Posts Tagged finger

[Abstract] Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings

Abstract

Robots have the potential to help provide exercise therapy in a repeatable and reproducible manner for stroke survivors. To facilitate rehabilitation of the wrist and fingers joint, an electromechanical exoskeleton was developed that simultaneously moves the wrist and metacarpophalangeal joints.
The device was designed for the ease of manufacturing and maintenance, with specific considerations for countries with limited resources. Active participation of the user is ensured by the implementation of electromyographic control and visual feedback of performance. Muscle activity requirements, movement parameters, range of motion, and speed of the device can all be customized to meet the needs of the user.
Twelve stroke survivors, ranging from the subacute to chronic phases of recovery (mean 10.6 months post-stroke) participated in a pilot study with the device. Participants completed 20 sessions, each lasting 45 minutes. Overall, subjects exhibited statistically significant changes (p < 0.05) in clinical outcome measures following the treatment, with the Fugl-Meyer Stroke Assessment score for the upper extremity increasing from 36 to 50 and the Barthel Index increasing from 74 to 89. Active range of wrist motion increased by 190 while spasticity decreased from 1.75 to 1.29 on the Modified Ashworth Scale.
Thus, this device shows promise for improving rehabilitation outcomes, especially for patients in countries with limited resources.

via Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings – IEEE Journals & Magazine

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[VIDEO] Stroke Rehabilitation: Use of electrical stimulation to help arm and hand recovery

This video demonstrates how to use FES, Functional Electrical Stimulation, to engage the muscles of the arm to extend the fingers.

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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] Neural Correlates of Passive Position Finger Sense After Stroke

Background. Proprioception of fingers is essential for motor control. Reduced proprioception is common after stroke and is associated with longer hospitalization and reduced quality of life. Neural correlates of proprioception deficits after stroke remain incompletely understood, partly because of weaknesses of clinical proprioception assessments.

Objective. To examine the neural basis of finger proprioception deficits after stroke. We hypothesized that a model incorporating both neural injury and neural function of the somatosensory system is necessary for delineating proprioception deficits poststroke.

Methods. Finger proprioception was measured using a robot in 27 individuals with chronic unilateral stroke; measures of neural injury (damage to gray and white matter, including corticospinal and thalamocortical sensory tracts), neural function (activation of and connectivity of cortical sensorimotor areas), and clinical status (demographics and behavioral measures) were also assessed.

Results. Impairment in finger proprioception was present contralesionally in 67% and bilaterally in 56%. Robotic measures of proprioception deficits were more sensitive than standard scales and were specific to proprioception. Multivariable modeling found that contralesional proprioception deficits were best explained (r2 = 0.63; P = .0006) by a combination of neural function (connectivity between ipsilesional secondary somatosensory cortex and ipsilesional primary motor cortex) and neural injury (total sensory system injury).

Conclusions. Impairment of finger proprioception occurs frequently after stroke and is best measured using a quantitative device such as a robot. A model containing a measure of neural function plus a measure of neural injury best explained proprioception performance. These measurements might be useful in the development of novel neurorehabilitation therapies.

via Neural Correlates of Passive Position Finger Sense After Stroke – Morgan L. Ingemanson, Justin R. Rowe, Vicky Chan, Jeff Riley, Eric T. Wolbrecht, David J. Reinkensmeyer, Steven C. Cramer, 2019

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[Abstract] Pre-therapeutic Device for Post-stroke Hemiplegic Patients’ Wrist and Finger Rehabilitation

Abstract

Background/Objectives

This paper suggests a pre-therapeutic device for post-stroke hemiplegic patients’ wrist and finger rehabilitation both to decrease and analyze their muscle tones before the main physical or occupational therapy.

Method/Statistical Analysis

We designed a robot which consists of a BLDC motor, a torque sensor, linear motion guides and bearings. Mechanical structure of the robot induces flexion and extension of wrist and finger (MCP) joints simultaneously with the single motor. The frames of the robot were 3D printed. During the flexion/extension exercise, angular position and repulsive torque of the joints are measured and displayed in real time.

Findings

A prototype was 3D printed to conduct preliminary experiment on normal subject. From the neutral joint position (midway between extension and flexion), the robot rotated 120 degrees to extension direction and 30 degrees to flexion direction. First, the subject used the machine with the usual wrist and finger characteristics without any tones. Second, the same subject intentionally gave strength to the joints in order to imitate affected upper limb of a hemiplegic patient. During extension exercise, maximum repulsive torque of the normal hand was 2 Nm whereas that of the firm hand was almost 5 Nm. The result revealed that the device was capable enough to not only rotate rigid wrist and fingers with the novel robotic structure, but also present quantitative data such as the repulsive torque according to the joint orientation as an index of joint spasticity level.

Improvements/Applications

We are planning to improve the system by applying torque control and arranging experiments at hospitals to obtain patients’ data and feedbacks to meet actual needs in the field.

via Indian Journals

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[ARTICLE] An Attention-Controlled Hand Exoskeleton for the Rehabilitation of Finger Extension and Flexion Using a Rigid-Soft Combined Mechanism – Full Text

Hand rehabilitation exoskeletons are in need of improving key features such as simplicity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control. This article presents a brain-controlled hand exoskeleton based on a multi-segment mechanism driven by a steel spring. Active rehabilitation training is realized using a threshold of the attention value measured by an electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. We present a prototype implementation of this rigid-soft combined multi-segment mechanism with active training and provide a preliminary evaluation. The experimental results showed that the proposed mechanism could generate enough range of motion with a single input by distributing an actuated linear motion into the rotational motions of finger joints during finger flexion/extension. The average attention value in the experiment of concentration with visual guidance was significantly higher than that in the experiment without visual guidance. The feasibility of the attention-based control with visual guidance was proven with an overall exoskeleton actuation success rate of 95.54% (14 human subjects). In the exoskeleton actuation experiment using the general threshold, it performed just as good as using the customized thresholds; therefore, a general threshold of the attention value can be set for a certain group of users in hand exoskeleton activation.

Introduction

Hand function is essential for our daily life (Heo et al., 2012). In fact, only partial loss of the ability to move our fingers can inhibit activities of daily living (ADL), and even reduce our quality of life (Takahashi et al., 2008). Research on robotic training of the wrist and hand has shown that improvements in finger or wrist level function can be generalized across the arm (Lambercy et al., 2011). Finger muscle weakness is believed to be the main cause of loss of hand function after strokes, especially for finger extension (Cruz et al., 2005Kamper et al., 2006). Hand rehabilitation requires repetitive task exercises, where a task is divided into several movements and patients are asked to practice those movements to improve their hand strength, range of motion, and motion accuracy (Takahashi et al., 2008Ueki et al., 2012). High costs of traditional treatments often prevent patients from spending enough time on the necessary rehabilitation (Maciejasz et al., 2014). In recent years, robotic technologies have been applied in motion rehabilitation to provide training assistance and quantitative assessments of recovery. Studies show that intense repetitive movements with robotic assistance can significantly improve the hand motor functions of patients (Takahashi et al., 2008Ueki et al., 2008Kutner et al., 2010Carmeli et al., 2011Wolf et al., 2006).

Patients should be actively involved in training to achieve better rehabilitation results (Teo and Chew, 2014Li et al., 2018). Motor rehabilitation has implemented Brain Computer Interface (BCI) methods as one of the means to detect human movement intent and get patients to be actively involved in the motor training process (Teo and Chew, 2014Li et al., 2018). Motor imagery-based BCIs (Jiang et al., 2015Pichiorri et al., 2015Kraus et al., 2016Vourvopoulos and Bermúdez I Badia, 2016), movement-related cortical potentials-based BCIs (Xu et al., 2014Bhagat et al., 2016), and steady-state motion visual evoked potential-based BCIs (Zhang et al., 2015) have been used to control rehabilitation robots. However, the high cost and complexity of the preparation in utilizing these methods mean that most current BCI devices are more suitable for research purposes than clinical practices. This is attributable to the fact that the ease of use and device cost are two main factors to consider during the selection of human movement intent detection based on BCIs for practical use (van Dokkum et al., 2015Li et al., 2018). Therefore, non-invasive, easy-to-install BCIs that are convenient to use with acceptable accuracy should be introduced to hand rehabilitation robot control.

Owing to the versatility and complexity of human hands, developing hand exoskeleton robots for rehabilitation assistance in hand movements is challenging (Heo et al., 2012Arata et al., 2013). In recent years, hand exoskeleton devices have drawn much research attention, and the results of current research look promising (Heo et al., 2012). Hand exoskeleton devices mainly use linkage, wire, or hydraulically/pneumatically driven mechanisms (Polygerinos et al., 2015a). The rigid mechanical design of linkage-based mechanisms provides robustness and reliability of power transmission, and has been widely applied in hand exoskeletons (Tong et al., 2010Ito et al., 2011Arata et al., 2013Cui et al., 2015Polygerinos et al., 2015a). However, the safety problem of misalignment between the human finger joints and the exoskeleton joints may occur during rehabilitation movements (Heo et al., 2012Cui et al., 2015). Compensation approaches used in current studies make the mechanism more complicated (Nakagawara et al., 2005Fang et al., 2009Ho et al., 2011). Pneumatic and hydraulic soft hand exoskeletons, which are made of flexible materials, are proposed to assist hand opening or closing (Ang and Yeow, 2017Polygerinos et al., 2015aYap et al., 2015b). However, despite bi-directional assistance—namely finger flexion and extension—being essential for hand rehabilitation (Iqbal et al., 2014), a large group of current soft hand exoskeleton devices only provide finger flexion assistance (Connelly et al., 2010Polygerinos et al., 20132015aYap et al., 2015ab). Wire-driven mechanisms can also be complex to transmit bi-directional movements since wires can only transmit forces along one direction (In et al., 2015Borboni et al., 2016). In order to transmit bi-directional movements, a tendon-driven hand exoskeleton was proposed, where the tendon works as a tendon during the extension movement and as compressed flexible beam constrained into rectilinear slides mounted on the distal sections of the glove during flexion (Borboni et al., 2016). Arata et al. (2013) attempted to avoid wire extension and other associated issues by proposing a hand exoskeleton with a three-layered sliding spring mechanism. Hand rehabilitation exoskeleton devices are still seeking to achieve key features such as low complexity, compactness, bi-directional actuation, low cost, portability, safe human-robotic interaction, and intuitive control.

In this article, we describe the design and characterization of a novel multi-segment mechanism driven by one layer of a steel spring that can assist both extension and flexion of the finger. Thanks to the inherent features of this multi-segment mechanism, joint misalignment between the device and the human finger is no longer a problem, enhancing the simplicity and flexibility of the device. Moreover, its compliance makes the hand exoskeleton safe for human-robotic interaction. This mechanism can generate enough range of motion with a single input by distributing an actuated linear motion to the rotational motions of finger joints. Active rehabilitation training is realized by using a threshold of the attention value measured by a commercialized electroencephalography (EEG) sensor as a brain-controlled switch for the hand exoskeleton. Features of this hand exoskeleton include active involvement of patients, low complexity, compactness, bi-directional actuation, low cost, portability, and safe human-robotic interaction. The main contributions of this article include: (1) prototyping and evaluation of a hand exoskeleton with a rigid-soft combined multi-segment mechanism driven by one layer of a steel spring with a sufficient output force capacity; (2) using attention-based BCI control to increase patients’ participation in exoskeleton-assisted hand rehabilitation; and (3) determining the threshold of attention value for our attention-based hand rehabilitation robot control.

Exoskeleton Design

Design Requirements

The target users are stroke survivors during flaccid paralysis period who need continuous passive motion training of their hands. They should also be able to focus their attention on motion rehabilitation training for at least a short period of time. For the purpose of hand rehabilitation, an exoskeleton should have minimal ADL interference and have the ability to generate adequate forces to perform hand flexion and extension with a range of motion that is similar or slightly lower than the motion range of a natural finger.

To achieve minimal ADL interference, the device is to be confined to the back of the finger and the width of the device should not exceed the finger width. Here, the width and height constraints of the exoskeleton on the back of the finger are both 20 mm. Low weight of the rehabilitation systems is a key requirement to allow practical use by a wide stroke population (Nycz et al., 2016). Therefore, the target weight of the exoskeleton should be as light as possible to make the patient feel more comfortable to wear it. The typical weight of other hand exoskeletons is in the range of 0.7 kg–5 kg (CyberGlove Systems Inc., 2016Delph et al., 2013Polygerinos et al., 2015aRehab-Robotics Company Ltd., 2019). In this article, the target weight of the exoskeleton is less than 0.5 kg.

There are 15 joints in the human hand. The thumb joint consists of an interphalangeal joint (IPJ), a metacarpophalangeal joint (MPJ), and a carpometacarpal joint (CMJ). Each of the other four fingers has three joints including a metacarpophalangeal joint (MCPJ), a proximal interphalangeal joint (PIPJ), and a distal interphalangeal joint (DIPJ). The hand exoskeleton must have three bending degrees of freedom (DOF) to exercise the three joints of the finger. For some rehabilitation applications, it is unnecessary for each of the MCPJ, PIPJ, and DIPJ of the human finger to have independent motion as long as the whole range of motion of the finger is covered. Tripod grasping requires the MPJ and IPJ of the thumb to bend around 51° and 27°; MCPJ, PIPJ, and DIPJ of the index finger to bend around 46°, 48°, and 12°; and for the middle finger to bend around 46°, 54°, and 12° (In et al., 2015). For the execution speed of rehabilitation exercises, physiotherapists suggest a lower speed than 20 s for a flexion/extension cycle of a finger joint (Borboni et al., 2016). It has to be stressed that hyperextension of all these joints should always be carefully avoided.

The exerted force to the finger should be able to enable continuous passive motion training. In addition, the output force should help the patient to generate grasping forces required to manipulate objects in ADL. Pinch forces required to complete functional tasks are typically below 20 N (Smaby et al., 2004). Polygerinos et al. (2015b) estimated each robot finger should exert a distal tip force of about 7.3 N to achieve a palmar grasp—namely four fingers against the palm of the hand—to pick up objects less than 1.5 kg. Existing devices can provide a maximum transmission output force between 7 N and 35 N (Kokubun et al., 2013In et al., 2015Polygerinos et al., 2015bBorboni et al., 2016Nycz et al., 2016).

The design should allow some customization to hand size and adaptability to different patient statuses and different stages of rehabilitation.

Rigid-Soft Combined Mechanism

Based on our established design requirements, a hand exoskeleton was designed and constructed (see Figure 1). In our design, each finger was driven by one actuator for finger extension and flexion, resulting in a highly compact device. A multi-segment mechanism with a spring layer was proposed. It has respectable adaptability, thus avoiding joint misalignment problems. A three-dimensional model of a single finger actuator is shown in Figure 1A. This finger actuator contained a linear motor, a steel strap, and a multi-segment mechanism. As shown in Figure 1B, the spring layer bended and slid because of the linear motion input provided by the linear actuator. The structure then became like a circular sector. When the structure was attached to a finger, it supported the finger flexion/extension motion. Five finger actuators were attached to a fabric glove via Velcro straps and five linear motors were attached to a rigid part which was fixed to the forearm by a Velcro strap. Each steel strap was attached to a motor by a small rigid 3D-printed part. It should be noted that the current structure is not applicable to thumb adduction/abduction.

Figure 1. Design of the hand exoskeleton: (A) CAD drawing of the index finger acuator; (B) bending motion generated by the proposed mutli-segment mechanism with a spring layer; (C) segment thicknesses (unit: mm); and (D) overview of the hand exoskeleton prototype.

[…]

 

Continue —>  Frontiers | An Attention-Controlled Hand Exoskeleton for the Rehabilitation of Finger Extension and Flexion Using a Rigid-Soft Combined Mechanism | Frontiers in Neurorobotics

 

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[WEB SITE] How to stretch your hands and wrists – Videos

Wrist pain can be frustrating and inconvenient. It can also make work or basic day-to-day activities, such as using a computer or cooking a meal, more difficult.

Exercises can improve mobility and decrease the chance of injury or reinjury. Wrist stretches are easy to do at home or at the office. When done properly, they can benefit a person’s overall wrist and hand health.

Anyone experiencing chronic pain or pain with numbness should visit a doctor for a thorough diagnosis.

The following stretches can help improve strength and mobility:

Wrist and hand stretches

A person should do the exercises below slowly and gently, focusing on stretching and strengthening. If the stretch hurts, stop.

The following wrist and hand stretches may improve strength and mobility:

1. Raised fist stretch

Raised fist stretch

To do this stretch:

  1. Start with your arm up beside your head, with your hand open.
  2. Make a fist, keeping your thumb outside of it.
  3. Slide your fingers toward your wrist until you feel a stretch.

2. Wrist rotations

Wrist rotations

To do this stretch:

  1. Stretch your arm out in front of you.
  2. Slowly, point the fingers down until you feel a stretch. Use the other hand to gently pull the raised hand toward the body. Hold this position for 3–5 seconds.
  3. Point the fingers toward the ceiling until you feel a stretch. Use the other hand to gently pull the raised hand toward the body. Hold this position for 3–5 seconds.
  4. Repeat this three times.

3. Prayer position

Prayer position

To do this stretch:

  1. Sit with your palms together and your elbows on the table in a prayer position.
  2. Lower the sides of the hands toward the table until you feel a stretch. Keep your palms together. Hold this position for 5–7 seconds.
  3. Relax.
  4. Repeat this three times.

4. Hooked stretch

Hooked stretch

To do this stretch:

  1. Hook one elbow under the other and pull both arms towards the center of the torso. You should feel a stretch in your shoulders.
  2. Wrap one arm around the other so that the palms are touching.
  3. Hold the position for 25 seconds.
  4. Switch arms and repeat it on the other side.

5. Finger stretch

finger stretch

To do this stretch:

  1. Bring the pinky and ring fingers together.
  2. Separate the middle and index fingers from the ring finger.
  3. Repeat the stretch 10 times.

6. Fist-opener

Fist opener

To do this stretch:

  1. Make a fist and hold it in front of you.
  2. Stretch your fingers until your hand is flat and open, with the fingers together.
  3. Repeat the movements 10 times.

7. Sponge-squeeze

Sponge squeeze

To do this stretch:

  1. Squeeze a sponge or stress ball, making a fist.
  2. Hold the position for 10 seconds.
  3. Relax.
  4. Repeat this 10 times.

8. Windshield wiper wrist movement

To do this stretch:

  1. Start with your hand face down on a table.
  2. Gently, point the hand to one side as far as it can go without moving the wrist. Hold it there for 3–5 seconds.
  3. Do the same on the other side.
  4. Repeat the movement three times on each side.

9. Thumb pull

To do this stretch:

  1. Grab your thumb with the other hand.
  2. Gently pull the thumb backward, away from the hand.
  3. Hold the stretch for 25 seconds.
  4. Repeat it on the other thumb.

10. Flower stretch

To do this stretch:

  1. Stretch the arms in front of you, with the backs of the hands and wrists touching.
  2. Imagine an invisible force pulling the fingers further from the body. Feel the stretch.
  3. Hold it for 25 seconds.

11. Finger fan

To do this stretch:

  1. Make a fist.
  2. Stretch your fingers outwards as far as they can go, like a fan.
  3. Repeat the movements 10 times.

12. Imaginary piano

To do this stretch:

  1. Pretend to play a piano.
  2. Flip your hands over and play an upside-down piano.

13. Finger pulls

To do this stretch:

  1. Lay your hand flat on a table.
  2. Gently pull a finger upward so that it points toward the ceiling.
  3. Hold the position for 5 seconds.
  4. Release the finger.
  5. Repeat this on all the other fingers.

14. Alternate finger stretch

To do this stretch:

  1. Bring the middle and ring fingers together.
  2. Separate the pinky and index fingers from them.
  3. Repeat the stretch 10 times.

15. Wrist-strengthener

To do this stretch:

  1. Get into position on your hands and knees, with the fingers pointing toward the body.
  2. Slowly lean forward, keeping your elbows straight.
  3. Hold the position for 20 seconds.
  4. Relax, then repeat the stretch.

Takeaway

Working with computers, writing, and doing manual labor put strain on the hands and wrists and can cause problems over time, such as tendonitis and carpal tunnel syndrome.

Taking frequent breaks and stretching before and while using the hands and wrists can help prevent strain. Improving flexibility and strength gradually can help people avoid wrist and hand injuries.

via Medical News Today: How to stretch your hands and wrists

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[JUST ACCEPTED] “A tablet-based tool for accurate measurement of hand proprioception after stroke” – Abstract

The following article has just been accepted for publication in Journal of Neurologic Physical Therapy: “A tablet-based tool for accurate measurement of hand proprioception after stroke”
By Hannah Justine Block, Ph.D.; Jasmine L Mirdamadi; Sydney Ryckman; Anna K Lynch; Reid Wilson; Divya Udayan; Crystal L Massie, PhD, OTR

Provisional Abstract:

Background and Purpose. Proprioceptive deficits in the hand are common following stroke, but current clinical measurement techniques are too imprecise to detect subtle impairments or small changes during rehabilitation. We developed a tablet-based tool to measure static hand proprioception using an adaptive staircase procedure. Here we compare the tablet with other methods in 16 chronic stroke survivors and age-matched controls.
Methods. We quantified proprioception at the metacarpophalangeal joint of the index finger of each hand using three methods: the tablet task, a custom passive motion direction discrimination test (PMDD), and a manual assessment similar to the Fugl-Meyer (F-M) proprioception subsection.
Results. Both the tablet and PMDD found impaired proprioception in the affected relative to the unaffected hand (p = 0.024 and 0.028) and relative to the control group (p = 0.040 and 0.032), while manual assessment did not. The PMDD had a ceiling effect as movements over 15° were not biomechanically feasible. The tablet and PMDD detected impaired proprioception in 56-75%, and the F-M in only 29%, of patients. PMDD and tablet measures were both correlated with primary tactile sensation, but not manual dexterity.
Discussion and Conclusions. Both tablet and custom PMDD performed better than manual assessment. PMDD may be useful when the deficit is mild or assessment of dynamic proprioception is desired. The tablet, lacking the PMDD’s ceiling effect, could be useful at any level of proprioceptive impairment, and may be preferable if testing or clinician training time needs to be minimized, or pain or spasticity is present.

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via JUST ACCEPTED: “A tablet-based tool for accurate measurement of hand proprioception after stroke”

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[ARTICLE] Effect of the combination of motor imagery and electrical stimulation on upper extremity motor function in patients with chronic stroke: preliminary results – Full Text

 

The combination of motor imagery (MI) and afferent input with electrical stimulation (ES) enhances the excitability of the corticospinal tract compared with motor imagery alone or electrical stimulation alone. However, its therapeutic effect is unknown in patients with hemiparetic stroke. We performed a preliminary examination of the therapeutic effects of MI + ES on upper extremity (UE) motor function in patients with chronic stroke.

A total of 10 patients with chronic stroke demonstrating severe hemiparesis participated. The imagined task was extension of the affected finger. Peripheral nerve electrical stimulation was applied to the radial nerve at the spiral groove. MI + ES intervention was conducted for 10 days. UE motor function as assessed with the Fugl–Meyer assessment UE motor score (FMA-UE), the amount of the affected UE use in daily life as assessed with a Motor Activity Log (MAL-AOU), and the degree of hypertonia in flexor muscles as assessed with the Modified Ashworth Scale (MAS) were evaluated before and after intervention. To assess the change in spinal neural circuits, reciprocal inhibition between forearm extensor and flexor muscles with the H reflex conditioning-test paradigm at interstimulus intervals (ISIs) of 0, 20, and 100 ms were measured before and after intervention.

UE motor function, the amount of the affected UE use, and muscle hypertonia in flexor muscles were significantly improved after MI + ES intervention (FMA-UE: p < 0.01, MAL-AOU: p < 0.01, MAS: p = 0.02). Neurophysiologically, the intervention induced restoration of reciprocal inhibition from the forearm extensor to the flexor muscles (ISI at 0 ms: p = 0.03, ISI at 20 ms: p = 0.03, ISI at 100 ms: p = 0.01).

MI + ES intervention was effective for improving UE motor function in patients with severe paralysis.

Upper motor dysfunction is a common problem in patients with stroke and disrupts activities of daily living and eventually worsens quality of life.1,2 Recently, several rehabilitation approaches have been developed to improve upper extremity (UE) motor function. Previous research has shown that intensive use of the paretic upper limb contributes to improved motor function, even though the motor recovery period has already passed.36 However, intensive use of the paretic upper limb is impossible for patients with severe upper limb paralysis, because they cannot voluntarily control the paretic hand. Therefore, other rehabilitative approaches for severely impaired patients are needed. As an alternative approach, motor imagery (MI) can be applied to patients regardless of the degree of motor paralysis. MI is defined as a dynamic state during which the representation of a given motor act is internally rehearsed within working memory without any overt motor output.7 Functional imaging studies have revealed that brain activity during motor execution and MI is largely shared in motor networks, such as the primary motor area, supplementary motor area, and premotor area.810 Also, transcranial magnetic stimulation (TMS) studies reported that excitability of the corticospinal tract (CST) is significantly higher during MI in comparison with baseline.1115 Based on these observations, MI has been applied for rehabilitation of patients with hemiparetic stroke, and the positive therapeutic effects on UE motor function have been reported.1620 However, the effect size differs among the studies,19 and is lower with regard to motor recovery of the paretic hand.20 To obtain clinically significant improvement, ingenuity to strengthen the therapeutic effect of MI is thought to be necessary.

The combination of MI and afferent input with electrical stimulation (ES) is an approach to enhance the therapeutic effect of MI. The effectiveness of ES for modulation of the excitability of the CST and improvement of dexterity performance of the paretic hand has been reported in patients with mild to moderate paralysis.21,22 Moreover, the additive effect of MI and ES has been reported in healthy adults. Saito and colleagues reported that a combination of MI and peripheral nerve ES enhances the excitability of the CST compared with MI alone or ES alone.23 In addition, Kaneko and colleagues reported that the combination of MI and electrical muscular stimulation reproduces the excitability of the CST at levels similar to voluntary muscle contraction.24 However, its therapeutic effects for motor function in patients with stroke are unknown. Therefore, we performed a preliminary examination of the therapeutic effects of a combination of MI and peripheral nerve ES (MI + ES) on UE motor function in patients with severe paralysis. The aim of this study is to investigate the feasibility and potential of the therapeutic effect for future randomized controlled trials.[…]

 

Continue —> Effect of the combination of motor imagery and electrical stimulation on upper extremity motor function in patients with chronic stroke: preliminary results – Kohei Okuyama, Miho Ogura, Michiyuki Kawakami, Kengo Tsujimoto, Kohsuke Okada, Kazuma Miwa, Yoko Takahashi, Kaoru Abe, Shigeo Tanabe, Tomofumi Yamaguchi, Meigen Liu, 2018

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Figure 1. The experimental setup of the intervention with combination of motor imagery and electrical stimulation (MI + ES).

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[BOOK Chapter] Application of a Robotic Rehabilitation Training System for Recovery of Severe Plegie Hand Motor Function after a Stroke – Full Text PDF

Abstract

We have developed a rehabilitation training system (UR-System-PARKO: Useful
and Ultimate Rehabilitation System-PARKO) for patients after a stroke to promote
recovery of motor function of the severe plegic hand with hemiplegia. A clinical
test with six patients for the therapeutic effect of the UR-System-PARKO for severe
plegic hand was performed. For all patients, the active ranges of motion (total
active motion) of finger extension improved after training with the UR-SystemPARKO. Moreover, the modified Ashworth scale (MAS) scores of finger extension
increased. Thus, the training reduced the spastic paralysis. These results suggest the
effectiveness of training with the UR-System-PARKO for recovery of motor function as defined by finger extension in the severe plegic hand.

1. Introduction

Stroke is the leading cause of disability in Japan, with more than 1 million people
in Japan living with a disability as a result of stroke. Therefore, interventions that
address the sensorimotor impairments resulting from stroke are important. Motor
function may be restored more than 6 months after a stroke [1, 2], but these studies
included patients with only moderate poststroke hemiplegia, whereas most stroke
survivors have a severely plegic hand with difficulty extending the fingers [3]. This
suggests that a method is needed for treatment of these severely affected cases.
However, although a few studies on rehabilitation therapy for severe plegic hands
have been reported, no marked recovery of ability in extension of the fingers of
the plegic hands was achieved in any study [4, 5]. Proprioceptive neuromuscular
facilitation (PNF) is a therapeutic method that was reported to increase the muscle
strength of the plegic extremities in patients with stroke-induced hemiplegia [6].
However, since PNF is indicated for patients with a certain level of joint motion,
this method has not been used for severe plegic hands where the fingers cannot
extend. Thus, the first author developed a method to build up the extensor digitorum muscle strength using PNF [7, 8] for stroke patients with severe hemiplegia.

With this therapy, he has performed repeated facilitation training using his hands
on stroke patients with a severe plegic hand to help them recover their motor function, and a good treatment outcome was achieved [9, 10] (Figure 1).
Facilitation training uses extension of the elbow joint with resistance applied to
the tips of the fully extended hemiplegic fingers to increase the force of the extensor digitorum muscle. However, this approach is time-consuming for the therapist.
Therefore, development of a training system is required instead of repeated
facilitation training by a therapist. The objectives of this study were to develop
a training system to increase the output of the extensor digitorum muscle force
and to verify the effect of training with the developed system on a severe plegic
hand. The training system is called the UR-System-PARKO (a useful and ultimate
rehabilitation support system for PARKO). The UR-System-PARKO was developed
by remodeling the simplified training system, which developed previously for
resistance training of hemiplegic upper limbs [11]. A brace for securing the plegic
hand to the UR-System-PARKO was developed on the basis of repeated facilitation
training by a therapist.[…]

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