Posts Tagged Fingers

[Abstract] Assessment of the Ipsilesional Hand Function in Stroke Survivors: The Effect of Lesion Side 

Background

The aim of this study was to examine the effect of the side of brain lesion on the ipsilesional hand function of stroke survivors.

Methods

Twenty-four chronic stroke survivors, equally allocated in 2 groups according to the side of brain lesion (right or left), and 12 sex- and age-matched healthy controls performed the Jebsen-Taylor Hand Function Test (JTHFT), the Nine-Hole Peg Test (9HPT), the maximum power grip strength (PwGSmax) test, and the maximum pinch grip strength (PnGSmax) test. Only the ipsilesional hand of the stroke survivors and both hands (left and right) of the controls were assessed.

Results

PwGS max and PnGS max were similar among all tested groups. Performances in JTHFT and 9HPT were affected by the brain injury. Individuals with left brain damage showed better performance in 9HPT than individuals with right brain damage, but performance in JTHFT was similar.

Conclusions

Individuals after a brain injury have the capacity to produce maximum strength preserved when using their ipsilesional hand. However, the dexterity of their hands and digits is affected, in particular for stroke individuals with right brain lesion.

Source: Assessment of the Ipsilesional Hand Function in Stroke Survivors: The Effect of Lesion Side – Journal of Stroke and Cerebrovascular Diseases

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[WEB SITE] Your left hand knows what your right hand is doing

The saying goes that “your left hand doesn’t know what your right hand is doing,” but actually, your left hand is paying more attention than you’d think. Researchers at Tel-Aviv University found that when people practiced finger movements with their right hand while watching their left hand on 3D virtual reality headsets, they could use their left hand more efficiently after the exercise. The work, appearing in Cell Reports, provides a new strategy to improve physical therapy for people with limited strength in their hands.

“We are tricking the brain,” says lead author Roy Mukamel, a professor of psychology at Tel Aviv University in Israel. “This entire experiment ended up being a nice demonstration about how to combine software engineering and neuroscience.”

After completing baseline tests to assess the initial motor skills of each hand, 53 participants strapped on virtual reality headsets, which showed simulated versions of their hands. During the first experiment, the participants completed a series of finger movements with their right hand while the screen showed their virtual left hand moving instead. Next, the participants put a motorized glove on their left hand, which moved their fingers to match the motions of the right hand. While this occurred, the headsets again showed their virtual left hand moving instead of their right.

After analyzing the results, the researchers discovered that the left hand’s performance significantly improved (i.e., had more precise movements in a faster amount of time) when the screen showed the left hand. But the most notable improvements occurred when the virtual reality screen showed the left hand moving while the motorized glove moved the right hand in reality.

The researchers also used fMRI to track which brain structures were activated during the experiments in 18 of the participants. The scientists noted that one section of the brain, called the superior parietal lobe, was activated in each person during training. They also discovered that the level of activity in this brain region was correlated to the level of improved performance in the left hand–the more activity, the better the left hand performed.

“Technologically these experiments were a big challenge,” says Mukamel. “We manipulated what people see and combined it with the passive movement of the hand to show that our hands can learn when they’re not moving under voluntary control.”

The researchers are optimistic that this research can be applied to patients in physical therapy programs who have lost the strength or control of their hands. “We need to show a way to obtain high-performance gains relative to other traditional types of therapies,” says Mukamel. “If we can train one hand without voluntarily moving it and still show significant improvements in the motor skills of that hand, then that’s the ideal.”

This work was supported through the Sagol School of Neuroscience and School of Psychological Sciences at Tel-Aviv University in Israel.

Article: Neural Network Underlying Intermanual Skill Transfer in Humans, Ossmy and Mukamel, Cell Reports, 10.1016/j.celrep.2016.11.009, published 13 December 2016.

Source: Your left hand knows what your right hand is doing – Medical News Today

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[VIDEO] PABLO System Hand-Arm-Rehabilitation (English) – YouTube

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[WEB SITE] Finger Motion – tyromotion

Finger-Motion-AppFINGER MOTION

Exercises which bring feeling to your finger tips!

The new Finger Motion application was specifically developed for exercises  using individual fingers and the hand on the iPad. A variety of games, which can be played with individual or multiple fingers, are available. Furthermore, in the extended version the user can carry out exercises instructed by a deviation profile to improve motion control. The user also receives individual feedback on accuracy and execution after each exercise. Follow-up evaluations give insight into the number and intensity of games passed.

 Favorit at the Fast Forward Award 2015 for most innovative therapy app!

One App, many Benefits

The Fingermotion App allows clinics to be closer to their patients than ever, even after completion of the patients’ therapy programme. Simply create your own page and connect with your patients. The additional offer makes your clinic unique, attracts new target groups and increases revenue!

  • Free ad for your clinic on the front page – whatever you want, completely individual
  • Additional offers and services for patients
  • Motivating exercises for higher patient satisfaction

Fingermotion-Appstore

 

 

Image advertising for practices and clinics with Finger Motion

1. App StoreInvest in the start package and load app. You’ll receive a new iPad, your individual cover page and vouchers.

Finger-Motion-Laden

2. AdvertiseHand out vouchers for cheaper downloads to your patients and advertise your clinic directly on your patients’ iPad!

Finger-Motion-Werben

3. ProfitierenPosition yourself as an innovative clinic and provide your patients with more opportunities and more motivation.

Finger-Motion-Profit

 

The App – an Overview

Health and Fitness
Version: 2.0
15.5 MB
 German, English
Tyromotion GmbH
Requires iOS 7.0 oder newer.
Test version: € 0,-
Full version: € 2,99

iPad-App

 

 

 

 

 

 

 

 

 

Download test version for free

Source: Finger Motion

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[ARTICLE] Design of a Reconfigurable Robotic System for Flexoextension Fitted to Hand Fingers Size – Full Text PDF

Due to the growing demand for assistance in rehabilitation therapies for hand movements, a robotic system is proposed to mobilize the hand fingers in flexion and extension exercises. The robotic system is composed by four, type slider-crank, mechanisms that have the ability to fit the user fingers length from the index to the little finger, through the adjustment of only one link for each mechanism. The trajectory developed by each mechanism corresponds to the natural flexoextension path of each finger.
The amplitude of the rotations for metacarpophalangeal joint (MCP) and proximal interphalangeal joint (PIP) varies from 0 to 90∘ and the distal interphalangeal joint (DIP) varies from 0 to 60∘; the joint rotations are coordinated naturally. The four RRRT
mechanisms orientation allows a 15∘ abduction movement for index, ring, and little fingers. The kinematic analysis of this mechanism was developed in order to assure that the displacement speed and smooth acceleration into the desired range of motion
and the simulation results are presented. The reconfiguration of mechanisms covers about 95% of hand sizes of a group of Mexican adult population. Maximum trajectory tracking error is less than 3% in full range of movement and it can be compensated by the additional rotation of finger joints without injury to the user.

1. Introduction

The number of people with disabilities is increasing; thus, the demand of rehabilitation services is increasing too, due to the population growth and ageing, emerging chronic diseases, and the medical advances that preserve and extend life expectancy [1].The World Health Organization reported “an estimated 10% of the world’s population, some 650 million people, experience some form of impairment or disability”; about 80% of people with disabilities live in developing countries. The majority are poor and experience difficulties in accessing basic health services, including rehabilitation services [1], an alternative to address this problem is the use of robotic systems in rehabilitation therapies. Robotic systems have already proven to enhance hand therapies through incorporating intensive and interactive exercises [2,3]. Levanon confirms that “advanced technology can enrich treatment and can help patients who cannot come to the clinic regularly for treatment” [4]. “Disorders of the upper extremities specifically limit the independence of affected subjects” [5] and impairment of hand affects significantly
the execution of activities of daily living (ADL). There are injuries like fractures, sprains, and dislocations that cause temporary disability and they require mobilization exercises
as part of rehabilitation therapy [6]. Fasoli et al. concludes that “robotic therapy may complement other treatment approaches by reducing motor impairment in persons with
moderate to severe chronic impairments” [7]. On the other hand, Carey et al. concluded “that individuals with chronic stroke receiving intensive tracking training showed improved tracking accuracy and grasp and release function, and these improvements were accompanied by brain reorganization” [8]. Thus, Kitago et al. stablish that there is a great need to develop new approaches to rehabilitation of the upper limb after stroke. Robotic therapy is a promising form of  neurorehabilitation that can be delivered in higher doses
than conventional therapy [9]. Additionally, rehabilitation robots also can be a platform for quantitative monitoring on the recovery process in a rehabilitation program due to the standardized experimental setup and the high repeatability of motion tasks.
Different robotic devices for upper limb rehabilitation have been developed over the past two decades to provide hand motor therapy [5]. There are different design philosophies
applied to robotic therapies, determining the degrees of freedom considered and technologies used. The objective is to develop a training platform that helps patients regain hand range of motion and the ability to grasp objects, ultimately allowing the impaired hand to partake in activities of daily living [10].
In the specific case of the fingers of the hand, exoskeletons, wearable orthosis and gloves, haptic interfaces, and end effector-based devices have been developed and evaluated in order to facilitate the rehabilitation process [3, 5]. Exoskeletons are devices with a mechanical structure that mirrors the skeletal structure of the limb; that is, each segment of the limb associated with a joint movement is attached to the corresponding segment of the device. This design allows independent, concurrent, and precise control of movements
in a few limb joints. It is, however, more complex than an end-effector-based device [5]. An example of this approach is the HEXORR, Hand EXOskeleton Rehabilitation Robot [10].
This device has been designed to provide full range of motion (ROM) for all of the hand’s digits. The thumb actuator allows for variable thumb plane of motion to incorporate different degrees of extension-flexion and abduction-adduction. The finger four-bar linkage is driven by a direct current, brushless motor. The mechanisms of HEXORR only have one rotation axis for all the metacarpophalangeal joints for index to little fingers, but the rotation axes of the finger joints are not collinear. This device does not consider the distal interphalangeal joins of the fingers.
Glove devices are wearable, such as the robotic glove, which utilizes soft actuators consisting ofmolded elastomeric chambers with fiber reinforcements that induce specific
bending, twisting, and extending trajectories under fluid pressurization. These soft actuators were mechanically programmed to match and support the range of motion of
individual fingers [11].These devices require a pneumatic or hydraulic facility, which is more complex than electric supply, especially for domestic use. The variation in hand size can be a complication for the use of these devices.
The haptic devices form another group of systems interacting with the user through the sense of touch and the mobilization of the limb. Haptic devices can be classified as
either active or passive, depending on their type of actuator.
An example of this approach is the “haptic knob” which is a two-degree-of-freedom robotic interface to train movements and force control of wrist and hand. The “haptic knob” uses an actuated parallelogram structure that presents two movable surfaces that are squeezed by the subject [12]. This device is oriented to perform many ADL such as grasping and manipulating objects.
The advantage of the end-effector-based systems is their simpler structure and thus less complicated control algorithms.
However, it is difficult to isolate specific movements of a particular joint. The Rutgers Hand Master II is a force feedback glove powered by pneumatic pistons positioned in the palm of the hand and provides force feedback to the thumb, index, middle, and ring fingertips [13]. The fingertips develop a linear trajectory, whose amplitude depends on the
length of the pneumatic pistons. Amadeo is a commercially available device that provides endpoint control of each of the hand digits along linear fixed trajectories electric motor [14]. In this case, the fingertips develop a linear trajectory too.
The design of a reconfigurable robotic system proposed, Ro-Share, has advantages with respect to the devices mentioned.
First, it is designed so that each fingertip develops a natural flexoextension trajectory considering the joint coordination of each finger kinematic chain. Each of the
fingers is free to move without forcing the rotation axis alignment of its joints. Only one actuator is necessary for each mechanism that mobilizes one finger. Each mechanism can
be adjusted to the finger length by the length adjustment of its crank link. The variation of hand length can be up to 16% for a male and female adult from 18 to 90 years old specific
population [15].Hence, a robotic system to guide the fingertip of fingers, index, middle, ring, and little finger, in flexion and extension exercises is proposed, which must be able to fit finger sizes through only one link length adjustment.

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[Abstract] Brain–machine interfaces for rehabilitation of poststroke hemiplegia

Abstract

Noninvasive brain–machine interfaces (BMIs) are typically associated with neuroprosthetic applications or communication aids developed to assist in daily life after loss of motor function, eg, in severe paralysis. However, BMI technology has recently been found to be a powerful tool to promote neural plasticity facilitating motor recovery after brain damage, eg, due to stroke or trauma. In such BMI paradigms, motor cortical output and input are simultaneously activated, for instance by translating motor cortical activity associated with the attempt to move the paralyzed fingers into actual exoskeleton-driven finger movements, resulting in contingent visual and somatosensory feedback. Here, we describe the rationale and basic principles underlying such BMI motor rehabilitation paradigms and review recent studies that provide new insights into BMI-related neural plasticity and reorganization. Current challenges in clinical implementation and the broader use of BMI technology in stroke neurorehabilitation are discussed.

 

Source: Brain–machine interfaces for rehabilitation of poststroke hemiplegia

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[Abstract] Benefits of using a voice and EMG-driven actuated glove to support occupational therapy for stroke survivors.

Many mechatronic devices exist to facilitate hand rehabilitation, however few directly address deficits in muscle activation patterns while also enabling functional task practice.

We developed an innovative voice and electromyography-driven actuated (VAEDA) glove, which is sufficiently flexible/portable for incorporation into hand-focused therapy post-stroke. The therapeutic benefits of this device were examined in a longitudinal intervention study. Twenty-two participants with chronic, moderate hand impairment (Chedoke-McMaster Stroke Assessment Stage of Hand (CMSA-H=4)) enrolled >8 months post-stroke for 18 one-hour training sessions (3x/week) employing a novel hand-focused occupational therapy paradigm, either with (VAEDA) or without (No-VAEDA) actuated assistance.

Outcome measures included CMSA-H, Wolf Motor Function Test (WMFT), Action Research Arm Test, Fugl-Meyer Upper Extremity Motor Assessment (FMUE), grip and pinch strength and hand kinematics. All outcomes were recorded at baseline and endpoint (immediately after and 4 weeks post-training).

Significant improvement was observed following training for some measures for the VAEDA group (n=11) but for none of the measures for the No-VAEDA group (n=11). Specifically, statistically significant gains were observed for CMSA-H (p=0.038) and WMFT (p=0.012) as well as maximum digit aperture subset (p=0.003, n=7), but not for the FMUE or grip or pinch strengths.

In conclusion, therapy effectiveness appeared to be increased by employment of the VAEDA glove, which directly targets deficits in muscle activation patterns.

Source: IEEE Xplore Abstract (Abstract) – Benefits of using a voice and EMG-driven actuated glove to support occupational therapy for stroke s…

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[Abstract] A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application. 

Abstract

This paper presents a soft robotic glove designed to assist individuals with functional grasp pathologies in performing activities of daily living. The glove utilizes soft fabric-regulated pneumatic actuators that are low-profile and require lower pressure than previously developed actuators. They are able to support fingers and thumb motions during hand closure. Upon pressurization, the actuators are able to generate sufficient force to assist in hand closing and grasping during different manipulation tasks. In this work, experiments were conducted to evaluate the performances of the actuators as well as the glove in terms of its kinetic and kinematic assistance on a healthy participant. Additionally, surface electromyography and radio-frequency identification techniques were adopted to detect user intent to activate or deactivate the glove. Lastly, we present preliminary results of a healthy participant performing different manipulation tasks with the soft robotic glove controlled by surface electromyography and radio-frequency identification techniques.

Source: IEEE Xplore Abstract (Abstract) – A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assisti…

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[VIDEO] Amadeo Product Film – YouTube

The AMADEO is the latest in a long row of clinically tried and tested robotic- and computer-assisted therapy devices for fingers and hands. The new design and the specially developed tyroS software make the AMADEO more flexible and offer an expanded spectrum of therapy options.

 

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[Abstract] Deficits in motor abilities for multi-finger force control in hemiparetic stroke survivors.

Abstract

The ability to control redundant motor effectors is one of hallmarks in human motor control, and the topic has been studied extensively over several decades since the initial inquiries proposed by Nicholi Bernstein. However, our understanding of the influence of stroke on the control of redundant motor systems is very limited.

This study aimed to investigate the effect of stroke-related constraints on multi-finger force control abilities in a visuomotor task. Impaired (IH) and less-impaired hands (LH) of 19 hemiparetic stroke survivors and 19 age-matched control subjects were examined. Each hand repeatedly produced isometric forces to match a target force of 5 N shown on a computer screen using all four fingers. The hierarchical variability decomposition (HVD) model was used to separate force-matching errors (motor performance) into task-relevant measures (accuracy, steadiness, and reproducibility). Task-irrelevant sources of variability in individual finger force profiles within and between trials (flexibility and multiformity) were also quantified. The IH in the stroke survivors showed deficits in motor performance attributed mainly to lower accuracy and reproducibility as compared to control hands (p < 0.05). The LH in stroke survivors showed lower reproducibility and both hands in stroke also had higher multiformity than the control hands (p < 0.05).

The findings from our HVD model suggest that accuracy, reproducibility, and multiformity were mainly impaired during force-matching task in the stroke survivors. The specific motor deficits identified through the HVD model with the new conceptual framework may be considered as critical factors for scientific investigation on stroke and evidence-based rehabilitation of this population.

Source: Deficits in motor abilities for multi-finger force control in hemiparetic stroke survivors – Online First – Springer

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