Archive for category Paretic Hand

[ARTICLE] Brain–computer interface and assist-as-needed model for upper limb robotic arm – Full Text

Post-stroke paralysis, whereby subjects loose voluntary control over muscle actuation, is one of the main causes of disability. Repetitive physical therapy can reinstate lost motions and strengths through neuroplasticity. However, manually delivered therapies are becoming ineffective due to scarcity of therapists, subjectivity in the treatment, and lack of patient motivation. Robot-assisted physical therapy is being researched these days to impart an evidence-based systematic treatment. Recently, intelligent controllers and brain–computer interface are proposed for rehabilitation robots to encourage patient participation which is the key to quick recovery. In the present work, a brain–computer interface and assist-as-needed training paradigm have been proposed for an upper limb rehabilitation robot. The brain–computer interface system is implemented with the use of electroencephalography sensor; moreover, backdrivability in the actuator has been achieved with the use of assist-as-needed control approach, which allows subjects to move the robot actively using their limited motions and strengths. The robot only assists for the remaining course of trajectory which subjects are unable to perform themselves. The robot intervention point is obtained from the patient’s intent which is captured through brain–computer interface. Problems encountered during the practical implementation of brain–computer interface and achievement of backdrivability in the actuator have been discussed and resolved.

The recovery of upper limb motions and strengths in patients with damaged neuromuscular system via robotic rehabilitation devices is a promising way of enhancing existing treatments and their efficacies. Various reasons may cause limb dysfunctions, including stroke, spinal cord injuries, or even ligament rupture. According to the World Health Organization, about 15 million people globally suffer from Cerebro-Vascular Accidents (CVAs) each year and up to 65% of these need limb recovery procedures.1 Only in the last 15 years, the number of CVA or stroke patients is increased by 40%, which is the result of a more intense pace of living, deterioration of ecology, and increased aging population.2 Considering these statistics, development of new and efficient ways of rehabilitation is just as important as implementation of improved prevention strategies.

For the last 20 years, robotics-based therapy was steadily paving its way for becoming an essential practice in rehabilitation medicine.3,4 According to the systematic review of Kwakkel et al.5 on the upper limb recovery using robot-aided therapy, repetitive, meaningful, labor-intensive treatment programs implemented with robotic devices provide positive impact for the restoration of functional abilities in human limbs. In medical terminology, a device that provides support, and aligns or improves the function of movable limbs is known as orthosis, and robotic devices intended to provide such treatment are called robotic orthoses.6 Particularly, two key directions gained major attention in the medical engineering research: robot-assisted therapy and functional electrical simulation (FES) therapy. The FES therapy describes a technique that stimulates weakened or paralyzed muscles on a human limb by applying electric charges externally. The goal of FES therapy is to reactivate the neural connections between a muscle and human’s sensorimotor system to enable patients’ ability to control their limbs without assistance.7 In the study by Popovic and others, the functional electrical therapy (FET) was applied with the use of surface electrodes and it was used to stimulate arm fingers of patients, this therapy has demonstrated positive therapeutic effects.8 It was revealed that daily 30-min therapy for 1-month period allowed improvement in movement range, speed, and increased strength in muscles. There are also side effects of FES-based treatment such as pain and irritation on the affected area, autonomic dysreflexia, increased spasticity, broken bones, and mild electric shocks from faulty equipment. However, the robot-assisted rehabilitation is non-invasive and free from above risks, and it is preferred for the rehabilitation of stroke survivors.

The important advantage of robotic devices is that they can reduce the burden on health care workers who traditionally had to conduct labor-intensive training sessions for patients. Equipped with sensors, intelligent controllers, and haptic and visual interfaces, robotic orthosis can have a potential to put the recovery process to a new level by collecting relevant data about various health parameters (pulse rate, body temperature, etc.) and adjusting the training modes accordingly. Besides the positive impacts of robot-based rehabilitation, the reliability of robot-based assistance is still questionable and adversely it may worsen the recovery progress made before, and that depends on the type of assistance control robot employs.9 Assist-as-needed (AAN) control type has become one of the prominent strategies recently which has been recommended positively from clinical trials.10 In order to stabilize the system, AAN-based approach has become subject to be researched by scientists. In the work done by Wolbrecht, AAN control is obtained from the adaptive control by incorporating novel force to address and decrease the system’s parametric errors.11 There are also other works which propose AAN type of control for their systems;1214 however, there are no works which have incorporated both BCI (brain–computer interface)- and AAN-based control approach into the system.

Owing to the recent advances in biosensors, especially in their robustness and signal processing, robot controllers equipped with bio-sensing are able to achieve intelligence with less complex algorithms. One of the most recent applications of BCI is in the domain of orthoses.1517 Newer instances of orthoses combine latest advances in control theory and brain activity. Berlin Technical University in cooperation with Korean University created an exoskeleton to maneuver lower limbs. A feature of this work is the use of non-invasive electroencephalography (EEG). The study involved 11 healthy men aged 25 to 32 years.18 First upper limb exoskeleton controlled by BCI was proposed by AA Frolov et al.19 Authors concluded that BCI inclusion improves the movements of the paretic hand in post-stroke patients irrespective of severity and localization of the disease. In addition, it was shown that duration of the training also increases effectiveness of rehabilitation.

Based on the letters on the screen, it was possible to determine native language of the patient in the work done by Vasileva.20 In this work, non-invasive EEG had been used. However, it was noted that non-invasive devices have less accuracy than professional medical EEG equipment. To improve signal detection, Agapov et al.21 have developed advanced algorithm of processing visually evoked potentials. To visualize stimuli, “eSpeller” software was developed.

Motivated by the above-mentioned successes and advances, in the present work, possible use of BCI is investigated in the rehabilitation robots for the treatment of stroke survivors. The aim of this work is to develop EEG-based mechatronic system that can receive electrical brain signals, detect emotions and gestures of the patient, and intelligently control robotic arm. In addition, to ensure smooth and compliant movement of the rehabilitation robot and improve treatment efficacy, AAN control paradigm is also considered. This research used EEG package and a controller to develop BCI system and realize AAN-based control. Developed system can help patients to control robot with their thoughts and enhance their participation in the rehabilitation process. Methodology of the current work is explained in the “Methodology” section, and in the subsequent sections, results are discussed before drawing conclusions from this research work.

EEG sensor

In order to register the brain activity, 16 EEG electrodes distributed around the patient’s head have been used. To provide more information which is related to motor imaginary signals, the frequency characteristics were extracted from the data by converting them from the time domain to the frequency domain. Furthermore, to distinguish between movement intentions and rest positions, bandpass filter in the range of 5 to 40 Hz was used.22,23 Since EEG data set recording can be very large, the powerful surface Laplacian technique was applied to lower the risk of influence from the neighboring neurons on the crucial cerebral cortex neurons.24 Finally, only dominant frequency of 13 to 30 Hz, also known as beta wave frequency, was featured according to Gropper et al.25 This band distinction was benchmarker as a sensible area of resting brain activity.

Abiding by the previous works associated with EEG signal processing in Iáñez et al.26 and Hortal et al.,27 the feature selection was reduced to the group of 29 features, which later were used for the further classification and predictive model construction.

After receiving data using an EEG, algorithm needs to determine the desired effect for the user. Input data for this algorithm are EEG signals recorded during the demonstration of stimuli. In most of the currently existing studies on this subject, the problem of classifying signals is divided into three large subtasks:

  • Preprocessing the signal (in order to remove noise components);
  • Formation of a feature space;
  • Classification of objects in the constructed feature space.

It should be noted that the greatest influence on the final quality of the classification is made by the extent to which the task of forming the feature space was successfully accomplished. The general scheme of operation of BCI is depicted in Figure 1.


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Figure 1. Block diagram of BCI interface.

 

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Continue —>  Brain–computer interface and assist-as-needed model for upper limb robotic arm – Akim Kapsalyamov, Shahid Hussain, Askhat Sharipov, Prashant Jamwal, 2019

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Figure 4. (a) ELA actuated upper limb rehabilitation robot, (b) upper limb rehabilitation robot in use, and (c) robotic orthosis in use with EEG sensor.

 

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[WEB PAGE] Upper arm rehabilitation after severe stroke: where are we? – Physics World

10 Sep 2019 Andrea Rampin 
EEG cap

Stroke is the second leading cause of death worldwide and the third cause of induced disability, according to estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study. Treatments based on constraint-induced movement therapy, occupational practice, virtual reality and brain stimulation can work well for patients with mild impairment of upper limb movement, but they are not as effective for those burdened by severe disability. Therefore, novel individualized approaches are needed for this patient group.

Martina Coscia from the Wyss Center for Bio and Neuroengineering in Geneva, and colleagues from several other Swiss institutes, have published a review paper summarizing the most advanced techniques in use today for treatment of severe, chronic stroke patients. The researchers describe techniques being developed for upper limb motor rehabilitation: from robotics and muscular electrical stimulation, to brain stimulation and brain–computer/machine interfaces (Brain 10.1093/brain/awz181).

Robot-aided rehabilitation approaches include movement-assisting exoskeletons and end-effector devices, which enable upper arm movement by stimulating the peripheral nervous system. These techniques can also trigger reorganization of the impaired peripheral nervous system and encourage rehabilitation of the damaged somatosensory system. Several studies have reported the efficiency of robot-aided rehabilitation, alone or in combination with other techniques, in the treatment of upper limb motor impairment. One study that included severely impaired individuals also demonstrated encouraging results.

Muscular electrical stimulation can help improve the connection of motor neurons to the spinal cord and the motor cortex. Researchers have also demonstrated that application of electrical stimuli to the muscles provides positive effects on the neurons responsible for sensory signal transduction to the brain, thereby improving the motion control loop function. By modulating motor neurons’ sensitivity, muscular electrical stimulation inhibits the muscle spasms observed in other treatments.

More recently, therapies have moved on from the simple use of currents to harnessing coordinated stimuli to orchestrate more complex, task-related movements. Although this particular set of techniques didn’t show a particular advantage over physiotherapy in long-term studies of patients with mild upper limb impairment, it did seem to have a stronger effect for chronic severe patients.

Stimulating the brain

Brain stimulation, meanwhile, stimulates cortical neurons in order to improve their ability to form new connections within the affected neural network. Brain stimulation techniques can be divided into two branches – electrical and magnetic – both of which can activate or inhibit neural activity, depending on the polarity and intensity of the stimulus.

Transcranial magnetic stimulation

Researchers have achieved encouraging results using both techniques. In particular, magnetic field-triggered inhibition of the contralesional hemisphere (the hemisphere that was not affected by the stroke) activity yielded positive results. Magnetic, low-frequency stimulation of the contralesional hemisphere also proved encouraging – improving the reach to grasp ability of patients, although only for small objects. Excitingly, some studies suggest that coupling contralesional cortex inhibition with magnetic stimulation of the chronically affected area could achieve effective results.

Within these techniques, one promising approach is invasive brain stimulation, in which a device is surgically implanted in a superficial region of the brain. Such techniques allow for more sustained and spatially-oriented stimulation of the desired brain regions. The Everest trial used such methods and showed significant improvement for a larger percentage of patients after 24 weeks, compared with standard rehabilitation protocols.

Another promising recent development is non-invasive deep-brain stimulation, achieved by temporally interfering electric fields. The authors envision that a deeper understanding of the complex mechanisms involved in the brain’s reactions to magnetic and electrical stimulation will provide an important assistance in clinical application of these techniques.

The final category, brain–computer or brain–machine interfaces (BCIs or BMIs), exploit electroencephalogram (EEG) patterns to trigger feedback or an action output from an external device. Devices that produce feedback are used to train the patient to recruit the correct zone of the brain and help reorganize its interconnections. These techniques have only recently transitioned to the clinic; however, early results and observations are promising. For example, a BCI technique coupled with muscular electrical stimulation restored patients’ ability to extend their fingers.

In recent years, researchers have also tested combinations of the techniques described above. For example, combinations of robotics and muscular electrical stimulation have shown encouraging results, especially when more than one articulation was targeted by the treatment. Combining brain stimulation with muscular electrical stimulation and robotics has proved more effective in severe than in moderate cases. Also, coupling of muscular electrical stimulation with magnetic inhibitory brain stimulation provided better results than either individual technique. Interestingly, addition of electrical brain stimulation to a BCI system coupled with a robotic motor feedback enhanced the outcome, helping to achieve adaptive brain remodelling at the expense of inappropriate reorganization.

Coscia and co-authors highlight that all the techniques studied share a range of limitations that should be addressed, such as small sample size, limited understanding of the underlying mechanisms, lack of treatment personalization and minimal attention to the training task, which they note is often of limited importance for daily life. Addressing these limitations might be key to improving the clinical outcome for patients with severe stroke-induced upper limb paralysis treated with neurotechnology-aided interventions. Moreover, the authors plan to begin a clinical trial to test the use of a novel personalized therapy approach that will include a combination of the described techniques.

 

via Upper arm rehabilitation after severe stroke: where are we? – Physics World

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[Abstract] Myoelectric Computer Interface Training for Reducing Co-Activation and Enhancing Arm Movement in Chronic Stroke Survivors: A Randomized Trial

Background. Abnormal muscle co-activation contributes to impairment after stroke. We developed a myoelectric computer interface (MyoCI) training paradigm to reduce abnormal co-activation. MyoCI provides intuitive feedback about muscle activation patterns, enabling decoupling of these muscles.

Objective. To investigate tolerability and effects of MyoCI training of 3 muscle pairs on arm motor recovery after stroke, including effects of training dose and isometric versus movement-based training.

Methods. We randomized chronic stroke survivors with moderate-to-severe arm impairment to 3 groups. Two groups tested different doses of isometric MyoCI (60 vs 90 minutes), and one group tested MyoCI without arm restraint (90 minutes), over 6 weeks. Primary outcome was arm impairment (Fugl-Meyer Assessment). Secondary outcomes included function, spasticity, and elbow range-of-motion at weeks 6 and 10.

Results. Over all 32 subjects, MyoCI training of 3 muscle pairs significantly reduced impairment (Fugl-Meyer Assessment) by 3.3 ± 0.6 and 3.1 ± 0.7 (P < 10−4) at weeks 6 and 10, respectively. Each group improved significantly from baseline; no significant differences were seen between groups. Participants’ lab-based and home-based function also improved at weeks 6 and 10 (P ≤ .01). Spasticity also decreased over all subjects, and elbow range-of-motion improved. Both moderately and severely impaired patients showed significant improvement. No participants had training-related adverse events. MyoCI reduced abnormal co-activation, which appeared to transfer to reaching in the movement group.

Conclusions. MyoCI is a well-tolerated, novel rehabilitation tool that enables stroke survivors to reduce abnormal co-activation. It may reduce impairment and spasticity and improve arm function, even in severely impaired patients.

 

via Myoelectric Computer Interface Training for Reducing Co-Activation and Enhancing Arm Movement in Chronic Stroke Survivors: A Randomized Trial – Emily M. Mugler, Goran Tomic, Aparna Singh, Saad Hameed, Eric W. Lindberg, Jon Gaide, Murad Alqadi, Elizabeth Robinson, Katherine Dalzotto, Camila Limoli, Tyler Jacobson, Jungwha Lee, Marc W. Slutzky, 2019

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[Abstract] Development of the modified simple test for evaluating hand function (modified STEF): Construct, reliability, validity, and responsiveness

Abstract

Study Design

Clinimetric evaluation study.

Introduction

Despite the availability of numerous performance tests to measure finger dexterity, there is no international consensus on hand function evaluation.

Purpose of the Study

To evaluate the reliability, validity, and responsiveness of the modified version of the simple test for evaluating hand function (STEF), which is widely used in Japan.

Methods

The intrarater (n = 40) and inter-rater (n = 32) reliability of the modified STEF was evaluated by calculating the intraclass correlation coefficient (ICC), models (1,1) and (2,1), respectively, in healthy individuals. The criterion validity of the modified STEF (n = 50) was evaluated by calculating the Pearson correlation coefficient relative to the STEF, the Purdue pegboard test (PPT), and the Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire. The standardized response mean of the scores was calculated to determine responsiveness (n = 35). The modified STEF was used prospectively to measure the change in hand function in a cohort of patients with hand trauma injuries and inflammatory diseases (n = 30), as well as in a cohort of patients with cervical spondylosis (n = 20), from preoperative baseline to 1 and 3 months postoperatively.

Results

ICC1.1 and ICC2.1 values were ≥0.80, indicative of high intrarater and inter-rater reliability. All correlation coefficients were significant (P < .05): STEF (r = 0.89), PPT (r = 0.69), and DASH (r = −0.34). The standardized response mean indicated greater responsiveness of the modified STEF (0.89) than the STEF (0.71) and PPT (0.68) but a lower responsiveness than the DASH (1.11).

Discussion

It must be mentioned that modified STEF and DASH cannot be compared without caution. The two types of tools should complement each other when measuring someone’s activity and participation level.

Conclusions

The modified STEF is a reliable measurement tool, with a moderate positive correlation with the PPT and a greater responsiveness than the STEF and PPT.

via Development of the modified simple test for evaluating hand function (modified STEF): Construct, reliability, validity, and responsiveness – ScienceDirect

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[Abstract] An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential

Abstract

Movement impairments resulting from neurologic injuries, such as stroke, can be treated with robotic exoskeletons that assist with movement retraining. Exoskeleton designs benefit from low impedance and accurate torque control. We designed a two-degrees-of-freedom tethered exoskeleton that can provide independent torque control on elbow flexion/extension and forearm supination/pronation. Two identical series elastic actuators (SEAs) are used to actuate the exoskeleton. The two SEAs are coupled through a novel cable-driven differential. The exoskeleton is compact and lightweight, with a mass of 0.9 kg. Applied rms torque errors were less than 0.19 Nm. Benchtop tests demonstrated a torque rise time of approximately 0.1 s, a torque control bandwidth of 3.7 Hz, and an impedance of less than 0.03 Nm/° at 1 Hz. The controller can simulate a stable maximum wall stiffness of 0.45 Nm/°. The overall performance is adequate for robotic therapy applications and the novelty of the design is discussed.

via An Elbow Exoskeleton for Upper Limb Rehabilitation With Series Elastic Actuator and Cable-Driven Differential – IEEE Journals & Magazine

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[Abstract] Robotic Techniques Used for Increasing Improvement Rate In The Rehabilitation Process Of Upper Limb Stroke Patients – Full Text PDF

Abstract

The rate of stroke patients in Pakistan is increasing, resulting in the decrease mobility of the patients. The movement of upper limb stoke patient is decreased due to the weakness and loss of joint control in his upper body. To improve the coordination of movement of the upper limb stroke patients, many methods e.g. passive and active modes for improving the disrupted mobility are introduced. The objectives of this paper are to first review the studies on upper limb stroke patients and the techniques used for increasing the improvement rate through physical therapy by exoskeleton and evaluation of the performance of the patient using methods such as quantification and graphical representations so that it can be shown to the patient for his motivation to improve further. The paper introduces a mechanical design of exoskeleton with 1 degree of freedom for elbow and 2 degrees of freedom for shoulder movement for rehabilitation of joints of stoke patients. It also mentions the safety that will be taken in the process so that the exoskeleton is safe to use when it is in contact with human. The model of the exoskeleton has the characteristic of being modular and easily operable and use admittance control strategy. Control strategy of the exoskeleton is discussed to maintain the position and orientation of the device and also is able to cater the gravitational attraction which plays an important part in the movement of the actuators. The mathematical model of motion attained due to the degrees of freedom of the exoskeleton is then evaluated and the lastly areas where the future work of exoskeleton can be done are discussed.

Full Text PDF

via Robotic Techniques Used for Increasing Improvement Rate In The Rehabilitation Process Of Upper Limb Stroke Patients | Sukkur IBA Journal of Computing and Mathematical Sciences

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[Abstract] Modeling and analysis of hydraulic piston actuation of McKibben fluidic artificial muscles for hand rehabilitation

Soft robotic actuators are well-suited for interactions with the human body, particularly in rehabilitation applications. The fluidic artificial muscle (FAM), specifically the McKibben FAM, is a type of soft robotic actuator that can be driven either pneumatically or hydraulically, and has potential for use in rehabilitation devices. The force applied by a FAM is well-described by a variety of models, the most common of which is based on the virtual work principle. However, the use of a piston assembly as a hydraulic power source for activation of FAMs has not previously been modeled in detail. This article presents a FAM designed to address the specific needs of a hand rehabilitation device. A syringe pump test bed is used to find and validate a novel volume–strain relationship. The volume–strain relationship remains constant with the coupled piston–FAM system, regardless of load. This confirms a bivariate approach to FAM control which is particularly beneficial in the exoskeleton application as the load varies throughout use. A novel, fixed-end cylindrical model is found to predict the strain of the FAM, given a volume input, regardless of load. For the FAMs tested in this work, the fixed-end cylindrical model improves strain prediction seven-fold when compared with traditional models.

via Modeling and analysis of hydraulic piston actuation of McKibben fluidic artificial muscles for hand rehabilitation – Anderson S Camp, Edward M Chapman, Paola Jaramillo Cienfuegos,

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[WEB SITE] When it Comes to Stroke Recovery, Who You See Matters

(a) Top view of the experiment. A tablet monitor was placed over the participant’s right forearms on the desk in front of them. (b) Diagrammatic view of the experiment from the left. There is a space to open the hand, which made it easier to imagine the opening-clench hand movement. (Photo courtesy of Toshihisa Tanaka, TUAT)

For stroke patients, observing their own hand movements in a video-assisted therapy – as opposed to someone else’s hand – could enhance brain activity and speed up rehabilitation, according to researchers.

The scientists, from Tokyo University of Agriculture and Technology (TUAT), published their findings in IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Brain plasticity, where a healthy region of the brain fulfills the function of a damaged region of the brain, is a key factor in the recovery of motor functions caused by stroke. Studies have shown that sensory stimulation of the neural pathways that control the sense of touch can promote brain plasticity, essentially rewiring the brain to regain movement and senses.

To promote brain plasticity, stroke patients may incorporate a technique called motor imagery in their therapy. Motor imagery allows a participant to mentally simulate a given action by imagining themselves going through the motions of performing that activity. This therapy may be enhanced by a brain-computer interface technology, which detects and records the patients’ motor intention while they observe the action of their own hand or the hand of another person, a media release from Tokyo University of Agriculture and Technology explains.

“We set out to determine whether it makes a difference if the participant is observing their own hand or that of another person while they’re imagining themselves performing the task,” says co-author Toshihisa Tanaka, a professor in the Department of Electrical and Electrical Engineering at TUAT in Japan and a researcher at the RIKEN Center for Brain Science and the RIKEN Center for Advanced Intelligent Project.

The researchers monitored brain activity of 15 healthy right-handed male participants under three different scenarios. In the first scenario, participants were asked to imagine their hand moving in synchrony with hand movements being displayed in a video clip showing their own hand performing the task, together with corresponding voice cues.

In the second scenario, they were asked to imagine their hand moving in synchrony with hand movements being displayed on a video clip showing another person’s hand performing the task, together with voice cues. In the third scenario, the participants were asked to open and close their hands in response to voice cues only.

Using electroencephalography (EEG), brain activity of the participants was observed as they performed each task.

The team found meaningful differences in EEG measurements when participants were observing their own hand movement and that of another person. The findings suggest that, in order for motor imagery-based therapy to be most effective, video footage of a patient’s own hand should be used.

“Visual tasks where a patient observes their own hand movement can be incorporated into brain-computer interface technology used for stroke rehabilitation that estimates a patient’s motor intention from variations in brain activity, as it can give the patient both visual and sense of movement feedback,” Tanaka explains.

[Source(s): Tokyo University of Agriculture and Technology, EurekAlert]

via When it Comes to Stroke Recovery, Who You See Matters – Rehab Managment

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[Abstract] Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

via Fuzzy sliding mode control of a wearable rehabilitation robot for wrist and finger | Emerald Insight

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[Abstract] Task-oriented Motor Learning in Upper Extremity Rehabilitation Post Stroke

Abstract

Background: Upper extremity deficits are the most popular symptoms following stroke. Task-oriented training has the ability to increase motor area excitability in the brain, which can stimulate the recovery of motor control.

Objective: This study was aimed to examine the efficiency of the task-oriented approach on paretic upper extremity following a stroke, and to identify efficient treatment dosage in those populations.

Method: We searched through PubMed, Scopus, Physiotherapy Evidence Database (PEDro), National Rehabilitation Information (REHABDATA), and Web of Science databases. Randomized clinical trials (RCTs) and pseudo-RCTs those investigating upper extremity in patients with stroke published in English language were selected. Different scales and measurement methods to assess range of motion, strength, spasticity, and upper extremity function were considered. The quality assessment of included articles was evaluated utilizing the PEDro scale. Effect sizes were calculated.

Results: Six RCTs were included in the present study. The quality assessment for included studies ranged from 6 to 8 with 6.5 as a median. A total of 456 post-stroke patients, 41.66% of which were women, were included in all studies. The included studies demonstrated a meaningful influence of task-oriented training intervention on the hemiplegic upper limb motor functions but not spasticity post-stroke.

Conclusion: Task-oriented training does not produce a superior effect than other conventional physical therapy interventions in treating upper extremity in patients with stroke. There is no evidence supporting the beneficial effect of task-oriented on spasticity. Task-oriented training with the following dosage 30 to 90 minutes/session, 2 to 3 sessions weekly for 6 to 10 weeks may improve motor function and strength of paretic upper extremity post-stroke.

via Task-oriented Motor Learning in Upper Extremity Rehabilitation Post Stroke – Anas R. Alashram, Giuseppe Annino, Nicola Biagio Mercuri,

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