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.
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.
If you’ve ever dealt with a chronically sore shoulder, you know it can be debilitating and depressing. However, it appears that virtual reality could be a key factor in increasing your range of motion. It all comes down to training your brain differently, and VR pain management developer Karuna Labs believes it has made a breakthrough.
Sometimes my mind plays tricks on me
A recent study with 15 patients suffering from chronic shoulder pain was conducted by Karuna Labs. In the study, the company’s Virtual Embodiment Training technology was used to measure effects on non-painful limbs. Using “Mirror Visual Feedback,” participants were shown their non-affected arm as if it were their painful arm. Tests were conducted to measure the range of motion on their shoulder flexon, scaption, and abduction.
When this was done, the range of motion was reduced in the shoulder. What did not happen, however, was the reverse. Mirroring the non-affected arm onto the painful side did not increase the range of motion.
This certainly makes sense. Pain starts in your nerves rather than the brain directly, but Karuna Labs is confident about its potential applications. The technology is already being used to address pain more broadly in both homes and in clinics.
“These study results using our proprietary VR technology have immense implications for rehabilitation in patients suffering from chronic pain,” said CEO Lincoln Nguyen in the announcement. “Not only does our Virtual Embodiment Training provide measurable improvements in range of motion for patients with chronic pain, but it may have the potential to become a safe alternative to surgery, opioids, and traditional physical therapy upon further study.”
This isn’t the first time we’ve heard of VR being used to aid rehabilitation, either. Neuro Rehab VR developed its own platform via Oculus Quest that is meant to complement physical therapy rather than eliminate it. Using software while performing exercises leads to more accuracy. This could potentially mean faster recovery as better connections are established in the brain.
To demonstrate the feasibility of algorithmic prediction utilizing a model of baseline arm movement, genetic factors, demographic characteristics, and multi-modal assessment of the structure and function of motor pathways. To identify prognostic factors and the biological substrate for reductions in arm impairment in response to repetitive task practice.
This prospective single-group interventional study seeks to predict response to a repetitive task practice program using an intent-to-treat paradigm. Response is measured as a change of ≥5 points on the Upper Extremity Fugl-Meyer from baseline to final evaluation (at the end of training).
Anticipated enrollment of 96 community-dwelling adults with chronic stroke (onset ≥6 months) and moderate to severe residual hemiparesis of the upper limb as defined by a score of 10-45 points on the Upper Extremity Fugl-Meyer.
The intervention is a form of repetitive task practice using a combination of robot-assisted therapy coupled with functional arm use in real-world tasks administered over 12 weeks.
Main outcome measures
Upper extremity Fugl-Meyer Assessment (primary outcome), Wolf Motor Function Test, Action Research Arm Test, Stroke Impact Scale, questionnaires on pain and expectancy, magnetic resonance imaging, transcranial magnetic stimulation, arm kinematics, accelerometry, and a saliva sample for genetic testing.
Methods for this trial are outlined and an illustration of inter-individual variability is provided by example of two participants who present similarly at baseline but achieve markedly different outcomes.
This article presents the design, methodology, and rationale of an ongoing study to develop a predictive model of response to a standardized therapy for stroke survivors with chronic hemiparesis. Applying concepts from precision medicine to neurorehabilitation is practicable and needed to establish realistic rehabilitation goals and to effectively allocate resources.
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.
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.
A low-cost 1-DOF hand exoskeleton for neuromuscular rehabilitation has been designed and assembled. It consists of a base equipped with a servo motor, an index finger part, and a thumb part, connected through three gears. The index part has a tri-axial load cell and an attached ring to measure the finger force. An admittance control scheme was designed to provide intuitive control and positive force amplification to assist the user’s finger movement. To evaluate the effects of different control parameters on neuromuscular response of the fingers, we created an integrated exoskeleton-hand musculoskeletal model to virtually simulate and optimize the control loop. The exoskeleton is controlled by a proportional derivative controller that computes the motor torque to follow a desired joint angle of the index part, which is obtained from inverse kinematics of a virtual end-effector mass driven by the finger force. We conducted parametric simulations of the exoskeleton in action, driven by the user’s
closing and opening finger motion, with different proportional gains, endeffector masses, and other coefficients. We compared the interaction forces between the index finger and the ring in both passive and active modes. The best performing assistive controller can reduce the force from around 1.45N (in passive mode) to only around 0.52N, more than 64% of reduction. As a result, the muscle activations of the flexors and extensors were reduced significantly. We also noted the admittance control scheme is versatile and can also provide resistance (e.g. for strength training) by simply increasing the virtual end-effect mass.
Stroke, one of the leading causes of adult disability, affects approximately 800,000 individuals each year in the United States . Nearly 80% of stroke survivors suffer
from hemiparesis of the upper arm and thus impaired hand function, which is integral
to most activities of daily living. It is well established that highly repetitive training
can aid in the recovery of motor function of the hand however this can be labor intensive for the providing physical therapist in addition to the cost. In the past decade,
more robotic hand rehabilitation devices have been introduced to help patients recover
hand function through assistance during repetitive training of the hand [2-4].
In a comprehensive review by Heo et al. , hand exoskeleton technologies for rehabilitation and assistive engineering, from basic hand biomechanics, neurophysiology, sensors and actuators, physical human-robot interactions and ergonomics, are summarized. Different types of actuators and control schemes have been used for hand exoskeletons. In some control schemes, the robotic device will move the user passively through a preprogrammed trajectory for continuous passive movement (CPM) therapy. These devices can be beneficial for severely impaired individuals who may not have the ability to generate the forces required for specific finger or hand movement or for individuals who have abnormal muscle synergies preventing continuous movement. A few devices such as the Kinetic Maestra and Vector 1 are commercially available devices that are used for CPM [5, 6]. These devices allow for
passive movement through the range of motion for individual fingers. However, as
there is no active participation by the user, this device on its own may not promote
neurorehabilitation. These devices can be combined with other simulations or control
schemes that require active participation by the user. One commercially available
hand exoskeleton that has been used extensively by our lab to provide haptics to virtual simulations is the CyberGrasp . The CyberGrasp is a cable driven exoskeleton
that weighs 450 grams and can provide up to 12 N of force on each finger and can be
used to provide assistance for extension of the user’s fingers. In one study, this was
used in combination with a virtual reality simulation to train finger individuation as
the user played a virtual piano . The CyberGrasp was used to resist finger flexion
of the inactive fingers, promoting movement of the active independent finger. Similarly, the eXtension Glove (X-Glove) was developed to be used for cyclical stretching
in addition to active movement training [9-11]. This cable driven design is actuated
using linear servos allowing for individual finger movement in both extension and
flexion. In addition to this, each cable is integrated with a tension sensor which allows
the force of each digit to be monitored. This device has two modes that can be used
for rehabilitation, the first mode cyclically extends and flexes the fingers. The second
mode is an active training mode in which the glove provides constant extension assistance so that the user can complete flexion tasks as long as they overcome the force
required to keep the finger extended. In a further attempt to integrate user control with
the exoskeleton, an external input from the user such as force or electromyography
has been incorporated into some designs such as the Helping Hand . This soft
robotic device allows for active assistance for each finger individually, in addition to
the ability to follow control states triggered by EMG.
In this paper, we introduce a low cost 1-DOF hand exoskeleton for neuromuscular
rehabilitation of individual fingers. This exoskeleton consists of a base equipped with
a servo motor, an index finger component and a thumb component connected with
gears. The exoskeleton’s control system was designed to generate suitable actuation
torques based on the interaction force between the user’s finger and the exoskeleton’s
index component. The goal of this study is to model the exoskeleton interacting with a
neuromuscular hand model in order to evaluate the effectiveness of an intuitive admittance control algorithm on providing different levels of assistance or resistance during hand rehabilitation.
2.1 The 1-DOF Exoskeleton and Hand Model
This exoskeleton consists of a base stationed with a servo motor (Dynamixel
XM430), an index finger part and a thumb part, which are connected through 3 gears
of equal sizes as shown in Fig. 1. The motor drives the top gear which in turn rotates
the gear attached to the index part and then the gear attached the thumb part. The
index and thumb parts both have rings for the fingers, and an OptoForce tri-axial load
cell or force sensor (OnRobot, Denmark) is attached to the index ring. All parts are
3D printed with a carbon fiber reinforced nylon material called Onyx (Markforged,
USA). The total weight of this exoskeleton is 0.158kg and the mass and inertia properties of its components, which were either measured or computed based on material
and part geometry, are listed in Table 1.
Every year, millions of people experience a stroke but only a few of them fully recover. Recovery requires a working staff, which is time consuming and inefficient. Therefore, over the past few years rehabilitation robots like Exoskeletons have been used in the recuperation process for patients. In this paper we have designed an Exosuit which takes into considerations of the rigid Exo-Skeleton and its limitations for patients suffering from loss of function of the arm. This paper concentrates on enabling a stroke affected person to perform flexion-extension at elbow joint. Validation of the developed model on general population is still needed.
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Stroke is a leading cause of serious long-term disability. World Health Organization (WHO) published that the second leading of death is stroke accident and every year, 15 million people worldwide suffer from stroke attack, two-thirds of them have a permanent disability. Muscle impairment can be treated by intensive movements involving repetitive task, task-oriented and task-variegated. Conventional stroke rehabilitation is expensive, less engaging and at the same time need more time for the rehabilitation process and need more energy and time for the therapist to guide the stroke-survivor. Modern stroke rehabilitation is more promising and more effective with modern rehabilitation aids allowing the rehabilitation process to be faster, however, this therapist method can be obtained in the big cities. To cover the lack of rehabilitation process in this research will develop and improve post-stroke rehabilitation using games. This research using electromyography (EMG) device to analyze the muscle contraction during the rehabilitation process and using Kinect XBOX to record trajectory hands movements. Five games from movements sequence have designed and will be examined in this research. This games obtained two results, the first is the EMG signal and the second is trajectory data. EMG signal can recognize muscle contractions during playing game and the trajectory data can save the pattern of movements and showed the pattern to the monitor. EMG signal processing using time or frequency feature extractions is a good idea to obtain more information from muscle contractions, also velocity, similarities and error movements can be obtained by study the possible approaches.
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