Posts Tagged hemiparesis

[Abstract] A soft supernumerary robotic finger and mobile arm support for grasping compensation and hemiparetic upper limb rehabilitation

Abstract

In this paper, we present the combination of our soft supernumerary robotic finger i.e. Soft-SixthFinger with a commercially available zero gravity arm support, the SaeboMAS. The overall proposed system can provide the needed assistance during paretic upper limb rehabilitation involving both grasping and arm mobility to solve task-oriented activities. The Soft-SixthFinger is a wearable robotic supernumerary finger designed to be used as an active assistive device by post stroke patients to compensate the paretic hand grasp. The device works jointly with the paretic hand/arm to grasp an object similarly to the two parts of a robotic gripper. The SaeboMAS is a commercially available mobile arm support to neutralize gravity effects on the paretic arm specifically designed to facilitate and challenge the weakened shoulder muscles during functional tasks. The proposed system has been designed to be used during the rehabilitation phase when the arm is potentially able to recover its functionality, but the hand is still not able to perform a grasp due to the lack of an efficient thumb opposition. The overall system also act as a motivation tool for the patients to perform task-oriented rehabilitation activities.

With the aid of proposed system, the patient can closely simulate the desired motion with the non-functional arm for rehabilitation purposes, while performing a grasp with the help of the Soft-SixthFinger. As a pilot study we tested the proposed system with a chronic stroke patient to evaluate how the mobile arm support in conjunction with a robotic supernumerary finger can help in performing the tasks requiring the manipulation of grasped object through the paretic arm. In particular, we performed the Frenchay Arm Test (FAT) and Box and Block Test (BBT). The proposed system successfully enabled the patient to complete tasks which were previously impossible to perform.

Source: A soft supernumerary robotic finger and mobile arm support for grasping compensation and hemiparetic upper limb rehabilitation

, , , , , , , , , , ,

Leave a comment

[ARTICLE] Hybrid Assistive Neuromuscular Dynamic Stimulation Therapy: A New Strategy for Improving Upper Extremity Function in Patients with Hemiparesis following Stroke – Full Text

Abstract

Hybrid Assistive Neuromuscular Dynamic Stimulation (HANDS) therapy is one of the neurorehabilitation therapeutic approaches that facilitates the use of the paretic upper extremity (UE) in daily life by combining closed-loop electromyography- (EMG-) controlled neuromuscular electrical stimulation (NMES) with a wrist-hand splint. This closed-loop EMG-controlled NMES can change its stimulation intensity in direct proportion to the changes in voluntary generated EMG amplitudes recorded with surface electrodes placed on the target muscle. The stimulation was applied to the paretic finger extensors. Patients wore a wrist-hand splint and carried a portable stimulator in an arm holder for 8 hours during the daytime. The system was active for 8 hours, and patients were instructed to use their paretic hand as much as possible. HANDS therapy was conducted for 3 weeks. The patients were also instructed to practice bimanual activities in their daily lives. Paretic upper extremity motor function improved after 3 weeks of HANDS therapy. Functional improvement of upper extremity motor function and spasticity with HANDS therapy is based on the disinhibition of the affected hemisphere and modulation of reciprocal inhibition. HANDS therapy may offer a promising option for the management of the paretic UE in patients with stroke.

1. Functional Recovery of Upper Extremity Motor Function following Stroke

Stroke is a common health-care problem that causes physical impairment, disability, and problems in social participation. The most common impairment caused by stroke is motor impairment. Motor impairment affects the control of the unilateral upper and lower extremities. Recovery of function in the hemiparetic upper extremity is noted in fewer than 15% of patients after stroke [1].

Patients often compensate for their paretic upper extremity by using their intact upper extremity in the performance of everyday tasks [2]. It is supposed that strong reliance on compensatory overuse of the intact upper extremity inhibits functional recovery of the impaired upper extremity. This may explain the limited improvement of the functional capability of the paretic upper extremity in activities of daily living (ADL).

Principles of motor rehabilitation following stroke have been described as being dose-dependent and task-specific [3]. High-intensity practice and task-specific training are recommended for functional recovery. Several systematic reviews [4, 5] have explored whether high-intensity therapy improves recovery, and the principle that increased intensive training is helpful is widely accepted. Task-specific training is a well-accepted principle in motor rehabilitation. Training should target the goals that are relevant for the needs of the patients and preferably be given in the patient’s own environment.

The goal of upper extremity rehabilitation is to improve the capability of the paretic upper extremity for ADL. Constraint-induced movement therapy (CIMT) has been developed to enhance the forced use of the paretic hand in ADL with reduction of the compensatory overuse of the intact upper extremity. However, to participate in CIMT, the candidates must be able to voluntary extend their fingers and wrist at least 10 degrees, practice for 6 hours daily in a 2-week course, and spend waking hours with their nonparetic hand in a mitt [6].

To counter potential problems inherent in the intensive services needed for CIMT, we developed an alternative therapeutic approach that provides high-intensity training to facilitate the use of the paretic upper extremity in daily living by combining closed-loop electromyography- (EMG-) controlled neuromuscular electrical stimulation (NMES) with a wrist-hand splint for patients with moderate to severe hemiparesis. Fujiwara et al. called this hybrid assistive neuromuscular dynamic stimulation (HANDS) therapy [7].

2. HANDS Therapy

A PubMed literature search was conducted using the MeSH terms stroke, rehabilitation, upper extremity function, and neuromuscular electrical stimulation, and 71 articles were identified. A further search of PubMed with the terms stroke, rehabilitation, upper extremity function, neuromuscular electrical stimulation, and splint identified 4 articles, all regarding HANDS therapy.

HANDS therapy facilitates the use of the paretic upper extremity in daily living by combining closed-loop EMG-controlled NMES with a wrist-hand splint for patients with moderate to severe hemiparesis. This HANDS system is active for 8 hours, and patients are instructed to use their paretic hand as much as possible while wearing the HANDS system. Their nonparetic upper extremity is not restrained. The patients are also instructed to practice bimanual activities in their ADL. All participants in HANDS therapy are admitted, and the length of the intervention is 21 days. They receive 90 minutes of occupational therapy per day, 5 days a week. Each session of occupational therapy consists of gentle stretching exercise of the paretic upper extremity and active muscle reeducation exercise. All participants are instructed how to use their paretic hand in ADL with the HANDS system. Occupational therapists are directed toward participants’ goals and focused on their particular impairments and disabilities; thus, the specific therapy that each patient receives varies [7, 8].

Fujiwara et al. [7, 8] reported the indications for HANDS therapy as follows: () no cognitive deficits; () no pain in the paretic upper extremity; () passive extension range of motion (ROM) greater than 0 degrees of the affected wrist and −10 degrees of the metacarpophalangeal joints; () detectable surface EMG signals in the affected extensor digitorum communis (EDC) or extensor pollicis longus (EPL) when the patient intends to extend their fingers; () ability to raise the paretic hand to the height of the nipple; () scores of Fugl-Meyer test position sense of joints in the glenohumeral joint, elbow, wrist, and thumb of 1 or more; and () the ability to walk without physical assistance in daily life (e.g., including patients who can walk independently with a cane and/or an orthosis). The exclusion criteria were () history of major psychiatric or previous neurological disease, including seizures; () cognitive impairment precluding appropriately giving informed consent or the patient’s Mini Mental Examination Scale score was below 25; () patients with severe pain in the paretic upper extremity; () patients with a pacemaker or other implanted stimulator; and () patients with visuospatial neglect or apraxia.

Previous reports showed that none of the patients experienced any discomfort or significant disability with the HANDS therapy.

2.1. Closed-Loop Electromyography- (EMG-) Controlled Neuromuscular Electrical Stimulation (NMES)

Twenty-nine articles were found in PubMed using the terms stroke, electromyography, neuromuscular electrical stimulation, and upper extremity. Thirteen of 29 articles were on EMG-triggered NMES. Six of 29 articles were on EMG-controlled NMES. Two involved contralaterally controlled electrical stimulation.

EMG-triggered NMES applies preset electrical stimulation when EMG activity reaches a target threshold. The stimulus intensity and duration are determined and not changeable. EMG-controlled NMES applies electrical stimulation during voluntary contraction and changes the stimulation intensity in proportion to the changes in EMG amplitude.

For assistive stimulation, HANDS therapy used closed-loop EMG-controlled NMES, which was developed by Muraoka [9] and commercially available with MURO stimulation (Pacific Supply, Osaka, Japan). This closed-loop EMG-controlled NMES is portable and attaches to the arm (Figure 1). The surface electrodes pick up EMG signals at the target muscle and simultaneously stimulate it in direct proportion to the picked-up EMG signal, with the exception of the 25 ms after delivering each stimulation pulse, in which stimulation artifacts and M wave are observed. The external adjustment unit sets () range of stimulus intensity; () sensitivity of the EMG; () threshold of EMG amplitude that starts stimulation; and () gradient of stimulus intensity change to the change of EMG amplitude. Once these parameters were set with the external adjustment unit, the stimulator memorized these parameters.

Continue —>  Hybrid Assistive Neuromuscular Dynamic Stimulation Therapy: A New Strategy for Improving Upper Extremity Function in Patients with Hemiparesis following Stroke

, , , , , , , ,

Leave a comment

[Abstract] Clinically Important Difference of the Arm Motor Ability Test in Stroke Survivors 

Background. The Arm Motor Ability Test (AMAT) is used to assess and quantify upper-extremity (UE) functional limitation in stroke and other conditions. However, the AMAT score change indicative of important and clinically meaningful change has not been determined.

Objective. To determine the clinically important difference (CID) for the AMAT for individuals with stroke exhibiting mild to moderate hemiparesis.

Methods. A total of 146 chronic stroke survivors exhibiting stable, mild to moderate UE hemiparesis were administered the AMAT before and after interventions targeting their affected UEs. Patients and treating therapists rated perceived amount of UE motor recovery for each participant on a global rating of change (GROC) scale evaluating several facets of UE movement (grasp, release, move the affected UE, perform 5 important functional tasks, overall UE function). Estimated CID of the Functional Ability Scale of the AMAT was calculated using the receiver operating characteristics curve with the GROC scale as the anchor. Distribution-based methods were also used to estimate the CID.

Results. Mean baseline, postintervention, and change in AMAT values for all participants were 3.0 (0.68), 3.3 (0.73), and 0.33 (0.43) respectively. The CID was estimated as an improvement of 0.32 to 0.42 when anchored by the therapist’s perception of improvement and 0.29 to 0.40 when anchored by the patient’s perception of improvement. The CID using distribution-based methods ranged from 0.40 to 0.44.

Conclusions. A change of 0.44 or greater on the AMAT indicates a clinically meaningful improvement in UE functional movements. Clinicians should use this value to determine goals and interpret change scores.

Source: Clinically Important Difference of the Arm Motor Ability Test in Stroke Survivors – Mar 01, 2017

, , , , , , , ,

Leave a comment

[WEB SITE] Virtual reality intervention shows promise to repair mobility and motor skills in impaired limb

A combination of traditional physical therapy and technology may improve the motor skills and mobility of an impaired hand by having its partner, more mobile hand lead by example through virtual reality training, new Tel Aviv University research suggests.

“Patients suffering from hemiparesis — the weakness or paralysis of one of two paired limbs — undergo physical therapy, but this therapy is challenging, exhausting, and usually has a fairly limited effect,” said lead investigator Prof. Roy Mukamel of TAU’s School of Psychological Sciences and Sagol School of Neuroscience, who conducted the research with his student Ori Ossmy. “Our results suggest that training with a healthy hand through a virtual reality intervention provides a promising way to repair mobility and motor skills in an impaired limb.” The research was published in Cell Reports.

Does the left hand know what the right hand is doing?

53 healthy participants completed baseline tests to assess the motor skills of their hands, then strapped on virtual reality headsets that showed simulated versions of their hands. The virtual reality technology, however, presented the participants with a “mirror image” of their hands — when they moved their real right hand, their virtual left hand would move.

In the first experiment, participants completed a series of finger movements with their right hands, while the screen showed their “virtual” left hands moving instead. In the next, participants placed motorized gloves on their left hands, which moved their fingers to match the motions of their right hands. Again, the headsets presented the virtual left hands moving instead of their right hands.

The research team found that when subjects practiced finger movements with their right hands while watching their left hands on 3D virtual reality headsets, they could use their left hands more efficiently after the exercise. But the most notable improvements occurred when the virtual reality screen showed the left hand moving while in reality the motorized glove moved the hand.

Tricking the brain

“We effectively tricked the brain,” said Prof. Mukamel.

“Technologically, these experiments were a big challenge,” Prof. Mukamel continued. “We manipulated what people saw and combined it with the passive, mechanical movement of the hand to show that our left hand can learn even when it is not moving under voluntary control.”

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

The researchers are currently examining the applicability of their novel VR training scheme to stroke patients.

Source: American Friends of Tel Aviv University

Source: Virtual reality intervention shows promise to repair mobility and motor skills in impaired limb

, , , , , , , ,

Leave a comment

[Abstract] Effects of treadmill training with load addition on non-paretic lower limb on gait parameters after stroke: a randomized controlled clinical trial

Highlights

  • Load use as a restraint for the movement of non-paretic lower limb is proposed.
  • Currently, only immediate effects of this practice are available.
  • Stroke patients performed gait training with and without load addition, for two weeks.
  • Kinematic gait parameters were improved after training and maintained at follow-up.
  • Load addition did not provide additional benefits to gait training.

Abstract

The addition of load on the non-paretic lower limb for the purpose of restraining this limb and stimulating the use of the paretic limb has been suggested to improve hemiparetic gait. However, the results are conflicting and only short-term effects have been observed.

This study aims to investigate the effects of adding load on non-paretic lower limb during treadmill gait training as a multisession intervention on kinematic gait parameters after stroke.

With this aim, 38 subacute stroke patients (mean time since stroke: 4.5 months) were randomly divided into two groups: treadmill training with load (equivalent to 5% of body weight) on the non-paretic ankle (experimental group) and treadmill training without load (control group). Both groups performed treadmill training during 30 minutes per day, for two consecutive weeks (nine sessions). Spatiotemporal and angular gait parameters were assessed by a motion system analysis at baseline, post-training (at the end of 9 days of interventions) and follow-up (40 days after the end of interventions).

Several post-training effects were demonstrated: patients walked faster and with longer paretic and non-paretic steps compared to baseline, and maintained these gains at follow-up. In addition, patients exhibited greater hip and knee joint excursion in both limbs at post-training, while maintaining most of these benefits at follow-up. All these improvements were observed in both groups.

Although the proposal gait training program has provided better gait parameters for these subacute stroke patients, our data indicate that load addition used as a restraint may not provide additional benefits to gait training.

Source: Effects of treadmill training with load addition on non-paretic lower limb on gait parameters after stroke: a randomized controlled clinical trial – Gait & Posture

, , , , , ,

Leave a comment

[ARTICLE] Efficacy of home-based visuomotor feedback training in stroke patients with chronic hemispatial neglect – Full Text

Hemispatial neglect is a severe cognitive condition frequently observed after a stroke, associated with unawareness of one side of space, disability and poor long-term outcome. Visuomotor feedback training (VFT) is a neglect rehabilitation technique that involves a simple, inexpensive and feasible training of grasping-to-lift rods at the centre. We compared the immediate and long-term effects of VFT vs. a control training when delivered in a home-based setting. Twenty participants were randomly allocated to an intervention (who received VFT) or a control group (n = 10 each). Training was delivered for two sessions by an experimenter and then patients self-administered it for 10 sessions over two weeks. Outcome measures included the Behavioural Inattention Test (BIT), line bisection, Balloons Test, Landmark task, room description task, subjective straight-ahead pointing task and the Stroke Impact Scale. The measures were obtained before, immediately after the training sessions and after four-months post-training. Significantly greater short and long-term improvements were obtained after VFT when compared to control training in line bisection, BIT and spatial bias in cancellation. VFT also produced improvements on activities of daily living. We conclude that VFT is a feasible, effective, home-based rehabilitation method for neglect patients that warrants further investigation with well-designed randomised controlled trials on a large sample of patients.

Continue —> Efficacy of home-based visuomotor feedback training in stroke patients with chronic hemispatial neglect: Neuropsychological Rehabilitation: Vol 0, No 0

Figure

Figure 3 of 5 Figure 3. (A) Lesion map for individual patients. B-C) Lesion overlap map summarising the degree of involvement for each voxel in the intervention (B; N = 8) and control (C; N = 5) groups. Lesions were identified by a clinical neurologist (K.M.), who was blind to the design, group assignment and purpose of the study. Lesions were mapped onto 11 axial slices of a T1-weighted template, corresponding to the MNI z coordinates of −24, −16, −8, 0, 8, 16, 24, 32, 40, 50, 60 mm using identical or closest matching transverse slices for each patient using MRIcro software package (Rorden & Brett, 2000 Rorden, C., & Brett, M. (2000). Stereotaxic display of brain lesions. Behavioural Neurology, 12, 191–200. doi: 10.1155/2000/421719 [CrossRef], [PubMed], [Web of Science ®] ). Due to technical difficulties at the clinical facility, we were able to obtain and map digital brain scans for 13 patients only (6 MRIs and 7 CTs) as the remaining digital brain scans were either lost or corrupted. Please note however, that all brain scan reports were available and confirmed the presence of a stroke and its location for all our patients. The range of colour scale derives from the absolute number of patient lesions involved in each voxel.

, , , , , , , ,

Leave a comment

[ARTICLE] Motor Learning in Stroke – Full Text

Background and Objective: Stroke rehabilitation assumes motor learning contributes to motor recovery, yet motor learning in stroke has received little systematic investigation. Here we aimed to illustrate that despite matching levels of performance on a task, a trained patient should not be considered equal to an untrained patient with less impairment. Methods: We examined motor learning in healthy control participants and groups of stroke survivors with mild-to-moderate or moderate-to-severe motor impairment. Participants performed a series of isometric contractions of the elbow flexors to navigate an on-screen cursor to different targets, and trained to perform this task over a 4-day period. The speed-accuracy trade-off function (SAF) was assessed for each group, controlling for differences in self-selected movement speeds between individuals. Results: The initial SAF for each group was proportional to their impairment. All groups were able to improve their performance through skill acquisition. Interestingly, training led the moderate-to-severe group to match the untrained (baseline) performance of the mild-to-moderate group, while the trained mild-to-moderate group matched the untrained (baseline) performance of the controls. Critically, this did not make the two groups equivalent; they differed in their capacity to improve beyond this matched performance level. Specifically, the trained groups had reached a plateau, while the untrained groups had not. Conclusions: Despite matching levels of performance on a task, a trained patient is not equal to an untrained patient with less impairment. This has important implications for decisions both on the focus of rehabilitation efforts for chronic stroke, as well as for returning to work and other activities.

Stroke is a leading cause of adult disability, leaving 30% to 66% of patients with lasting motor impairment.1,2 It has long been proposed that motor recovery following stroke is a form of relearning3,4 and that there is considerable overlap between the brain regions involved in both processes.57 However, while acquiring skill at a task may allow a patient to perform at the same level as an individual with lesser impairment, this does not necessarily make them equal. For example, well-recovered stroke patients can match the performance of healthy controls on a motor task, but differences exist in the neural networks that underlie performance for each group.8 Furthermore, matched performance does not necessarily imply that both groups have the same ability to continue improving given the opportunity for practice. These differences can complicate judgments regarding patients’ capacity to return to work and other activities,9 and which rehabilitation activities they should focus on. In this article, we propose that acquiring skill through motor training raises a similar issue—a patient who has trained on a task may “appear better,” masking categorical differences in his or her abilities. Consider two hypothetical patients—Patient A, who has mild motor impairment, and Patient B, who is more severely impaired. Patient A performs better in a movement task than Patient B. Patient B then trains at the task, reaching the same performance level as Patient A. If Patient B is now equal to Patient A, he or she should have a similar capacity for further improvement with training. If this is not the case (eg, if Patient B has reached a performance plateau beyond which further training has a limited effect), then a categorical difference remains between these patients despite their matching task performance.

In comparison to healthy individuals, stroke patients select slower voluntary movement speeds when performing movement tasks.10 As speed and accuracy are inherently linked,11 a confound arises when comparing the accuracy of movements performed at different speeds. This limitation makes it difficult to interpret previous results, such as cases where patients improve their accuracy yet decrease their speed.12 In such cases, it is impossible to determine whether a patient improved his or her ability to perform the task (through skill acquisition) or whether he or she simply changed the aspect of performance on which they focused (eg, sacrificed speed for accuracy while remaining at the same overall level of ability). The only way to disambiguate these alternatives is to first derive the speed-accuracy trade-off function (SAF13) for a given task; participants are required to complete the task in a fixed time, allowing accuracy to be measured without the confounding effects of differences in speed. Once derived, skill represents a shift in the SAF.1315

Here we introduce a serial voluntary isometric elbow force task, a modified version of the serial voluntary isometric pinch task (SVIPT). This task is based on an established laboratory-based model of motor learning in which participants learn to control a cursor by producing isometric forces.1319 In the task used in the present study, participants controlled a cursor by exerting forces with their elbow flexor muscles, allowing comparisons of performance across participants with greater ranges of impairment than would be possible with the standard (hand controlled) SVIPT paradigm. To control for differences in movement speeds across groups, performance was assessed by comparing the speed-accuracy trade-off pre and post training, using measures of task-level performance (ie, binary success/failure to complete all specified aspects of the task)1318 and trial-level measures of endpoint error and variability.20 We predicted that the severity of a participant’s motor impairment would limit his or her ability to perform the task and that training may allow him or her to achieve a similar level of performance as an individual with lesser impairment. However, we hypothesized that despite their matching performance, there would be a categorical difference between these individuals; the previously untrained participant with lesser impairment would be able to make large, rapid improvements through training, while the trained participant would not.

Figure 1. Experimental setup and procedure. (A) Participants sat with their (affected) arm supported by a robotic exoskeleton. A force transducer measured contractions of their elbow flexors. (B) On screen display. Contracting the elbow flexors moved the cursor (white circle) to the right, while relaxing moved the cursor to the home position (grey square). A “go” indicator (used in training trials) indicated to participants that they could begin a trial when ready (illustrated here as a green circle). Each trial involved navigating the cursor through the sequence Home-1–Home-2–Home-3–Home-4–Home-5. Target positions and sequence order remained fixed throughout the study. (C) Procedure. Participants first completed a pretraining skill assessment, performing the task at trial durations set by an auditory metronome (indicated by tempos presented in beats per minute—see main text for further detail). One “run” of the task involved completing 10 trials at each tempo in a pseudorandom order. This procedure was repeated to generate 2 runs of data (ie, a total of 20 trials for each tempo). Participants later trained to perform the task over consecutive days, aiming to complete the sequence as quickly and as accurately as possible. Finally, on a separate day, participants completed a posttraining skill assessment.

Continue —> Motor Learning in Stroke – Oct 27, 2016

, , , , , , , ,

Leave a comment

[Abstract] Functional outcome, Rehabilitation, Upper extremity function

Abstract

Introduction: Paretic upper limb in stroke patients has a significant impact on the quality of life. Modified Constraint Induced Movement Therapy (mCIMT) is one of the treatment options used for the improvement of the function of the paretic limb.

Aim: To investigate the efficacy of four week duration mCIMT in the management of upper extremity weakness in hemiparetic patients due to stroke.

Materials and Methods: Prospective single blind, parallel randomized controlled trial in which 30 patients received conventional rehabilitation programme (control group) and 30 patients participated in a mCIMT programme in addition to the conventional rehabilitation programme (study group). The mCIMT included three hours therapy sessions emphasizing the affected arm use in general functional tasks, three times a week for four weeks. Their normal arm was also constrained for five hours per day over five days per week. All the patients were assessed at baseline, one month and three months after completion of therapy using Fugl-Meyer Assessment (FMA) score for upper extremity and Motor Activity Log (MAL) scale comprising of Amount of Use (AOU) score and Quality of Use (QOU) score.

Results: All the 3 scores improved significantly in both the groups at each follow-up. Post-hoc analysis revealed that compared to conventional rehabilitation group, mCIMT group showed significantly better scores at 1 month {FMA1 (p-value <0.0001, es0.2870), AOU1 (p-value 0.0007, es0.1830), QOU1 (p-value 0.0015, es0.1640)} and 3 months {FMA3 (p-value <.0001, es0.4240), AOU3 (p-value 0.0003, es 0.2030), QOU3 (p-value 0.0008, es 0.1790)}.

Conclusion: Four weeks duration for mCIMT is effective in improving the motor function in paretic upper limb of stroke patients.

Source: JCDR – Functional outcome, Rehabilitation, Upper extremity function

, , , , , , , , ,

Leave a comment

[ARTICLE] Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation – Full Text

Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (p < 0.001) in accordance with the subjects’ abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (p = 0.008), accuracy of movement (p = 0.01), and movement velocity (p < 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (p = 0.001), grip force (p < 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (p = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.

Introduction

Despite their participation in standard rehabilitation programs (Jørgensen et al., 1999; Dobkin, 2005), restoration of arm and hand function for activities of daily living is not achieved in the majority of stroke patients. In the first weeks and months after stroke, a positive relationship between the dose of therapy and clinically meaningful improvements has been demonstrated (Lohse et al., 2014; Pollock et al., 2014). In stroke patients with long-standing (>6 months) upper limb paresis, however, treatment effects were small, with no evidence of a dose-response effect of task-specific training on the functional capacity (Lang et al., 2016). This has implications for the use of assistive technologies such as robot-assisted training during stroke rehabilitation. These devices are usually applied to further increase and standardize the amount of therapy. They have the potential to improve arm/hand function and muscle strength, albeit currently available clinical trials provide on the whole only low-quality evidence (Mehrholz et al., 2015). It has, notably, been suggested that technology-assisted improvements during stroke rehabilitation might at least partially be due to unspecific influences such as increased enthusiasm for novel interventions on the part of both patients and therapists (Kwakkel and Meskers, 2014). In particular, a comparison between robot-assisted training and dose-matched conventional physiotherapy in controlled trials revealed no additional, clinically relevant benefits (Lo et al., 2010; Klamroth-Marganska et al., 2014). This might be related to saturation effects. Alternatively, the active robotic assistance might be too supportive when providing “assistance-as-needed” during the exercises (Chase, 2014). More targeted assistance might therefore be necessary during these rehabilitation exercises to maintain engagement without compromising the patients’ motivation; i.e., by providing only as much support as necessary and as little as possible (Grimm and Gharabaghi, 2016). In this context, passive gravity compensation with a multi-joint arm exoskeleton may be a viable alternative to active robotic assistance (Housman et al., 2009; Grimm et al., 2016a). In severely affected patients, performance-dependent, neuromuscular electrical stimulation of individual upper limb muscles integrated in the exoskeleton may increase the range of motion even further (Grimm and Gharabaghi, 2016; Grimm et al., 2016b). These approaches focus on the improvement of motor control, which is defined as the ability to make accurate and precise goal-directed movements without reducing movement speed (Reis et al., 2009; Shmuelof et al., 2012), or using compensatory movements (Kitago et al., 2013, 2015). Functional gains in hemiparetic patients, however, are often achieved by movements that aim to compensate the diminished range of motion of the affected limb (Cirstea and Levin, 2000; Grimm et al., 2016a). Although these compensatory strategies might be efficient in short-term task accomplishment, they may lead to long-term complications such as pain and joint-contracture (Cirstea and Levin, 2007; Grimm et al., 2016a). In this context, providing detailed information about how the movement is carried out, i.e., the quality of the movement, is more likely to recover natural movement patterns and avoid compensatory movements, than to provide information about movement outcome only (Cirstea et al., 2006; Cirstea and Levin, 2007; Grimm et al., 2016a). This feedback, however, needs to be provided implicitly, since explicit information has been shown to disrupt motor learning in stroke patients (Boyd and Winstein, 2004, 2006; Cirstea and Levin, 2007). Information on movement quality has therefore been incorporated as implicit closed-loop feedback in the virtual environment of an exoskeleton-based rehabilitation device (Grimm et al., 2016a). Specifically, the continuous visual feedback of the whole arm kinematics allowed the patients to adjust their movement quality online during each task; an approach closely resembling natural motor learning (Grimm et al., 2016a).

Along these lines, virtual reality and interactive video gaming have emerged as treatment approaches in stroke rehabilitation (Laver et al., 2015). They have been used as an adjunct to conventional care (to increase overall therapy time) or compared with the same dose of conventional therapy. These studies have demonstrated benefits in improving upper limb function and activities of daily living, albeit currently available clinical trials tend to provide only low-quality evidence (Laver et al., 2015). Most of these studies were conducted with mildly to moderately affected patients. In the remaining patient group with moderate to severe upper limp impairment, the intervention effects were more heterogeneous and affected by the impairment level, with either no or only modest additional gains in comparison to dose-matched conventional treatments (Housman et al., 2009; Byl et al., 2013; Subramanian et al., 2013).

With respect to the restoration of arm and hand function in severely affected stroke patients in particular, there is still a lack of evidence for additional benefits from technology-assisted interventions for activities of daily living. The only means of providing such evidence is by sufficiently powered, randomized and adequately controlled trials (RCT).

However, such high-quality RCT studies require considerable resources. Pilot data acquired earlier in the course of feasibility studies may provide the rationale and justification for later large-scale RCT. Such studies therefore need to demonstrate significant improvements, with functional relevance for the participating patients. Then again, costly RCT can be avoided when innovative interventions prove to be feasible but not effective with regard to the treatment goal, i.e., that they do not result in functionally relevant upper extremity improvements in severely affected stroke patients.

One recent pilot study, for example, applied brain signals to control an active robotic exoskeleton within the framework of a brain-robot interface (BRI) for stroke rehabilitation. This device provided patient control over the training device via motor imagery-related oscillations of the ipsilesional cortex (Brauchle et al., 2015). The study illustrated that a BRI may successfully link three-dimensional robotic training to the participant’s effort. Furthermore, the BRI allowed the severely impaired stroke patients to perform task-oriented activities with a physiologically controlled multi-joint exoskeleton. However, this approach did not result in significant upper limb improvements with functional relevance for the participating patients. This training approach was potentially too challenging and may even have frustrated the patients (Fels et al., 2015). The patients’ cognitive resources for coping with the mental load of performing such a neurofeedback task must therefore be taken into consideration (Bauer and Gharabaghi, 2015a; Naros and Gharabaghi, 2015). Mathematical modeling on the basis of Bayesian simulation indicates that this might be achieved when the task difficulty is adapted in the course of the training (Bauer and Gharabaghi, 2015b). Such an adaptation strategy has the potential to facilitate reinforcement learning (Naros et al., 2016b) by progressively challenging the patient (Naros and Gharabaghi, 2015). Recent studies explored automated adaptation of training difficulty in stroke rehabilitation of less severely affected patients (Metzger et al., 2014; Wittmann et al., 2015). More specifically, both robot-assisted rehabilitation of proprioceptive hand function (Metzger et al., 2014) and inertial sensor-based virtual reality feedback of the arm (Wittmann et al., 2015) benefit from assessment-driven adjustments of exercise difficulty. Furthermore, a direct comparison between adaptive BRI training and non-adaptive training (Naros et al., 2016b) or sham adaptation (Bauer et al., 2016a) in healthy patients revealed the impact of reinforcement-based adaptation for the improvement of performance. Moreover, the exercise difficulty has been shown to influence the learning incentive during the training; more specifically, the optimal difficulty level could be determined empirically while disentangling the relative contribution of neurofeedback specificity and sensitivity (Bauer et al., 2016b).

In the present 4-week pilot study, we combined these approaches and customized them for the requirements of patients with severe upper extremity impairment by applying a multi-joint exoskeleton for task-oriented arm and hand training in an adaptive virtual environment. Notably, due to the severity of their impairment, these patients were not able to practice the reach-to-grasp movements without the exoskeleton. The set-up was, however, limited to pure antigravity support, i.e., it provided passive rather than active assistance. Furthermore, it tested the feasibility of closed-loop online adaptation of exercise difficulty and aimed at automated progression of task challenge.

Continue —> Frontiers | Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation | Neuroprosthetics

www.frontiersin.org

Figure 1. Training set-up with the exoskeleton (upper row) and the provided visual feedback in virtual reality (lower row).

, , , , , , , , , , , ,

Leave a comment

[ARTICLE] Neural Network Underlying Intermanual Skill Transfer in Humans- Full Text

 

Highlights

 

Unimanual training also enhances performance in the untrained hand (cross-education)

Real-time manipulation of visual feedback enhances magnitude of cross-education
Yoking movement of untrained to trained hand further increases cross-education
Functional connectivity with SPL during training predicts cross-education

Summary

Physical practice with one hand results in performance gains of the other (un-practiced) hand, yet the role of sensory feedback and underlying neurophysiology is unclear. Healthy subjects learned sequences of finger movements by physical training with their right hand while receiving real-time movement-based visual feedback via 3D virtual reality devices as if their immobile left hand was training. This manipulation resulted in significantly enhanced performance gain with the immobile hand, which was further increased when left-hand fingers were yoked to passively follow right-hand voluntary movements. Neuroimaging data show that, during training with manipulated visual feedback, activity in the left and right superior parietal lobule and their degree of coupling with motor and visual cortex, respectively, correlate with subsequent left-hand performance gain. These results point to a neural network subserving short-term motor skill learning and may have implications for developing new approaches for learning and rehabilitation in patients with unilateral motor deficits.

Introduction

It is common wisdom that “practice makes perfect”; however, what constitutes an optimal practice regime when learning a new skill is not clear. In the domain of motor skills, for example, when learning to dribble a basketball, physical training with the relevant effector obviously plays a crucial role. Nonetheless, research over the past decades has recognized that sensory feedback and mental imagery play a significant role in the learning process (Nyberg et al., 2006, Sigrist et al., 2013, Wolpert et al., 2011). In the case of vision, it has been shown that even in the absence of physical training, mere observation of someone else performing a motor task is sufficient to introduce significant gains in subsequent performance of the observer (Bird et al., 2005, Cross et al., 2009, Kelly et al., 2003, Mattar and Gribble, 2005, Nojima et al., 2015, Vogt and Thomaschke, 2007, Ossmy and Mukamel, 2016). Furthermore, passive limb movement has also been shown to facilitate learning (Beets et al., 2012, Darainy et al., 2013, Vahdat et al., 2014, Wong et al., 2012). Finally, physical training with one hand is known to result in significant performance gains in the opposite (untrained) hand—a phenomenon termed intermanual transfer or cross-education (Ruddy and Carson, 2013). Intermanual transfer has been reported as early as 1894, showing that unilateral strength training of a single limb increases the strength of the contralateral (untrained) homologous muscle group (Scripture et al., 1894). Since then, this effect has been demonstrated across multiple motor tasks (Anguera et al., 2007, Brass et al., 2001, Camus et al., 2009, Carroll et al., 2006, Criscimagna-Hemminger et al., 2003, Farthing et al., 2007, Lee et al., 2010, Malfait and Ostry, 2004, Perez and Cohen, 2008, Perez et al., 2007, Sainburg and Wang, 2002) and is suggested to occur through plastic changes in the brain that are not confined to the specific neural networks controlling the physically trained effector (e.g., plastic changes also in motor cortex ipsilateral to the active hand [Duque et al., 2008, Hortobágyi et al., 2003, Muellbacher et al., 2000, Obayashi, 2004]). Enhancing the behavioral effect of intermanual transfer and elucidating its underlying neural mechanism has important implications for rehabilitation of patients with unimanual deficits (Hendy et al., 2012, Ramachandran and Altschuler, 2009) in which direct training of the affected hand is difficult.

Given that visual input, physical training, and passive movement play a significant role in performance and intermanual transfer of motor skills, research in recent years examined the behavioral and neural consequences of training with manipulated visual feedback (Halsband and Lange, 2006). In particular, unimanual training with mirrored visual feedback (as if the opposite, passive hand, is training) has been shown to enhance transfer to the opposite hand and increase excitability of primary motor cortex (M1) ipsilateral to the physically trained hand (Garry et al., 2005, Hamzei et al., 2012, Nojima et al., 2012). Nonetheless, much less is known at the whole-brain network level and how inter-regional coupling during such training correlates with subsequent behavioral changes in performance. Additionally, at the behavioral level, the interaction between manipulated visual feedback and passive movement during training is unknown.

In the present study, we examined intermanual transfer using a novel setup employing 3D virtual reality (VR) devices to control visual feedback of finger movements during unimanual training of healthy adults (experiment 1). By using a novel device, we also examined whether the addition of passive finger movement of the non-physically training hand further enhances the intermanual transfer effect (experiment 2). Finally, we used whole-brain functional magnetic resonance imaging (fMRI) to probe the relevant brain regions engaged during such training and examined their degree of inter-regional coupling with respect to subsequent behavioral changes in performance of individual subjects (experiment 3).

Continue —>  Neural Network Underlying Intermanual Skill Transfer in Humans: Cell Reports

(A) Schematic illustration of one experimental condition. A unique sequence of five digits was presented together with a sketch of the mapped fingers (instructions). Subjects performed the sequence as accurately and rapidly as possible using their right hand (RH) and their left hand (LH) separately for initial evaluation of performance. Next, subjects were trained under a specific training type and finally repeated the evaluation test again. (B) Subjects wore a headset and motion sensitive gloves and received visual feedback of virtual hands. The VR devices allowed visual manipulation of online visual feedback. A camera mounted on the headset allowed embedding the virtual hands and subject’s view inside a natural environment. (C) Experiment 1 results. Physical training with the right hand while receiving online visual feedback as if the left hand is moving (RH-LH) resulted in highest left-hand performance gains relative to all other training conditions. Error bars indicate SEM across subjects. For condition acronyms, see Table 1.

, , , , , , , ,

Leave a comment

%d bloggers like this: