients based on sEMG signal, modifying the reference trajectory by the VIC and using model reference control to force rehabilitation robot to track the reference trajectory.

Network in Lewisham, thirteen employment, education and training projects in Lewisham, south‐east London, are working together to create a full vocational rehabilitation service for people with mental health problems. This is a complex process. This paper reviews the background, and the local development of the Network in Lewisham, and draws some early conclusions.

This paper aims to present a two-decade effort to improve team functioning and patient outcomes in inpatient stroke rehabilitation settings.

The principal improvement effort was conducted over a nine-year period in 50 Veterans Administration Hospitals in the USA. A comprehensive team-based model was developed and tested in a series of empirical studies. A leadership development intervention was used to improve team functioning, and a follow-up cluster-randomized trial documented patient outcome improvements associated with the leadership training.

Iterative team and leadership improvements are presented in summary form, and a set of practice-proven development observations are derived from the results. Details are also provided on the leadership training intervention that improved teamwork processes and resulted in improvements in patient outcomes that could be linked to the intervention itself.

The practice-proven development observations are connected to leadership development theory and applied in the form of suggestions to improve leadership development and teamwork in a broad array of medical treatment settings.

This paper includes suggestions for leadership improvement in medical treatment settings using interdisciplinary teams to meet the customized needs of the patient populations they serve.

The success of the team effectiveness model and the team-functioning domains provides a framework and best practice for other health care organizations seeking to improve teamwork effectiveness.