Posts Tagged finger

[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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[Abstract] Design a solution and a prototype for hand rehabilitation after trauma injures and post stroke

Abstract

Hand injuries are common but if left untreated, it may result in loss of function. Common causes of upper limb injuries are Post Stroke or Trauma. Trauma include falls, cuts from knives or glass as well as workplace injuries. The impairment of finger movements after injures results in a significant deficit in hands everyday performances.

Rehabilitation helps the patient to regain the hands full functionality. Hand therapy is the art that fills the gap between surgery and practical life. It helps the patient to regain the hands full functionality after a certain injury, surgery or Stroke. Hand therapy could be a very tedious process that implies physical exhaustion. Rehabilitation at home is a long process . And it should be done under therapist control. Also finding appointments with the therapist frequent enough for an efficient healing process, is difficult and costly.

Since trying new technologies is usually exciting to people, using the advancements in the field of artificial intelligence could be a solution to this. Different rehabilitation techniques have been developed, nevertheless, they require the presence of a tutor to be executed. To overcome this issue have been designed several apparatuses that allow the patient to perform the training by itself. Trying new technologies is exciting to people.

Hand exoskeleton was implemented to help the patients do their exercises at home in an engaging gamified environment. The objective is to design a portable, lightweight exoskeleton with adjustment fast assemble system. The device support fingers and excluding second injuries. It reproduce pinch exercise. Thus, an easy to use and effective device is needed to provide the right training and complete the rehabilitation techniques in the best way.

In this paper, a review of state of the art in this field is provided, along with an introduc- tion to the problems caused by a hand injuries and the consequences for the mobility of the hand. Then follows a complete review of the exoskeleton project design. The objective is to design a device that can be used at home, with a lightweight and affordable structure and a fast mounting system. For implementing all these features, many aspects have been analysed, starting from the rehabilitation requirements and the ergonomic issues. This device should be able to reproduce the training movements on an injured hand without the need for assistance by an external tutor.

The control system is based on Arduino UNO board, and the user interface is based on UNITY, the objective is to create an online media that allows the patient to exploit the capabilities of the exoskeleton, following the indication of its medic. On the other side, this interface should provide all the data related to the performances of the patient to allow a more precise therapy.

via Design a solution and a prototype for hand rehabilitation after trauma injures and post stroke | POLITesi – Politecnico di Milano

 

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[WEB SITE] MoreGrasp: Getting a better grip on things

September 18, 2018 by Barbara Gigler, Graz University of Technology

The goal of the MoreGrasp project was to develop a sensoric grasp neuroprosthesis to support the daily life activities of people living with severe to completely impaired hand function due to spinal cord injuries. The motor function of a neuroprosthesis was to be intuitively controlled by means of a brain-computer interface with emphasis on natural motor patterns. After three years, the breakthrough was reported by the members of the project consortium led by Gernot Müller-Putz, head of the Institute of Neural Engineering at TU Graz, which include the University of Heidelberg, the University of Glasgow, the two companies Medel Medizinische Elektronik and Bitbrain as well as the Know Center.

Gernot Müller-Putz says, “In , all the circuits in the brain and muscles in the body parts concerned are still intact, but the neurological connection between the brain and limbs is interrupted. We bypass this by communicating via a computer, which in turn, passes on the command to the muscles.” The muscles are controlled and encouraged to move by electrodes that are attached to the outside of the arm and can, for example, trigger the closing and opening of the fingers. The key was the sufficient distinguishability of the brainwaves to control the neuroprosthesis. For instance, if the participant thought about raising and lowering their foot and the signal measured by the EEG opened the right hand, the subject then—for instance—would think of a movement of the left hand and the right hand would close again.

The MoreGrasp consortium developed this technique further. This mental ‘detour’ of any distinguishable movement pattern is no longer necessary, as Müller-Putz explains: “We now use so-called ‘attempted movement.'” In doing so, the test subject attempts to carry out a movement like grasping a glass of water. Due to the tetraplegia, the brain signal is not passed on, but can be measured by means of an EEG and processed by the computer system. Müller-Putz is extremely pleased with the success of the research. He says, “We are now working with signals that only differ from each other very slightly. Nevertheless, we have managed to control the neuroprosthesis successfully. For users, this results in a completely new possibility of making movement sequences easier—especially during training. A variety of grips were investigated in the project: the palmar grasp (cylinder grasp, as for grasping a glass), the lateral grasp (key grasp, as for picking up a spoon), and opening the hand and turning it inwards and outwards.

Large-scale study

End users can register on the special online platform to enter a large-scale feasibility study intended to check compatibility of the technique in everyday life. Participants eligible for the study will be tested according to a complex procedure. Afterward, each subject will be provided with a tailor-made BCI training course which must be completed independently in sessions lasting several hours each week. In this way, brain signals will be gathered and the system itself will learn during each experiment.

 Explore further: Potential brain-machine interface for hand paralysis

via MoreGrasp: Getting a better grip on things

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[Abstract] Properties of Mechatronic System for Hand Rehabilitation

ABSTRACT

The article describes an innovative mechatronic device for the hand rehabilitation, which enables diagnostics, comprehensive exercises and reporting of the results of rehabilitation of individual fingers of people who have lost their full efficiency as a result of past illnesses (i.a. stroke) and orthopedic injuries. The basic purpose of the device is to provide controlled, active exercises of the individual fingers, to widen the range of their movements, and to increase their precision of movement. The developed mechatronic device works with original software for PCs containing a diagnostic module, reporting module and a set of virtual reality exercises using biofeedback. The device uses auditory and visual biofeedback, and electromyography (EMG).

CORRESPONDING AUTHOR: Jacek Stanisław Tutak   
Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, al. Powstańców Warszawy 8, 35-959 Rzeszów, Poland

via Properties of Mechatronic System for Hand Rehabilitation

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[WEB PAGE] Gripping Aid for Small Items Helpful for Fine-Motor Activities

small item aid and palm pad

The Small Items gripping aid, new from UK-based Active Hands, is designed to enable users with reduced hand function or little or no finger strength to grip small items such as toothbrushes, pens, art items, razors, and makeup brushes with ease.

The gripping aid consists of two parts: a neoprene glove and a Velcro-backed palm pad with clamp. Items clamped in the palm pad can be placed into the glove at any angle, making a wide range of activities accessible, according to the company in a media release.

The clamp mechanism can be easily opened and closed to switch between items. Additional palm pads can also be purchased, enabling users to preload commonly used items and simply switch between the palm pads without having to remove the glove each time. In this way, the aids can promote greater autonomy in many aspects of daily living.

brenda small

“Our mission is to help people achieve more active and inclusive lives – giving them independent access to a variety of activities that would be impossible without Active Hands gripping aids. This new product delivers an effective solution to completing fine-motor activities with reduced grip; something our customers have been asking for,” says Active Hands Director Rob Smith, in the release.

For more information, visit Active Hands.

[Source: Active Hands]

via Gripping Aid for Small Items Helpful for Fine-Motor Activities – Rehab Managment

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