Posts Tagged Functional electrical stimulation

[ARTICLE] Cooperative Control for A Hybrid Rehabilitation System Combining Functional Electrical Stimulation and Robotic Exoskeleton – Full Text

Functional electrical stimulation (FES) and robotic exoskeletons are two important technologies widely used for physical rehabilitation of paraplegic patients. We developed a hybrid rehabilitation system (FEXO Knee) that combined FES and an exoskeleton for swinging movement control of human knee joints. This study proposed a novel cooperative control strategy, which could realize arbitrary distribution of torque generated by FES and exoskeleton, and guarantee harmonic movements. The cooperative control adopted feedfoward control for FES and feedback control for exoskeleton. A parameter regulator was designed to update key parameters in real time to coordinate FES controller and exoskeleton controller. Two muscle groups (quadriceps and hamstrings) were stimulated to generate active torque for knee joint in synchronization with torque compensation from exoskeleton. The knee joint angle and the interactive torque between exoskeleton and shank were used as feedback signals for the control system. Central pattern generator (CPG) was adopted that acted as a phase predictor to deal with phase confliction of motor patterns, and realized synchronization between the two different bodies (shank and exoskeleton). Experimental evaluation of the hybrid FES-exoskeleton system was conducted on five healthy subjects and four paraplegic patients. Experimental results and statistical analysis showed good control performance of the cooperative control on torque distribution, trajectory tracking, and phase synchronization.

1. Introduction

Neurologic injuries such as stroke and spinal cord injury may cause paresis in patients and give rise to movement disability. Physical rehabilitation is highly necessary for paralyzed individuals to restore mobility of extremities. Functional electrical stimulation (FES) and robotic exoskeletons are two important technologies used widely in extremity rehabilitation.

Many FES systems have been developed by using either surface or implanted electrodes in the past decades (Popovic et al., 2001). As a neuro-rehabilitation approach that excites and activates muscles directly, FES can provide not only functional training but also therapeutic benefits to paralyzed patients. Although some advances in closed-loop control and multichannel selection of muscles have achieved complex stimulation, it is still a complicated and tough problem of controlling FES to assist paralyzed individuals to move in a natural manner, mainly due to the nonlinearity and time variability of human musculoskeletal system (Zhang et al., 2007Lynch and Popovic, 2008). The pathological muscle conditions and the poor controllability of FES result in insufficient joint torque to provide limbs movement and body support for patients (del Ama et al., 2012Ha et al., 2012Quintero et al., 2012). In addition, muscle fatigue is often induced under continuous electrical stimulation. In a word, these problems mentioned severely hinder the widespread usage of FES from becoming a popular treatment option.

Robotic exoskeleton is an alternative technology of extremity rehabilitation for paraplegic patients, and lower limb exoskeletons are designed to accomplish neuro-rehabilitation and replace the physical gait training effort of therapists (Dollar and Herr, 2008). The well-known representatives in the application of motor rehabilitation for lower limbs are Lokomat (Hocoma, Switzerland) (Colombo et al., 2000), LOPES (Veneman et al., 2007), POGO and PAM (Reinkensmeyer et al., 2006), ALEX (Banala et al., 2009), etc. The popular exoskeletons usually use electric actuators, hydraulic actuators, or pneumatic actuators (Fan and Yin, 2013Vitiello et al., 2013). In comparison with FES, the therapeutic effect of robotic rehabilitation is limited, because it can merely provide assistive torque to limbs, the muscles are not stimulated actively, which are passively contracted or stretched. Therefore, it is an urgent demand to combine FES with exoskeletons, merging as hybrid rehabilitation systems that bring about not only functional but also physiological benefits to patients.

There is an increasing interest in developing hybrid rehabilitation systems, taking the advantages of FES and exoskeleton, and overcoming the limitations in separate application (To et al., 2008del Ama et al., 2012). In general, there are two kinds of such hybrid rehabilitation systems, i.e., combination of FES and powerless (passive) orthoses, or combination of FES and powered (active) exoskeletons. The controlled-brake orthosis (CBO) developed by Goldfarb and Durfee (1996) used joint brakes to control the body movement generated by FES. An obvious deficiency of orthoses is the inability to generate active torque for joints. Compared with orthoses, powered exoskeletons using mechanical actuators can compensate insufficient torque generated by FES. Recently, some achievements in hybrid FES-exoskeleton systems have been made, such as WalkTrainer (Stauffer et al., 2009), Vanderbilt Exoskeleton (Ha et al., 2012), Kinesis (del Ama et al., 2014), iLeg (Chen et al., 2014) and so on. In WalkTrainer system, Stauffer et al. (2009) developed closed-loop control of FES that modulated muscle stimulation to minimize the interaction force between the wearer and the exoskeleton, or modulated the desired torques as a function of the gait cycle. That system did not take account for muscle fatigue compensation as the exoskeleton was not actively involved. In order to accomplish cooperative control of FES with the Vanderbilt Exoskeleton during walking, Ha et al. (2016) proposed a two-loop controller, where motor control loop and muscle control loop co-existed. In that manner, the motor control loop used joint angle feedback to control the output of the joint motor to track the desired joint trajectories, while the muscle control loop utilized joint torque profiles from previous steps to regulate the muscle stimulation for the subsequent step to minimize the motor torque contribution required for joint angle trajectory tracking. del Ama et al. (2014)proposed cooperative control to balance the effort between muscle stimulation and exoskeleton in hybrid system (Kinesis), which sought to minimize the interaction torque and realized hybrid ambulatory gait rehabilitation. The torque-time integral generated by FES was measured to estimate muscle fatigue and a learning method was used to modulate the stimulation strength so as to compensate the torque loss. Alibeji N. A. et al. (2015) and Alibeji et al. (2017) developed an adaptive control method inspired by muscle synergy to compensate for actuator redundancy and FES-induced muscle fatigue in a hybrid FES-exoskeleton system, which showed ability to coordinate FES of quadriceps and hamstrings muscles and electric motors at the hip joint and knee joint of the exoskeleton. Chen et al. (2014) designed an FES-assisted control strategy for a hybrid lower-limb rehabilitation system (iLeg), where active FES control was achieved via a combination of neural network based feedforward control and PD feedback control to realize torque control, and meanwhile impedance control was adopted for exoskeleton control. Tu et al. (2017) combined FES with exoskeleton to accomplish gait rehabilitation in a different way, where FES and exoskeleton made effect on different joints separately, i.e., exoskeleton was applied on hip and knee joints, and FES was applied on ankle joint. A sliding control algorithm called chattering mitigation robust variable control (CRVC) was used for cooperative control in that hybrid system.

This study aims to accomplish harmonic and elegant control between FES and exoskeleton and explore their combined function on single-joint movement. Different from previous works, the active roles of FES and exoskeleton can be set freely here, i.e., the contribution of FES and exoskeleton can be distributed arbitrarily under different circumstances with specified requirements. Meanwhile, the synchronization problem of different drivers (motor vs. muscle) is well solved. It is well known knee joints play very important roles in lower limb locomotion, and knee joint control is a benchmark in previous literature (Chang et al., 1997Ferrarin et al., 2001Hunt et al., 2004Sharma et al., 2009Alibeji N. et al., 2015). Therefore, a hybrid rehabilitation system called FEXO Knee is developed in this work, which combines FES with a knee exoskeleton. A novelty of the system is the interactive force can be measured, which can help realize the better cooperative control. Moreover, it is very interesting and challenging to synchronize the human leg (driven by biological muscles) and exoskeleton (driven by artificial motor) to accomplish one task together, which is particularly solved in this work. A new cooperative control scheme is proposed, which can achieve shank swing motion under the harmonized and synchronized action of FES and exoskeleton, and realize different contribution of FES and exoskeleton. In such a scheme, a biologically-inspired control method, central pattern generator (CPG), is adopted because CPG has some favorable properties in synchronization, entrainment, and robustness against disturbance in general (Ijspeert, 2008). A combination of feedforward control and feedback control is used for FES and exoskeleton. A parameter regulator based on policy gradient method is designed to coordinate FES controller and exoskeleton controller adaptively. Five healthy subjects and four hemiplegic patients have participated in a series of experiments to test the cooperative control performance of FEXO Knee.

2. Method

2.1. FEXO Knee

The cooperative control of FES and exoskeleton is accomplished on our available prototype, FEXO Knee, which has two parts: a self-designed knee exoskeleton and a commercial FES device (RehaStim 2, Hasomed, Germany). The exoskeleton is composed of mechanical parts, electric motor, elastic actuator, sensors, and accessories. The function of exoskeleton is to generate assistive torque for rhythmic swing of human shank. It is designed for subjects with sitting posture, so it has a base bench that may be fixed on a table to hold the whole structure. The preliminary version (FEXO Knee I) has been reported in Ren and Zhang (2014). The new version (FEXO Knee II) is shown in Figure 1.

Figure 1. Structure of exoskeleton in FEXO Knee: (1) base bench, (2) electric motor (AC servo motor), (3) reducer, (4) shank wrap, (5) silicone board, (6) interactive force sensors, (7) outer shell, (8) signal amplification circuit, (9) encoder, (10) linear springs.

Continue —>  Frontiers | Cooperative Control for A Hybrid Rehabilitation System Combining Functional Electrical Stimulation and Robotic Exoskeleton | Neuroscience


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[ARTICLE] The immediate effect of FES and TENS on gait parameters in patients after stroke – Full Text PDF


[Purpose] This study was conducted to compare the immediate effects of different electrotherapies on the gait parameters for stroke patients.

[Subjects and Methods] Thirty patients with stroke were randomly assigned either to the functional electrical stimulation group or the transcutaneous electrical nerve stimulation group, with 15 patients in each group. Each electrotherapy was performed for 30 minutes simultaneously with the therapeutic exercise, and the changes in the spatial and temporal parameters of gait were measured.

[Results] After the intervention, a significant, immediate improvement in cadence and speed was observed only in the functional electrical stimulation group.

[Conclusion] Based on this study, functional electrical stimulation that stimulates motor nerves of the dorsiflexor muscles on the paretic side is recommended to achieve immediate improvement in the gait ability of stroke patients.[…]

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[Abstract] Sensing motion and muscle activity for feedback control of functional electrical stimulation: Ten years of experience in Berlin


After complete or partial paralysis due to stroke or spinal cord injury, electrical nerve stimulation can be used to artificially generate functional muscle contractions. This technique is known as Functional Electrical Stimulation (FES). In combination with appropriate sensor technology and feedback control, FES can be empowered to elicit also complex functional movements of everyday relevance. Depending on the degree and phase of impairment, the goal may be temporary support in a rehabilitation phase, e.g. during re-learning of gait after a stroke, or permanent replacement/support of lost motor functions in form of assistive devices often referred to as neuro-prostheses.

In this contribution a number of real-time capable and portable approaches for sensing muscle contractions and motions are reviewed that enable the realization of feedback control schemes. These include inertial measurement units (IMUs), electromyography (EMG), and bioimpedance (BI). This contribution further outlines recent concepts for movement control, which include e.g. cascaded control schemes. A fast inner control loop based on the FES-evoked EMG directly controls the amount of recruited motor units. The design and validation of various novel FES systems are then described that support cycling, walking, reaching, and swallowing. All methods and systems have been developed at the Technische Universität Berlin by the Control Systems Group within the last 10 years in close cooperation with clinical and industrial partners.

Source: Sensing motion and muscle activity for feedback control of functional electrical stimulation: Ten years of experience in Berlin – ScienceDirect

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[ARTICLE] Adaptive hybrid robotic system for rehabilitation of reaching movement after a brain injury: a usability study – Full Text



Brain injury survivors often present upper-limb motor impairment affecting the execution of functional activities such as reaching. A currently active research line seeking to maximize upper-limb motor recovery after a brain injury, deals with the combined use of functional electrical stimulation (FES) and mechanical supporting devices, in what has been previously termed hybrid robotic systems. This study evaluates from the technical and clinical perspectives the usability of an integrated hybrid robotic system for the rehabilitation of upper-limb reaching movements after a brain lesion affecting the motor function.


The presented system is comprised of four main components. The hybrid assistance is given by a passive exoskeleton to support the arm weight against gravity and a functional electrical stimulation device to assist the execution of the reaching task. The feedback error learning (FEL) controller was implemented to adjust the intensity of the electrical stimuli delivered on target muscles according to the performance of the users. This control strategy is based on a proportional-integral-derivative feedback controller and an artificial neural network as the feedforward controller. Two experiments were carried out in this evaluation. First, the technical viability and the performance of the implemented FEL controller was evaluated in healthy subjects (N = 12). Second, a small cohort of patients with a brain injury (N = 4) participated in two experimental session to evaluate the system performance. Also, the overall satisfaction and emotional response of the users after they used the system was assessed.


In the experiment with healthy subjects, a significant reduction of the tracking error was found during the execution of reaching movements. In the experiment with patients, a decreasing trend of the error trajectory was found together with an increasing trend in the task performance as the movement was repeated. Brain injury patients expressed a great acceptance in using the system as a rehabilitation tool.


The study demonstrates the technical feasibility of using the hybrid robotic system for reaching rehabilitation. Patients’ reports on the received intervention reveal a great satisfaction and acceptance of the hybrid robotic system.


Upper limb hemiparesis is one of the most common consequences after a brain injury accident [1]. This motor impairment has an adverse impact on the quality of life of survivors since it hinders the execution of activities of daily living. From the rehabilitation perspective, it is widely accepted that high-intensity and repetitive task-specific practice is the most effective principle to promote motor recovery after a brain injury [12]. However, traditional rehabilitation treatment offers a dose of movement repetition that is in most cases insufficient to facilitate neural reorganization [3]. In response to these current clinical shortcomings, there is a clear interest in alternative rehabilitation methods that improve the arm motor functionality of brain injury survivors.

Hybrid robotic systems for motor rehabilitation are a promising approach that combine the advantages of robotic support or assistive devices and functional electrical stimulation (FES) technologies to overcome their individual limitations and to offer more robust rehabilitation interventions [4]. Despite the potential benefits of using hybrid robotic systems for arm rehabilitation, a recent published review shows that only a few hybrid systems presented in the literature were tested with stroke patients [4]. Possible reasons could be the difficulties arising from the integration of both assistive technologies or the lack of integrated platforms that can be easily setup and used.

End-effector robotic devices combined with FES represent the most typical hybrid systems used to train reaching tasks under constrained conditions [567]. With these systems, patients’ forearms are typically restricted to the horizontal plane to isolate the training of the elbow extension movement. The main advantage of this approach is the simplicity of the setup, with only 1 Degree of Freedom (DoF). However, to maximize the treatment’s outcomes and achieve functional improvement it is necessary to train actions with higher range of motion (> 1 DoF) and functional connotations [89]. Yet, the complexity for driving a successful movement execution in such scenarios requires the implementation of a robust and reliable FES controller.

The appropriate design and implementation of FES controllers play a key role to achieve stable and robust motion control in hybrid robotic systems. The control strategy must be able to drive all the necessary joints to realize the desired movement, and compensate any disturbances to the motion, i.e. muscle fatigue onset as well as the strong nonlinear and time-varying response of the musculoskeletal system to FES [1011]. Consequently, open-loop and simple feedback controllers (e.g. proportional-integral-derivative -PID-) are not robust enough to cope with these disturbances [812]. Meadmore et al. presented a more suitable hybrid robotic system for functional rehabilitation scenarios [13]. They implemented a model-based iterative learning controller (ILC) that adjusts the FES intensity based on the tracking error of the previously executed movement (see [1314] for a detail description of the system). This iterative adjustment allows compensating for disturbances caused by FES. Although this approach addresses some of the issues regarding motion control with FES, it requires a detailed mathematical description of the musculoskeletal system to work properly. In this context, unmodeled dynamics and the linearization of the model can reduce the robustness of the controller performance. Also, the identification of the model’s parameters is complex and time consuming, which limits its applicability in clinical settings [1112].

The Feedback Error Learning (FEL) scheme proposed by Kawato [15] can be considered as an alternative to ILC. This scheme was developed to describe how the central nervous system acquires an internal model of the body to improve the motor control. Under this scheme, the motor control command of a feedback controller is used to train a feedforward controller to learn implicitly the inverse dynamics of the controlled system on-line (i.e. the arm). Complementary, this on-line learning procedure also allows the controller to adapt and compensate for disturbances. In contrast with the ILC, the main advantage of this strategy is that the controller does not require an explicit model of the controlled system to work correctly and that it can directly learn the non-linear characteristic of the controlled system. Therefore, using the FEL control strategy to control a hybrid robotic system can simplify the setup of the system considerably, which makes easier to deploy it in clinical settings as well as personalize its response according to each patient’s musculoskeletal characteristics and movement capabilities. The FEL has been used previously to control the wrist [16] and the lower limb [17] motion with FES in healthy subjects; but it has not been tested on brain injury patients. In a previous pilot study, we partially showed the suitability of the FEL scheme in hybrid robotic systems for reaching rehabilitation with healthy subjects [18]. However, a rigorous and robust analysis has not been presented neither this concept has not been tested with motor impaired patients.

The main objective of this study is to verify the usability of a fully integrated hybrid robotic system based on an FEL scheme for rehabilitation of reaching movement in brain injury patients. To attain such objective two-step experimentation was followed. The first part consists of demonstrating the technical viability and learning capability of the developed FEL controller to drive the execution of a coordinated shoulder-elbow joint movement. The second part consists of testing the usability of the platform with brain injury patients in a more realistic rehabilitation scenario. For this purpose, we assessed the patients’ performance and overall satisfaction and emotional response after using the system.


In this section, we present the hybrid robotic system for the rehabilitation of reaching movement in patients with a brain injury. The system focuses on aiding users to move their paretic arm towards specific distal directions in the space. During the execution of the reaching task, the FEL controller adjusts the intensities of the electrical stimuli delivered to target muscles in order to aid the subjects in tracking accurately the target paths.

Description of the hybrid rehabilitation platform for reaching rehabilitation

Figure 1 shows the general overview of the developed platform. This rehabilitation platform is composed of four main components: the hybrid assistive device (upper limb exoskeleton + FES device); the high-level controller (HLC); the visual feedback and; the user interface. […]

Fig. 1 a General overview of the presented hybrid robotic platform for reaching rehabilitation. bVisual feedback provided to the users. The green ball represents the actual arm position, the blue cross is the reference trajectory, the initial and final position are represented by the gray ball and red square respectively. c Interface for system configuration

Source: Adaptive hybrid robotic system for rehabilitation of reaching movement after a brain injury: a usability study | Journal of NeuroEngineering and Rehabilitation | Full Text

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[VIDEO] Bioness L300 Go Technology Introduction – YouTube

Published on Sep 25, 2017
L300 Go is a functional electrical stimulation (FES) system that satisfies the productivity demands of today’s value-based healthcare system. Key aspects of the L300 experience have been dramatically improved with 3D Motion Detection, multi-channel stimulation, Smart Bluetooth® programming and a home user mobile app that tracks activity to keep patients engaged in the rehabilitation process. All of this in a streamlined design, with a fitting process that is faster and easier than ever before.

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[WEB SITE] Bioness Announces Commercial Availability of the L300 Go™ System to Healthcare Professionals

Source: Bioness Announces Commercial Availability of the L300 Go™ System to Healthcare Professionals

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[Review] Review of devices used in neuromuscular electrical stimulation for stroke rehabilitation – PDF


Neuromuscular electrical stimulation (NMES), specifically functional electrical stimulation (FES) that compensates for voluntary motion, and therapeutic electrical stimulation (TES) aimed at muscle strengthening and recovery from paralysis are widely used in stroke rehabilitation. The electrical stimulation of muscle contraction should be synchronized with intended motion to restore paralysis. Therefore, NMES devices, which monitor electromyogram (EMG) or electroencephalogram (EEG) changes with motor intention and use them as a trigger, have been developed. Devices that modify the current intensity of NMES, based on EMG or EEG, have also been proposed. Given the diversity in devices and stimulation methods of NMES, the aim of the current review was to introduce some commercial FES and TES devices and application methods, which depend on the condition of the patient with stroke, including the degree of paralysis.

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[Abstract] Preliminary results of testing the recoveriX system on stroke patients 


Motor imagery based brain-computer interfaces (BCI) extract the movement intentions of subjects in real-time and can be used to control a cursor or medical devices. In the last years, the control of functional electrical stimulation (FES) devices drew researchers’ attention for the post-stroke rehabilitation field. In here, a patient can use the movement imagery to artificially induce movements of the paretic arms through FES in real-time.

Five patients who had a stroke that affected the motor system participated in the current study, and were trained across 10 to 24 sessions lasting about 40 min each with the recoveriX® system. The patients had to imagine 80 left and 80 right hand movements. The electroencephalogram (EEG) data was analyzed with Common Spatial Patterns (CSP) and linear discriminant analysis (LDA) and a feedback was provided in form of a cursor on a computer screen. If the correct imagination was classified, the FES device was also activated to induce the right or left hand movement.

In at least one session, all patients were able to achieve a maximum accuracy above 96%. Moreover, all patients exhibited improvements in motor function. On one hand, the high accuracies achieved within the study show that the patients are highly motivated to participate into a study to improve their lost motor functions. On the other hand, this study reflects the efficacy of combining motor imagination, visual feedback and real hand movement that activates tactile and proprioceptive systems.

Source: O174 Preliminary results of testing the recoveriX system on stroke patients – Clinical Neurophysiology

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[Abstract] Towards an ankle neuroprosthesis for hybrid robotics: Concepts and current sources for functional electrical stimulation


Hybrid rehabilitation robotics combine neuro-prosthetic devices (close-loop functional electrical stimulation systems) and traditional robotic structures and actuators to explore better therapies and promote a more efficient motor function recovery or compensation. Although hybrid robotics and ankle neuroprostheses (NPs) have been widely developed over the last years, there are just few studies on the use of NPs to electrically control both ankle flexion and extension to promote ankle recovery and improved gait patterns in paretic limbs. The aim of this work is to develop an ankle NP specifically designed to work in the field of hybrid robotics. This article presents early steps towards this goal and makes a brief review about motor NPs and Functional Electrical Stimulation (FES) principles and most common devices used to aid the ankle functioning during the gait cycle. It also shows a current sources analysis done in this framework, in order to choose the best one for this intended application.

Source: Towards an ankle neuroprosthesis for hybrid robotics: Concepts and current sources for functional electrical stimulation – IEEE Xplore Document

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[ARTICLE] Robot Assisted Training for the Upper Limb after Stroke (RATULS): study protocol for a randomised controlled trial – Full Text



Loss of arm function is a common and distressing consequence of stroke. We describe the protocol for a pragmatic, multicentre randomised controlled trial to determine whether robot-assisted training improves upper limb function following stroke.


Study design: a pragmatic, three-arm, multicentre randomised controlled trial, economic analysis and process evaluation.

Setting: NHS stroke services.

Participants: adults with acute or chronic first-ever stroke (1 week to 5 years post stroke) causing moderate to severe upper limb functional limitation.

Randomisation groups:

1. Robot-assisted training using the InMotion robotic gym system for 45 min, three times/week for 12 weeks

2. Enhanced upper limb therapy for 45 min, three times/week for 12 weeks

3. Usual NHS care in accordance with local clinical practice

Randomisation: individual participant randomisation stratified by centre, time since stroke, and severity of upper limb impairment.

Primary outcome: upper limb function measured by the Action Research Arm Test (ARAT) at 3 months post randomisation.

Secondary outcomes: upper limb impairment (Fugl-Meyer Test), activities of daily living (Barthel ADL Index), quality of life (Stroke Impact Scale, EQ-5D-5L), resource use, cost per quality-adjusted life year and adverse events, at 3 and 6 months.

Blinding: outcomes are undertaken by blinded assessors.

Economic analysis: micro-costing and economic evaluation of interventions compared to usual NHS care. A within-trial analysis, with an economic model will be used to extrapolate longer-term costs and outcomes.

Process evaluation: semi-structured interviews with participants and professionals to seek their views and experiences of the rehabilitation that they have received or provided, and factors affecting the implementation of the trial.

Sample size: allowing for 10% attrition, 720 participants provide 80% power to detect a 15% difference in successful outcome between each of the treatment pairs. Successful outcome definition: baseline ARAT 0–7 must improve by 3 or more points; baseline ARAT 8–13 improve by 4 or more points; baseline ARAT 14–19 improve by 5 or more points; baseline ARAT 20–39 improve by 6 or more points.


The results from this trial will determine whether robot-assisted training improves upper limb function post stroke.


Stroke is the commonest cause of complex adult disability in high-income countries [1]. Loss of arm function affects 69% of people who have a stroke [2]. Only 12% of people with arm weakness at the onset of stroke make a full recovery [3]. Improving arm function has been identified as a research priority by stroke survivors, carers and health professionals who report that current rehabilitation pays insufficient attention to arm recovery [4].

Robot-assisted training enables a greater number of repetitive tasks to be practised in a consistent and controllable manner. Repetitive task training is known to drive Hebbian plasticity, where wiring of pathways that are coincidently active is strengthened [5, 6]. A dose of greater than 20 h of repetitive task training improves upper limb motor recovery following a stroke [7] and, therefore, robot-assisted training has the potential to improve arm motor recovery after stroke. We anticipate that Hebbian neuroplasticity, which is learning dependent, will operate regardless of the post-stroke phase.

A Cochrane systematic review of electromechanical and robot-assisted arm training after stroke reported outcomes from a total of 1160 patients who participated in 34 randomised controlled trials (RCTs). Improvements in arm function (standardised mean difference (SMD) 0.35, 95% confidence interval (CI), 0.18–0.51) and activities of daily living (SMD 0.37, 95% CI, 0.11–0.64) were found in patients who received this treatment, but studies were often of low quality [8]. In the UK there is currently insufficient evidence to justify the use of this technology in routine clinical practice.

In addition, studies which suggest that robot-assisted training may improve upper limb function after stroke should be treated with caution as participants who were randomised to receive robot-assisted training may have also received an increased intensity of rehabilitation sessions (e.g. frequency or duration) compared to participants in the control groups. Greater intensity of upper limb rehabilitation sessions has been shown to improve upper limb functional outcomes [7], and a meta-analysis of robot-assisted training RCTs reported that if control group therapy sessions were delivered at the same frequency and duration, there was no additional functional improvement [9]. Studies are required which provide further direct evidence of the effectiveness of robot-assisted training without the confounding effect of therapy dose.

The aim of the Robot Assisted Training for the Upper Limb after Stroke (RATULS) trial is to evaluate the clinical and cost-effectiveness of robot-assisted training compared to an upper limb therapy programme of the same frequency and duration, and usual post-stroke care.

The null hypothesis is that there is no difference in upper limb function at 3 months between study participants who receive robot-assisted training and those who receive an enhanced upper limb therapy programme and those who receive usual post-stroke care. The RATULS trial will be making comparisons of the effectiveness of rehabilitation on upper limb function between all three pairs of trial arms.

Source: Robot Assisted Training for the Upper Limb after Stroke (RATULS): study protocol for a randomised controlled trial | Trials | Full Text


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