Archive for category Rehabilitation robotics

[Abstract] A Method for Self-Service Rehabilitation Training of Human Lower Limbs – IEEE Conference Publication


Recently, rehabilitation robot technologies have been paid more attention by the researchers in the fields of rehabilitation medicine engineering and robotics. To assist active rehabilitation of patients with unilateral lower extremity injury, we propose a new self-service rehabilitation training method in which the injured lower limbs are controlled by using the contralateral healthy upper ones. First, the movement data of upper and lower limbs of a healthy person in normal walk are acquired by gait measurement experiments. Second, the eigenvectors of upper and lower limb movements in a cycle are extracted in turn. Third, the linear relationship between the movement of upper and lower limbs is identified using the least squares method. Finally, the results of simulation experiments show that the established linear mapping can achieve good accuracy and adaptability, and the self-service rehabilitation training method is effective.

via A Method for Self-Service Rehabilitation Training of Human Lower Limbs – IEEE Conference Publication

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[Abstract] Design of Powered Wearable Elbow Brace for Rehabilitation Applications at Clinic and Home – IEEE Conference Publication


Spasticity and contractures are secondary to most neurological and orthopaedic pathologies. The most conservative method of management of spasticity and contractures is passive stretching exercises. Robotic rehabilitation aims to provide a solution to this problem. We describe in details the design of a powered wearable orthosis especially designed for managing spasticity and contractures. The device is fully portable, allowing the patient to undergo repeated-passive-dynamic exercises outside the hospital environment. The design of the device is modular to make it adaptable to different anatomies and pathologies. The device is also fitted with electrogoniometers and torque sensors to record kinematics and dynamics of the patient, improving the insight of the clinicians to the rehabilitation of the patient, as well as providing data for further clinical and scientific investigations. The mechanical integrity of the device elements is simulated and verified.

via Design of Powered Wearable Elbow Brace for Rehabilitation Applications at Clinic and Home – IEEE Conference Publication

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[Abstract] The effects of a robot-assisted arm training plus hand functional electrical stimulation on recovery after stroke: a randomized clinical trial



To compare the effects of unilateral, proximal arm robot-assisted therapy combined with hand functional electrical stimulation to intensive conventional therapy for restoring arm function in subacute stroke survivors.


This was a single blinded, randomized controlled trial.


Inpatient Rehabilitation University Hospital.


Forty patients diagnosed with ischemic stroke (time since stroke <8 weeks) and upper limb impairment were enrolled.


Participants randomized to the experimental group received 30 sessions (5 sessions/week) of robot-assisted arm therapy and hand functional electrical stimulation (RAT + FES). Participants randomized to the control group received a time-matched intensive conventional therapy (ICT).

Main outcome measures

The primary outcome was arm motor recovery measured with the Fugl-Meyer Motor Assessment. Secondary outcomes included motor function, arm spasticity and activities of daily living. Measurements were performed at baseline, after 3 weeks, at the end of treatment and at 6-month follow-up. Presence of motor evoked potentials (MEPs) was also measured at baseline.


Both groups significantly improved all outcome measures except for spasticity without differences between groups. Patients with moderate impairment and presence of MEPs who underwent early rehabilitation (<30 days post stroke) demonstrated the greatest clinical improvements.


A robot-assisted arm training plus hand functional electrical stimulation was no more effective than intensive conventional arm training. However, at the same level of arm impairment and corticospinal tract integrity, it induced a higher level of arm recovery.


via The effects of a robot-assisted arm training plus hand functional electrical stimulation on recovery after stroke: a randomized clinical trial – ScienceDirect

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[ARTICLE] A comparison of the rehabilitation effectiveness of neuromuscular electrical stimulation robotic hand training and pure robotic hand training after stroke: A randomized controlled trial – Full Text


The rehabilitation effects of the NMES robotic hand and robotic hand were compared.

Both training systems could significantly improve the motor function of upper limb.

The NMES robot was more effective than the pure robot.

NMES applied on distal muscle could benefit the recovery in the entire upper limb.




To compare the rehabilitation effects of the electromyography (EMG)-driven neuromuscular electrical stimulation (NMES) robotic hand and EMG-driven robotic hand for chronic stroke.


This study was a randomized controlled trial with a 3-month follow-up. Thirty chronic stroke patients were randomly assigned to receive 20-session upper limb training with either EMG-driven NMES robotic hand (NMES group, n = 15) or EMG-driven robotic hand (pure group, n = 15). The training effects were evaluated before and after the training, as well as 3 months later, using the clinical scores of Fugl-Meyer Assessment (FMA), Modified Ashworth Scale (MAS), Action Research Arm Test (ARAT), and Functional Independence Measure (FIM). Session-by-session EMG parameters, including the normalized EMG activation level and co-contraction indexes (CIs) of the target muscles were applied to monitor the recovery progress in muscular coordination patterns.


Both groups achieved significantly increased FMA and ARAT scores (p < 0.05), and the NMES group improved more (p < 0.05). A significant improvement in MAS was obtained in the NMES group (p < 0.05) but absence in the pure group. Meanwhile, better performance could be obtained in the NMES group in releasing the EMG activation levels and CIs than the pure group across the training sessions (p < 0.05).


Both training systems were effective in improving the long-term distal motor functions in upper limb, where the NMES robot-assisted training achieved better voluntary motor recovery and muscle coordination and more release in muscle spasticity.


This study indicated more effective distal rehabilitation using the NMES robot than the pure robot-assisted rehabilitation.

1. Introduction

Upper limb motor deficits are common after stroke, and observed in over 80% of stroke survivors [1,2]. Various rehabilitation devices have been purposed to assist human physical therapists to provide effective long-term rehabilitation programs [[3][4][5]]. Among them, rehabilitation robots and neuromuscular electrical stimulation (NMES) are most widely used in stroke rehabilitation practices. Rehabilitation robots have been recognized as efficient in such cases and could represent a cost-effective addition to conventional rehabilitation services because they provide highly intensive and repetitive training [[6][7][8][9]]. It has been reported that the integration of voluntary effort (e.g. electromyography, EMG) into robotic design could contribute significantly to motor recovery in stroke patients [6,10]. This is because an EMG-driven strategy can maximize the involvement of voluntary effort in the training, and its effectiveness at improving upper limb voluntary motor functions have been proved by many EMG-driven robot-assisted upper-limb training systems [[11][12][13]]. However, rehabilitation robots are unable to directly activate the desired muscle groups, which may only assist, or even dominate limb movement such as continuous passive motions (CPM) [14]. In addition, stroke patients usually cooperate with compensatory motions from other muscular activities to activate the target muscles, which may lead to ‘learned disuse’ [15]. However, NMES can effectively limit compensatory motions by stimulating specific muscles via cyclic electrical currents, which provides repetitive sensorimotor experiences [16]. With the advantage of precisely activating the target muscle, NMES has been reported to be effective in evoking sensory feedback, improving muscle force, and thus promoting motor function in stroke patients [17,18]. Nevertheless, training programs assisted by NMES alone are also suboptimal due to the difficulty of controlling movement trajectories and the early appearance of fatigue [19,20].

Accordingly, various NMES robot-assisted upper-limb training programs which combine these two unique techniques have been proposed to integrate the benefits and minimize the disadvantages [7,12,14,21,22]. The rehabilitation effectiveness of these combined systems has been investigated and reported to be effective in improving motor recovery. Several studies have compared the training outcomes of NMES robot-assisted training and other training programs. For example, Qian et al. [22] reported that NMES-robot-assisted upper-limb training could achieve better motor outcomes when compared with conventional therapies for subacute stroke patients. Meanwhile, another study which compared the training effects between robot-aided training with NMES and robot-aided training solely using the InMotion ARM™ Robot in the subacute period demonstrated that the active ranges of motion of the NMES robot-training group were significantly higher compared with the robot-training group [23]. Coincidentally, investigations into applications in chronic stroke patients have also been carried out. For instance, Hu et al. [14] proposed an EMG-driven NMES robot system for wrist training; this combined device improved muscle activation levels related to the wrist and reduced compensatory muscular activities at the elbow, while these training outcomes were absent for the EMG-driven robot-assisted training alone. Indeed, a similar study by another research group also achieved better rehabilitation outcomes on some clinical assessments using the combined system compared to robot-assisted therapy alone [21].

In the literature, most studies on current rehabilitation devices combining the NMES and robotic systems targeted the elbow and wrist joints [7,[21][22][23]], while very few focused on the hand and fingers [24]. In addition, a comparison of the training effects for hand rehabilitation between the NMES robot and other hand rehabilitation devices has not yet been adequately conducted. Indeed, the primary upper-limb disability post-stroke is the loss of hand function, and rehabilitation of the distal joints after stroke is much more difficult than the motor recovery of the proximal joints due to the compensatory motions from the proximal joints [25]. Hence, developing effective rehabilitation devices to minimize compensatory movements for hand motor recovery is especially meaningful for stroke rehabilitation. In our previous work, we developed an EMG-driven NMES robotic hand and suggested it for use in hand rehabilitation after stroke [26]. Our device provides fine control of hand movements and activates the target muscles selectively for finger extension/flexion, and its feasibility and effectiveness have been verified by a single group trial [12]. However, whether the long-term rehabilitation effect of this EMG-driven NMES robotic hand is comparable or even better than other hand rehabilitation devices are still unclear and need to be investigated quantitively. Therefore, the objective of this study is to compare the training effects of hand rehabilitation assisted by an NMES robotic hand and by a pure robotic hand though a randomized controlled trial with a 3-month follow-up (3MFU).

2. Methodology

2.1. Participants

This work was approved by the Human Subjects Ethics Sub-Committee of the Hong Kong Polytechnic University. A total of 53 stroke survivors were screened for the training from local districts. 30 participants with chronic stroke satisfied the following inclusion criteria: (1) The participants were at least 6 months after the onset of a singular and unilateral brain lesion due to stroke, (2) both the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joints could be extended to 180° passively, (3) muscle spasticity during extension at the finger joints and the wrist joint was below 3 as measured by the Modified Ashworth Scale (MAS) [27], ranged from 0 (no increase in muscle tone) to 4 (affected part rigid), (4) detectable voluntary EMG signals from the driving muscle on the affected side (three times of the standard deviation (SD) above the EMG baseline), and (5) no visual deficit and able to understand and follow simple instructions as assessed by the Mini-Mental State Examination (MMSE > 21) [28].

This work involved a randomized controlled trial with a 3-month follow-up (3MFU). The potential participants were first told that the training program they would receive could be either NMES robotic hand training or pure robotic hand training, and all recruited participants submitted their written consent before randomization. Then, the recruited participants were randomly assigned into two groups according to a computer-based random number generator, i.e., the computer program generated either “1” (denoting the NMES robotic hand training group) or “2” (the pure robotic hand group) with an equal probability of 0.5 (Matlab, 2017, Mathworks, Inc.). Fig. 1 shows the Consolidated Standards of Reporting Trials flowchart of the training program.

Fig. 1

Fig. 1. The consolidated standards of reporting trials flowchart of the experimental design.

2.2. Interventions

For both groups, each participant was invited to attend a 20-session robotic hand training with an intensity of 3–5 sessions/week, completed within 7 consecutive weeks. The training setup of both groups is shown in Fig. 2. This robotic hand training system can assist with finger extension and flexion of the paretic limb for patients after stroke. In this work, real-time voluntary EMG detected from the abductor pollicis brevis (APB) and extensor digitorum (ED) muscles were used to control the respective hand closing and opening movements, and the threshold level of each motion phase was set at three times the SD above the EMG baseline at resting state [12]. For example, during the motions of finger flexion, once the EMG activation level of the APB muscle reached a preset threshold, the robotic hand would provide mechanical assistance for hand closing. Similarly, during the motions of finger extension, the robotic hand would assist with hand opening when the EMG activation level of the ED muscle reached a preset threshold. For the NMES robot group, synchronized support from the NMES and the robot were both provided. The NMES electrode pair (30 mm diameter; Axelgaard Corp., Fallbrook, CA, USA) was attached over the ED muscle to provide stimulation during finger extension. The outputs of NMES were square pulses with a constant amplitude of 70 V, a stimulation frequency of 40 Hz, and a manually adjustable pulse width in the range 0–300 μs. Before the training, the pulse width was set at the minimum intensity, which achieved a fully extended position of the fingers in each patient. During the training, NMES would be triggered by the EMG from the ED muscle first and then provided stimulation to the ED muscle to assist hand-opening motions for the entire phase of finger extension, while no assistance from NMES was provided during finger flexion to avoid the possible increase of finger spasticity after stimulation [29]. For the pure robot group, the difference between the two groups was that no NMES was applied in the pure robot group. A detailed account of the working principles of the robotic hand have been described in our previous work [12,30,31].

Fig. 2

Fig. 2. The experimental setup of the robotic hand training: (A) pure robotic hand group; (B) neuromuscular electrical stimulation (NMES) robotic hand group.



Continue —-> A comparison of the rehabilitation effectiveness of neuromuscular electrical stimulation robotic hand training and pure robotic hand training after stroke: A randomized controlled trial – ScienceDirect

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[Abstract] Robot-Assisted Arm Training in Chronic Stroke: Addition of Transition-to-Task Practice


Background. Robot-assisted therapy provides high-intensity arm rehabilitation that can significantly reduce stroke-related upper extremity (UE) deficits. Motor improvement has been shown at the joints trained, but generalization to real-world function has not been profound.

Objective. To investigate the efficacy of robot-assisted therapy combined with therapist-assisted task training versus robot-assisted therapy alone on motor outcomes and use in participants with moderate to severe chronic stroke-related arm disability.

Methods. This was a single-blind randomized controlled trial of two 12-week robot-assisted interventions; 45 participants were stratified by Fugl-Meyer (FMA) impairment (mean 21 ± 1.36) to 60 minutes of robot therapy (RT; n = 22) or 45 minutes of RT combined with 15 minutes therapist-assisted transition-to-task training (TTT; n = 23). The primary outcome was the mean FMA change at week 12 using a linear mixed-model analysis. A subanalysis included the Wolf Motor Function Test (WMFT) and Stroke Impact Scale (SIS), with significance P <.05.

Results. There was no significant 12-week difference in FMA change between groups, and mean FMA gains were 2.87 ± 0.70 and 4.81 ± 0.68 for RT and TTT, respectively. TTT had greater 12-week secondary outcome improvements in the log WMFT (-0.52 ± 0.06 vs -0.18 ± 0.06; P = .01) and SIS hand (20.52 ± 2.94 vs 8.27 ± 3.03; P = .03).

Conclusion. Chronic UE motor deficits are responsive to intensive robot-assisted therapy of 45 or 60 minutes per session duration. The replacement of part of the robotic training with nonrobotic tasks did not reduce treatment effect and may benefit stroke-affected hand use and motor task performance.


via Robot-Assisted Arm Training in Chronic Stroke: Addition of Transition-to-Task Practice. – PubMed – NCBI

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[WEB SITE] Neurorehabilitation: Fighting strokes with robotics

Having a stroke can be a scary experience, but the long road to recovery might be getting shorter, thanks to research out of ECU.

Imagine suddenly losing control of a limb or your ability to communicate.

And while this happens, excruciating pain spreads across your head.

This was Joanna’s experience when she had a  at the age of 44.

“I was sick three days up to having my stroke,” Joanna explains. “I had vomiting, headaches and was not making much sense when talking.”

“Three days later, I was sitting down and then it felt like my head was being squeezed between two vices. Excruciating pain.”

Risk factor

In Australia, strokes affect around 55,000 people a year and are the third most common cause of death and a leading cause of disability.

There’s a range of factors that increase the risk of strokes, including diet, exercise and .

But one of the most telling  is, simply, age.

From the age of 45, the risk of a stroke in men is one in four, and for women, it’s one in five.

Fortunately, our knowledge of strokes and how to combat them has improved a lot in the past few decades.

A big part of the solution is getting help quickly, according to Edith Cowan University (ECU) Professor Dylan Edwards.

“If it’s the blockage of a blood vessel, it can be treated very well by anti-coagulant therapy that will break up the blood clot and restore the blood flow to the brain,” Dylan says.

“Typically, you notice somebody is having a stroke by them having issues with their speech or they have a weakness or funny sensation in one side of their body.”

But surviving a stroke is only part of the journey, and with 65% of stroke survivors suffering from some form of disability, restoring motor skills is a critical part of rehabilitation.

Road to recovery

Recovery from stroke can be a long and frustrating road for even the smallest paralysation.

For stroke survivor Joanna, the frustration she felt not being able to move normally made the recovery process even more challenging.

“The emotional side of having the stroke has affected me more than anything else,” Joanna says.

“You slowly get used to the fact that you can’t move your left side, and you know that you’ll get therapy. But when I had people come visit, when they left, I was in tears [out of frustration].”

Joanna eventually started to get some feeling back in her left side, just to her thumb at first.

“It was still a shock that I had lost all of that, so just a little bit of movement was enough to keep me going and stay motivated.”

Fighting back with technology

At ECU’s Lab for NeuroRehabilitation and Robotics, Dylan and his team have been researching how to help people recover their motor control after a brain or spinal cord injury.

Part of their research focuses on understanding the recovery of stroke survivors, using a robotic sensory platform called the Kinarm Exoskeleton Lab.

“The Kinarm looks like a fancy piece of gym equipment,” Dylan explains. “You sit inside the device and position your arms on top of movable handles, and you’re wheeled into this virtual reality environment.”

For the user in the chair, it feels like you’re playing a series of games, moving the chair’s arms to get a response on the screen—such as bouncing balls off paddles.

But the real work is happening behind the scenes.

“All of this information is acquired by these high-powered computers and analysed for how the person is performing,” Dylan says. “This [helps] identify the precise proprioceptive issue with an individual stroke survivor so we can prescribe therapy more effectively.”

In simplest terms, the Kinarm helps identify issues where the user is telling their arm to move but the resulting movement is not what they were trying to do.

This could be an arm not extending the full distance or slower reaction times.

With strokes usually affecting one side of the body more than the other, the unaffected side can provide a good baseline for what their normal reactions should be.

But what if both sides of the body have been affected? The Kinarm can pick up on that too, detecting deficits in what would be considered the unaffected side and showing this in the test results.

R&R—Robotics and Recovery

For Joanna, using the Kinarm has been a challenging experience, even three years after her stroke.

“It actually made you concentrate more in the game to hit the balls coming down,” she explains.

“I think that made you use the brain to try and keep up with your eye, which it didn’t, but I gave it my best shot. I also noticed my peripheral vision has gone.”

“It highlighted for me the improvements I have got since my stroke, which is nice for me three years on to see how it was then to what I could actually achieve on the Kinarm now.”

The data collected helps doctors prescribe the most beneficial treatment for their patients, based on the results of the tests.

Whether it’s heading towards recovering the function in a limb or something as simple as the mobility of a single joint, Dylan believes even small changes are worth pursuing.

“Some degree of independence—even though it might be apparent to an onlooker or a carer—can be very meaningful for a patient.”

“Small changes that we have made in the past through prescribing therapies effectively are things like being able to stabilise yourself on the train and send a text message.”

Recovering movement and lives

While full recovery from a stroke is not guaranteed, any improvement to quality of life can mean everything for survivors. Restoring simple movements can help patients build up their self-confidence to return to their everyday lives.

“Often stroke patients are in the older age bracket, and many of them are working,” Dylan says. “It’s very depressing to be disengaged from a functional work life, and going back to work might just be having the confidence of turning over a page of paper at your desk.”

As we learn more about how the body and brain recover after these , there’s hope we can find ways to better support those who have experienced extensive motor damage.

While there’s medication and training regimes to follow, at its core, it comes down to the drive to actively engage in recovering.

And even if it’s just through small victories, a spark from ECU’s Lab for NeuroRehabilitation and Robotics could help light the fire of determination in .

Explore further

Regulating blood supply to limbs improves stroke recovery


via Neurorehabilitation: Fighting strokes with robotics

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[ARTICLE] Robot-Assisted Stair Climbing Training on Postural Control and Sensory Integration Processes in Chronic Post-stroke Patients: A Randomized Controlled Clinical Trial – Full Text

Background: Postural control disturbances are one of the important causes of disability in stroke patients affecting balance and mobility. The impairment of sensory input integration from visual, somatosensory and vestibular systems contributes to postural control disorders in post-stroke patients. Robot-assisted gait training may be considered a valuable tool in improving gait and postural control abnormalities.

Objective: The primary aim of the study was to compare the effects of robot-assisted stair climbing training against sensory integration balance training on static and dynamic balance in chronic stroke patients. The secondary aims were to compare the training effects on sensory integration processes and mobility.

Methods: This single-blind, randomized, controlled trial involved 32 chronic stroke outpatients with postural instability. The experimental group (EG, n = 16) received robot-assisted stair climbing training. The control group (n = 16) received sensory integration balance training. Training protocols lasted for 5 weeks (50 min/session, two sessions/week). Before, after, and at 1-month follow-up, a blinded rater evaluated patients using a comprehensive test battery. Primary outcome: Berg Balance Scale (BBS). Secondary outcomes:10-meter walking test, 6-min walking test, Dynamic gait index (DGI), stair climbing test (SCT) up and down, the Time Up and Go, and length of sway and sway area of the Center of Pressure (CoP) assessed using the stabilometric assessment.

Results: There was a non-significant main effect of group on primary and secondary outcomes. A significant Time × Group interaction was measured on 6-min walking test (p = 0.013) and on posturographic outcomes (p = 0.005). Post hoc within-group analysis showed only in the EG a significant reduction of sway area and the CoP length on compliant surface in the eyes-closed and dome conditions.

Conclusion: Postural control disorders in patients with chronic stroke may be ameliorated by robot-assisted stair climbing training and sensory integration balance training. The robot-assisted stair climbing training contributed to improving sensorimotor integration processes on compliant surfaces. Clinical trial registration (NCT03566901).


Postural control disturbances are one of the leading causes of disability in stroke patients, leading to problems with transferring, maintaining body position, mobility, and walking (Bruni et al., 2018). Therefore, the recovery of postural control is one of the main goals of post-stroke patients. Various and mixed components (i.e., weakness, joint limitation, alteration of tone, loss of movement coordination and sensory organization components) can affect postural control. Indeed, the challenge is to determine the relative weight placed on each of these factors and their interaction to plan specific rehabilitation programs (Bonan et al., 2004).

The two functional goals of postural control are postural orientation and equilibrium. The former involves the active alignment of the trunk and head to gravity, the base of support, visual surround and an internal reference. The latter involves the coordination of movement strategies to stabilize the center of body mass during self-initiated and externally triggered stability perturbations. Postural control during static and dynamic conditions requires a complex interaction between musculoskeletal and neural systems (Horak, 2006). Musculoskeletal components include biomechanical constraints such as the joint range of motion, muscle properties and limits of stability (Horak, 2006). Neural components include sensory and perceptual processes, motor processes involved in organizing muscles into neuromuscular synergies, and higher-level processes essential to plan and execute actions requiring postural control (Shumway-Cook and Woollacott, 2012). A disorder in any of these systems may affect postural control during static (in quite stance) and dynamic (gait) tasks and increase the risk of falling (Horak, 2006).

Literature emphasized the role of impairments of sensory input integration from visual, somatosensory and vestibular systems in leading to postural control disorders in post-stroke patients (Bonan et al., 2004Smania et al., 2008). Healthy persons rely on somatosensory (70%), vision (10%) and vestibular (20%) information when standing on a firm base of support in a well-lit environment (Peterka, 2002). Conversely, in quite stance on an unstable surface, they increase sensory weighting to vestibular and vision information as they decrease their dependence on surface somatosensory inputs for postural orientation (Peterka, 2002). Bonan et al. (2004) investigate whether post-stroke postural control disturbances may be caused by the inability to select the pertinent somatosensory, vestibular or visual information. Forty patients with hemiplegia after a single hemisphere chronic stroke (at least 12 months) performed computerized dynamic posturography to assess the patient’s ability to use sensory inputs separately and to suppress inaccurate inputs in case of sensory conflict. Six sensory conditions were assessed by an equilibrium score, as a measure of body stability. Results show that patients with hemiplegia seem to rely mostly on visual input. In conditions of altered somatosensory information, with visual deprivation or visuo-vestibular conflict, the patient’s performance was significantly lower than healthy subjects. The mechanism of this excessive visual reliance remains unclear. However, higher-level inability to select the appropriate sensory input rather than to elementary sensory impairment has been advocated as a potential mechanism of action (Bonan et al., 2004).

Sensory strategies and sensory reweighting processes are essential to generate effective movement strategies (ankle, hip, and stepping strategies) which can be resolved through feed-back or feed-forward postural adjustments. The cerebral cortex shapes these postural responses both directly via corticospinal loops and indirectly via the brainstem centers (Jacobs and Horak, 2007). Moreover, the cerebellar- and basal ganglia-cortical loop is responsible for adapting postural responses according to prior experience and for optimizing postural responses, respectively (Jacobs and Horak, 2007).

Rehabilitation is the cornerstone in the management of postural control disorders in post-stroke patients (Pollock et al., 2014). To date, no one physical rehabilitation approach can be considered more effective than any other approach (Pollock et al., 2014). Specific treatments should be chosen according to the individual requirements and the evidence available for that specific treatment. Moreover, it appears to be most beneficial a mixture of different treatment for an individual patient (Pollock et al., 2014). Considering that, rehabilitation involving repetitive, high intensity, task-specific exercises is the pathway for restoring motor function after stroke (Mehrholz et al., 2013Lo et al., 2017) robotic assistive devices for gait training have been progressively being used in neurorehabilitation to Sung et al. (2017). In the current literature, three primary evidence have been reported.

Firstly, a recent literature review highlights that robot-assisted gait training is advantageous as add-on therapy in stroke rehabilitation, as it adds special therapeutic effects that could not be afforded by conventional therapy alone (Morone et al., 2017Sung et al., 2017). Specifically, robot-assisted gait training was beneficial for improving motor recovery, gait function, and postural control in post-stroke patients (Morone et al., 2017Sung et al., 2017). Stroke patients who received physiotherapy treatment in combination with robotic devices were more likely to reach better outcomes compared to patients who received conventional training alone (Bruni et al., 2018).

Second, the systematic review by Swinnen et al. (2014) supported the use of robot-assisted gait therapy to improve postural control in subacute and chronic stroke patients. A wide variability among studies was reported about the robotic-device system and the therapy doses (3–5 times per week, 3–10 weeks, 12–25 sessions). However, significant improvements (Cohen’s d = 0.01 to 3.01) in postural control scores measured with the Berg Balance Scale (BBS), the Tinetti test, postural sway tests, and the Timed Up and Go (TUG) test were found after robot-assisted gait training. Interestingly, in five studies an end-effector device (gait trainer) was used (Peurala et al., 2005Tong et al., 2006Dias et al., 2007Ng et al., 2008Conesa et al., 2012). In two study, the exoskeleton was used (Hidler et al., 2009Westlake and Patten, 2009). In one study, a single joint wearable knee orthosis was used (Wong et al., 2012). Because the limited number of studies available and methodological differences among them, more specific randomized controlled trial in specific populations are necessary to draw stronger conclusions (Swinnen et al., 2014).

Finally, technological and scientific development has led to the implementation of robotic devices specifically designed to overcome the motor limitation in different tasks. With this perspective, the robot-assisted end-effector-based stair climbing (RASC) is a promising approach to facilitate task-specific activity and cardiovascular stress (Hesse et al., 20102012Tomelleri et al., 2011Stoller et al., 20142016Mazzoleni et al., 2017).

To date, no studies have been performed on the effects of RASC training in improving postural control and sensory integration processes in chronic post-stroke patients.

The primary aim of the study was to compare the effects of robot-assisted stair climbing training against sensory integration balance training on static and dynamic balance in chronic stroke patients. The secondary aims were to compare the training effects on sensory integration processes and mobility. The hypothesis was that the task-specific and repetitive robot-assisted stairs climbing training might act as sensory integration balance training, improving postural control because sensorimotor integration processes are essential for balance and walking.[…]


Continue —->  Frontiers | Robot-Assisted Stair Climbing Training on Postural Control and Sensory Integration Processes in Chronic Post-stroke Patients: A Randomized Controlled Clinical Trial | Neuroscience

Figure 1. The G-EO system used in the Robot-Assisted Stair-Climbing Training (Written informed consent was obtained from the individual pictured, for the publication of this image).


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[Abstract] Variable impedance control of finger exoskeleton for hand rehabilitation following stroke



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.


via Variable impedance control of finger exoskeleton for hand rehabilitation following stroke | Emerald Insight

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[BOOK] Intelligent Biomechatronics in Neurorehabilitation – Xiaoling Hu – Google Books

Front Cover
Academic PressOct 19, 2019 – Science – 286 pages

Intelligent Biomechatronics in Neurorehabilitation presents global research and advancements in intelligent biomechatronics and its applications in neurorehabilitation. The book covers our current understanding of coding mechanisms in the nervous system, from the cellular level, to the system level in the design of biological and robotic interfaces. Developed biomechatronic systems are introduced as successful examples to illustrate the fundamental engineering principles in the design. The third part of the book covers the clinical performance of biomechatronic systems in trial studies. Finally, the book introduces achievements in the field and discusses commercialization and clinical challenges.

As the aging population continues to grow, healthcare providers are faced with the challenge of developing long-term rehabilitation for neurological disorders, such as stroke, Alzheimer’s and Parkinson’s diseases. Intelligent biomechatronics provide a seamless interface and real-time interactions with a biological system and the external environment, making them key to automation services.

  • Written by international experts in the rehabilitation and bioinstrumentation industries
  • Covers the current understanding of nervous system coding mechanisms, which are the basis for biological and robotic interfaces
  • Demonstrates and discusses robotic rehabilitation effectiveness and automatic evaluation

via Intelligent Biomechatronics in Neurorehabilitation – Xiaoling Hu – Google Books

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