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The purpose of this paper is to develop a novel wearable rehabilitation robotic hand driven by Pneumatic Muscle‐Torsion Spring (PM‐TS) for finger therapy. PM has complex nonlinear dynamics, which makes PM modelling difficult. To realize high‐accurate tracking for the robotic hand, an Echo State Network (ESN)‐based PID adaptive controller is proposed, even though the plant model is unknown.

To drive a single joint of rehabilitation robotic hand, the paper proposes a new PM‐TS actuator comprising a Pneumatic Muscle (PM) and a Torsion Spring (TS). Based on the novel actuator, a wearable robotic hand is designed. By employing the model‐free approximation capability of ESN, the RLSESN based PID adaptive controller is presented for improving the trajectory tracking performance of the rehabilitation robotic hand. An ESN together with Recursive Least Square (RLS) is called a RLSESN, where the ESN output weight matrix is updated by the online RLS learning algorithm.

Practical experiments demonstrate the validity of the PM‐TS actuator and indicate that the performance of the RLSESN based PID adaptive controller is better than that of the conventional PID controller. In addition, they also verify the effectiveness of the proposed rehabilitation robotic hand.

A new PM‐TS actuator configuration that uses a PM and a torsion spring for bi‐directional movement of joint is presented. By utilizing the new PM‐TS actuator, a novel wearable rehabilitation robotic hand for finger therapy is designed. Based on the unknown plant model, the RLSESN_PID controller is proposed to attain satisfactory performance.

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.

Currently, rehabilitation medical care is expensive, requires a large number of rehabilitation therapist and which can only limit in the fixed location. In addition, there is a lack of research on the structure optimization and theoretical analysis of soft actuators for hand rehabilitation. In view of the problems above, this paper aims to propose a cheap, portable, wearable soft multiple joints rehabilitation glove.

First, this paper determined the hyperelastic constitutive model by material tensile test. Second, the soft actuator’s internal longitudinal section shape was optimized through the comparison of three diverse chamber structures. Meanwhile, the motion model of the soft actuator is established by the finite element model analysis method. Then, this paper established the constitutive model of the soft actuator according to the torque equilibrium equation and analyzed the relationship between the soft actuator’s bending angle and the input air pressure. This paper has verified that the theoretical model is correct through the soft actuator bending test. Finally, rehabilitation gloves were manufactured according to the model and the rehabilitation performance and grasping ability of gloves were verified through experiments.

The optimization results show that the internal semicircular cavity has better performance. Then, the actuator performance is better after adding the external arc structure and optimizing the physical dimension. The experimental results show that the trajectory of the actuator conforms to the mathematical model and rehabilitation gloves can meet the needs of rehabilitation treatment.

Rehabilitation gloves made of actuators can help patients with hand dysfunction in daily rehabilitation training. Then, it can also assist patients with some fine and complicated hand movements.

This paper proposes a new type of soft rehabilitation glove, which is composed of new soft actuators and adapting pieces. The new actuator is small enough to be fitted to the knuckle of the glove to move each joint of the finger.

Computer‐based systems for motor function rehabilitation have been around for more than a decade, with work done to help recovery of function in the lower limb (ankle, leg) as well as upper limb (hand and arm).

More recently there has been a trend towards the use of game‐based systems to deliver rehabilitation goals. The authors' interdisciplinary group has been working in the area of motor function recovery of the hand and arm (following stroke) for a number of years, using both high‐end virtual reality (VR) technology as well as low‐cost video capture technology.

Over this time it has become clear that there are many challenges in designing usable, effective game‐based systems for motor function rehabilitation.

This paper reflects on user experiences across the range of technologies developed by the group. It presents a summary review of the authors' systems and details the protocols and user evaluation instruments used. It then critically reflects on this work and reviews other recent advances in game usability and playability, leading to suggestions for how the user experience of games for rehabilitation may be improved in future work.

Hand impairment post-stroke is a very common and important rehabilitation goal for functional independence. Advanced therapy options such as an app. therapy provides repetitive training, which may be beneficial for improving fine motor function. This study aims to evaluate the effect of app-based therapy compared to conventional hand therapy in improving dexterity in individuals with stroke.

In total, 39 individuals within the first year of stroke with Brunnstrom stage of hand recovery IV to VI were randomly divided into three groups. All three groups received 60 min of therapy for 21 sessions over a period of 30 days. Group A received conventional hand therapy; Group B received app. therapy, while Group C received conventional therapy along with the app. therapy. All participants were assessed on the Nine-Hole Peg Test and Jebsen–Taylor Hand Function Test at the beginning and after completion of 21 sessions of intervention. Kruskal–Wallis (H) test and Wilcoxon test were used for statistical analysis.

All three groups improved on hand function post-treatment. However, Group C demonstrated significant improvement with 16%–58% increase in hand function performance on outcome measures (p < 0.05).

Findings of the present study demonstrate improvement in dexterity with the app. therapy and combination therapy, in comparison to conventional therapy alone in individuals with stroke.

This experimental study focuses the first time on a structured protocol using an enabling technology adjunct to conventional physical therapy to improve hand function in individuals with stroke, which opens up the further scope in Neurorehabilitation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2020-0144/

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.

The purpose of this paper is to review the challenges present in the development of hand exoskeletons powered by pneumatic artificial muscles. This paper also presents the development of a novel strain sensor and its application in a five‐fingered hand exoskeleton.

The issues of current hand exoskeletons powered by pneumatic artificial muscles are examined by studying the artificial muscles and the human hand anatomy. Traditional sensors are no longer suitable for applications in hand exoskeletons. A novel strain sensor was developed by depositing a conducting polymer called polypyrrole onto a natural rubber substrate through vapor phase polymerization and is used in the authors' five‐fingered hand exoskeleton.

The error of measurements from the polypyrrole strain sensor in controlling the actuation of pneumatic artificial muscles is within 1.5 mm. The small physical size and weight of the novel polypyrrole strain sensor also helped to keep the exoskeleton's profile (less than 20 mm) and total weight low (<1 kg).

The novel strain sensor allows the realization of hand exoskeletons that are lightweight, portable and low profile. This improves the comfort and practicality of hand exoskeletons to allow their usage outside the research environment.

Pediatric disorders, such as cerebral palsy and stroke, can result in thumb-in-palm deformity greatly limiting hand function. This not only limits children's ability to perform activities of daily living but also limits important motor skill development. Specifically, the isolated orthosis for thumb actuation (IOTA) is 2 degrees of freedom (DOF) thumb exoskeleton that can actuate the carpometacarpal (CMC) and metacarpophalangeal (MCP) joints through ranges of motion required for activities of daily living. The paper aims to discuss these issues.

IOTA consists of a lightweight hand-mounted mechanism that can be secured and aligned to individual wearers. The mechanism is actuated via flexible cables that connect to a portable control box. Embedded encoders and bend sensors monitor the 2 DOF of the thumb and flexion/extension of the wrist. A linear force characterization was performed to test the mechanical efficiency of the cable-drive transmission and the output torque at the exoskeletal CMC and MCP joints was measured.

Using this platform, a number of control modes can be implemented that will enable the device to be controlled by a patient to assist with opposition grasp and fine motor control. Linear force and torque studies showed a maximum efficiency of 44 percent, resulting in a torque of 2.39±1.06 in.-lbf and 0.69±0.31 in.-lbf at the CMC and MCP joints, respectively.

The authors envision this at-home device augmenting the current in-clinic and at-home therapy, enabling telerehabilitation protocols.

This paper presents the design and characterization of a novel device specifically designed for pediatric grasp telerehabilitation to facilitate improved functionality and somatosensory learning.

The purpose of this paper is to propose a variable impedance control method of finger exoskeleton for hand rehabilitation using the contact forces between the finger and the exoskeleton, making the output trajectory of finger exoskeleton comply with the natural flexion-extension (NFE) trajectory accurately and adaptively.

This paper presents a variable impedance control method based on fuzzy neural network (FNN). The impedance control system sets the contact forces and joint angles collected by sensors as input. Then it uses the offline-trained FNN system to acquire the impedance parameters in real time, thus realizing tracking the NFE trajectory. K-means clustering method is applied to construct FNN, which can obtain the number of fuzzy rules automatically.

The results of simulations and experiments both show that the finger exoskeleton has an accurate output trajectory and an adaptive performance on three subjects with different physiological parameters. The variable impedance control system can drive the finger exoskeleton to comply with the NFE trajectory accurately and adaptively using the continuously changing contact forces.

The finger is regarded as a part of the control system to get the contact forces between finger and exoskeleton, and the impedance parameters can be updated in real time to make the output trajectory comply with the NFE trajectory accurately and adaptively during the rehabilitation.

Many people suffer from injuries related to their hand. This research aims to focus on the improvement of the previously developed smart glove by using position and force control algorithms. The new smart glove may be used for both physiotherapy and assistance.

The proposed robot uses shape memory alloy (SMA) actuators coupled to an under-actuated tendon-driven mechanism. The proposed device, which is presented as a wearable glove attached to an actuation module, is capable of exerting extremely high forces to grasp objects in various hand configurations. The device’s performance is studied in physiotherapy and object manipulation tasks. In the physiotherapy mode, hand motion frequency is controlled, whereas the grasping force is controlled in the object manipulation mode. To simulate the proposed system behavior, the kinematic and dynamic equations of the proposed system have been derived.

The achieved results verify that the system is suitable to be used as part of a rehabilitation device in which it can flex and extend fingers with accurate trajectories and grasp objects efficiently. Specifically, it will be shown that using six SMA wires with the diameter of 0.25 mm, the proposed robot can provide 45 N gripping force for the patients.

The proposed robot uses SMA actuators and an under-actuated tendon-driven mechanism. The resulted robotic system, which is presented as a wearable glove attached to an actuation module, is capable of exerting extremely high force levels to grasp objects in various hand configurations. It is shown that the motion and exerted force of the robot may be controlled effectively in practice.