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[ARTICLE] Portable Motion-Analysis Device for Upper-Limb Research, Assessment, and Rehabilitation in Non-Laboratory Settings – Full Text

Abstract

This study presents the design and feasibility testing of an interactive portable motion-analysis device for the assessment of upper-limb motor functions in clinical and home settings. The device engages subjects to perform tasks that imitate activities of daily living, e.g. drinking from a cup and moving other complex objects. Sitting at a magnetic table subjects hold a 3D printed cup with an adjustable magnet and move this cup on the table to targets that can be drawn on the table surface. A ball rolling inside the cup can enhance the task challenge by introducing additional dynamics. A single video camera with a portable computer tracks real-time kinematics of the cup and the rolling ball using a custom-developed, color-based computer-vision algorithm. Preliminary verification with marker-based 3D-motion capture demonstrated that the device produces accurate kinematic measurements. Based on the real-time 2D cup coordinates, audio-visual feedback about performance can be delivered to increase motivation. The feasibility of using this device in clinical diagnostics is demonstrated on 2 neurotypical children and also 3 children with upper-extremity impairments in the hospital, where conventional motion-analysis systems are difficult to use. The device meets key needs for clinical practice: 1) a portable solution for quantitative motor assessment for upper-limb movement disorders at non-laboratory clinical settings, 2) a low-cost rehabilitation device that can increase the volume of in-home physical therapy, and 3) the device affords testing and training a variety of motor tasks inspired by daily challenges to enhance self-confidence to participate in day-to-day activities.

SECTION I.

Introduction

An integral part of clinical care for individuals with motor disorders is to assess motor function to guide and evaluate medical treatment, surgical intervention or physical therapy. One of the challenges for assessing motor function is to define sensitive and quantitative measures that can be readily obtained in clinical practice. The objective of this study was to develop a device that affords quantitative assessment of motor impairments in non-laboratory settings. The specific focus is on individuals with upper-limb movement disorders. One central goal was to ground the task in scientific research to relate clinical measures to research and capitalize on insights from fundamental research.

This paper first lays out the need for such a device particularly for children with motor disorders and post-stroke rehabilitation. We then motivate the specific motor task that was originally conceived for basic research on motor control. We then detail the design of the prototype with all hardware and software components so that it can be replicated. One design goal was to make the device low-cost, so that it can be used in many clinical environments including at home for therapeutic exercises. We conclude with first results from pilot experiments acquired both in a traditional laboratory setting and in an Epilepsy Monitoring Unit. These first data were obtained from children with dystonia. However, the device is not limited to this population and is currently further modified for the assessment of stroke patients.

A. Clinical Assessments of Motor Disorders

A motor disorder manifests as an impaired ability to execute a movement with the intended spatial and temporal pattern. This includes abnormal posturing, presence of unintended excessive movement, and normal movements occurring at unintended or inappropriate times [1]. Patients with upper-limb impairments require special assistance to perform common motor tasks associated with self-care, such as feeding and dressing. Challenges in their movement control result in frustration, which leads to less engagement and practice, and thereby fewer opportunities to attenuate their motor disabilities and improve their movement control.

Motor disorder are observed also among children. Cerebral Palsy (CP) is a common cause of movement disorders among children, affecting 3 to 4 individuals per 1000 births in the US. The dyskinetic form of CP occurs in 15% of all cases [2]. Due to inflexible postures, caused by muscle spasms and contractures together with involuntary jerky movements, children with dyskinetic CP are often prevented from participation in many daily activities. This also prevents them from acquiring age-appropriate motor skills during critical periods of skill development [3], [4]. This is particularly aggravated when the condition affects the upper limbs.

For clinical motor assessments, the current standard tools are clinical scales. For cerebral palsy, typical tests are the gross motor function classification system (GMFCS) [5], the manual ability classification system (MACS) [6], the House Scale [7], the Melbourne Assessment [8], the Assisting Hand Assessment [9], the Hypertonia Assessment Tool (HAT) [10], the Barry-Albright Dystonia (BAD) scale [11], and the Shriners Hospital for Children Upper Extremity Evaluation [12]. These outcome measures were devised to satisfy the typical criteria for effective outcome measures, including reliability, validity, specificity, and responsiveness [13]. Although useful, these rating scales rely on subjective assessment and questionnaires that are vulnerable to inter-rater and test-retest reliability, nonlinearity, multi-dimensionality, and ceiling or floor effects [14]. These shortcomings need to be overcome by more quantitative outcome measures to provide a better evaluation of the individual’s motor functions and abilities, and potentially utilize such measures to objectvely assess and titrate interventions.

B. Quantitative Assessment of Motor Function

Motion tracking technologies have provided quantitative means of recording movements through a variety of sensing technology that tracks and stores movement. Camera-based motion capture, such as Vicon (Vicon Motion Systems, Oxford, UK) and Optitrak (Northern Digital Inc, Ontario, CA) requires external markers or sensors placed on key anatomical landmarks to reconstruct the skeletal model of human body parts. These state-of-the-art technologies track motion to very high precision with high sampling rates and they have been used for pre- and post-treatment assessment of upper- or lower-extremity pathologies. However, such data acquisition is limited to traditional laboratory settings because the multi-camera systems are expensive and not portable.

On the other hand, there are low-cost inertial measurement units (IMUs) that directly measure acceleration, rotational change and magnetic orientation. While these sensors have the advantage that they are self-contained and wearable, drawbacks are degraded accuracy due to drift, calibration errors and noise inherent to inertial sensors and the need to frequently recharge batteries for real-time data streaming [15]. Moreover, attaching sensors to body parts can be inconvenient or even impossible for certain clinical populations, and many children will not tolerate them.

In view of the above arguments, there is a strong need for less invasive devices that can provide quantitative measurements in tasks related to upper-extremeity motor function. Preferably, such a device should allow for portability and be low-cost to reach large populations.

C. Low-Cost Rehabilitation at Home

Rehabilitation follows standard practice and frequently requires one-on-one interaction with a therapist for extended periods of time. For these reasons, robotic devices have emerged to deliver higher-dosage and higher-intensity training for patients with movement disorders such as cerebral palsy and stroke [16]–[17][18][19]. However, while effective, robotic therapy is expensive and to date can only be used in clinical settings. To increase the volume in therapy, lower-cost devices that can be used at home are urgently needed.

Performance improvements with predominant home training are indeed possible. This was demonstrated by pediatric constraint-induced movement therapy (CIMT) for children with hemiparetic CP [20], [21]. Further, it was shown that even children with severe dystonia can improve their performance if they use an interface or device that enables and facilitates their severely handicapped movements [22].

A portable low-cost device for home use that is able to provide reliable quantitative measurements would help address the above shortcomings. Measurements could also be streamed to careproviders on a secure cloud protocol, for diagnosis of interventions, analysis of therapeutic outcomes, and further follow up.

D. Theoretically-Grounded Rehabilitation

Motor tasks for home therapy should be engaging to avoid boredom and attrition and should also have functional relevance. With this goal in mind, we developed a motor task that was motivated by the daily self-feeding activity of leading a cup of coffee or a spoon filled with soup to the mouth. The core challenge of actions of this kind is that moving such an object with sloshing liquid presents complex interaction forces: any force applied to the cup also applies a force to the liquid that then acts back on the hand. When such internal dynamics is present, interaction forces become quite complex, and the human performing the task needs to predict and preempt the internal dynamics of the moving liquid. Clearly, better understanding task is like guiding a cup of coffee to one’s mouth or a spoonful of soup has high functional relevance. While many such functional tasks have been developed for rehabiltation (e.g., the box-and-block and the pegboard task), the quantitative assessment should allow for more than descriptive outcome measures such as error or success rate. Monitoring the ‘process’ continuously should provide more detailed insight into coordinative challenges. This is indeed possible in the task of guiding a cup of coffee as we explain next.

In previous research, we abstracted a relevant, yet simplified model task, inspired by guiding a cup of coffee [23]–[24][25][26]. To reduce the complexity and afford theoretical analyses, the “cup of coffee” was simplified to a rigid object with a rolling ball inside. The rolling ball represents the moving liquid; this is also similar to the children’s game of transporting an egg in a spoon [27]. Fig.1A-C shows the transition from the real object to the simplified physical model. Importantly, the original task (Fig.1A) was reduced to a two-dimensional model, where the subject interacts with the object via a robotic manipulandum. The virtual model consists of a cart with a suspended pendulum, a well-known benchmark problem in control theory.

FIGURE 1.Model for the task of carrying a cup of coffee. A: The real object. B: The simplified physical model. C: The equivalent cart-and pendulum model implemented in the virtual task.

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[Systematic Review] Effects of Soft Robotic Gloves on Rehabilitation Outcomes in Individuals With Sensorimotor Hand Impairments: A Systematic Review

INTRODUCTION

For individuals with hand hemiparesis following a stroke, rehabilitation strategies are predominantly founded on the principles of neuroplasticity and automaticity [1] to regain optimal hand-related functional abilities and facilitate participation in everyday activities. Such an approach requires to engage these individuals into meaningful activity-specific exercises and to repeat those intensively on a daily basis. Adhering to these principles [2] remains challenging in clinical practice for rehabilitation professionals, especially given various time and productivity constraints. To overcome this challenge, the development of soft robotic gloves to facilitate hand rehabilitation have progressed substantially in the last decade. Moreover, these soft robotic gloves are foreseen as promising rehabilitation intervention to potentiate the effects of conventional rehabilitation interventions and are now about to transition into clinical practice, although their effects remain uncertain given the paucity of evidence. In this context, this review aims to map evidence on the effects of the different rehabilitation interventions using a soft robotic device for sensorimotor hand impairments and, whenever possible, the satisfaction related to their use.

METHODS

Eligibility Criteria, Information Sources, And Search

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [3]. A review of the literature published in English and French from 2000 to October 2018 using a combination of search terms was conducted in Medline, EMBASE, and CINAHL. The search strategy included a combination of search terms related to three key domains: technology attributes (robotics, bionics, exoskeleton device, robot*, exoskelet*, motorized, motor-driven, motor assisted), anatomy of the hand (hand, hands, wrist*, finger*, prehension, dexterity), and rehabilitation domains (rehabilitation, exercise*, exercise therapy, physical therapy modalities, physical therapy speciality, physical therapists, occupational therapy, occupational therapist, therap*, physiothrap*). Search terms related to amputation, surgery, computer-assisted device, and teleoperation were excluded. From this initial search, 1870 articles were found and only 1206 articles remained after eliminating all duplicates. To narrow down the number of articles, a new domain was added (i.e., technology= glove, soft, wearable) and the search among the keywords, title, and abstracts was continued in EndNote. Thereafter, 181 articles remained and were imported into the web-based software platform Covidence where 9 additional duplicates were found.

SELECTION OF SOURCES OF EVIDENCE

The articles title, abstract and full text of 172 articles were screened by two rehabilitation professionals to identify the articles qualifying for a subsequent full review. To be considered for full review the article has to  target 1) the effects or effectiveness of rehabilitation interventions using soft robotic gloves to optimize hand-related functional abilities and facilitate participation in everyday activities in people with sensorimotor disorders via randomized controlled trials (RCTs), non-randomized controlled trials (non-RCT), and other types of research designs (cohort studies, pre- and post-case interventions, case series, case-control studies and case reports) and 2) the users satisfaction and stakeholder views on the use of soft robotic gloves. For this review, in order to be considered a soft robotic glove, the technology had to generate assisted pinching or gripping movements soliciting multiple joints involving at least the thumb and the index finger and middle fingers. Interventions using a soft robotic glove could be performed in a hospital, rehabilitation center or at home with the direct or indirect supervision of a rehabilitation professional. The use of the soft robotic glove could also be combined with other technologies (e.g., virtual reality). Research protocols or manuscripts that did not include participants with sensorimotor impairment were excluded. All scientific manuscripts and conference abstracts focusing on upper limb exoskeleton including the elbow or shoulder joint were excluded.

Data Extraction And Charting Process

Studies that met the inclusion and exclusion criteria were read by a single rehabilitation professional and the following information were extracted on project-specific forms data extraction tables organized within an excel file: author-related information’s, journals and publication year, soft robotic glove attributes, study design, population and sample size, intervention, measurement instruments, results and interpretations, and user’s satisfaction. At the end, to establish if the use of a soft robotic glove yield to positive, neutral or negative effect, the p-value and effect size of each outcome measures from each article were determined.

RESULTS

Characteristics Of Sources Of Evidence

Ten articles included in this study originated from European or American countries; USA (5/10) [4-8], Italy (2/10) [9,10], United Kingdom (2/10) [11,12], and Netherlands (1/10) [13]. The majority of these studies were published in 2017 (6/10) [6,8-12] or 2018 (3/10) [5,7,13]. Only one study was published in 2011 [4].

Study Designs And Populations

Both experimental (3/10) [8,10,12] and quasi-experimental studies (7/10) [4-7,9,11,13] were selected with mean sample sizes of 12,4 participants and ranging from 2 to 27. Most studies investigated individuals with hemiparesis following a stroke (9/10) [4-6,8-13] whereas one article investigated individuals with of a traumatic spinal cord injury [7].

Synthesis Of Findings

Soft robotic gloves

Eight different soft robotic gloves (i.e., HandSOME [4,6], FES Hand Glove [7], Gloreha Light Glove [9], Gloreha Professional [10], VAEDA [8], HandinMind [12,13] and two others without names) with different types of assistance (i.e., motor [7,8,9,10,12,13], elastic [4,6], and pneumatic [5,11]) were identified.

Interventions

Four studies [4,5,11,13] used a transversal design to compare hand function with and without the use of a soft robotic device glove whereas three studies used an experimental design [8,10,12] and three used a quasi-experimental design [6,7,9] to compare hand sensorimotor integrity and functional abilities before and after an intervention with the soft robotic glove. No concomitant therapy was used in all of the studies. The intervention protocols of the experimental and quasi-experimental design studies varied in length from 4 to 8 weeks, in frequency from 3 to 6 times a week and training sessions duration from 40 to 90 minutes.

Outcome measures

The outcome measures included: Ashworth Spasticity Index [9] or Ashworth modified scale [6], edema [9], Hand pain VAS [9], Barthel [9], Motricity index [9,10], Nine hole peg test (NHPT) [9,10], grip strength [4,6,8-10], active range of motion (AROM) [4], Velocity of movements [4], Box and blocks test [4], Fugl-Meyer Assessment of Upper Extremity (FMA-UE) [6,8], Fugl-Meyer Hand (FMH) [8], The Action Research Arm Test (ARAT) [6,8], The Motor Activity Log [6], time to execute tasks [11], Toronto Rehabilitation Institute Hand Function Test (TRI-HFT) [5], pinch strength [8,10,12], JTFHT [12], Activity of Daily Living (ADL) [13], Functional Independence Measure (FIM) [7], Wolf Motor Function Test (WMFT) [8], Chedoke McMaster Stroke Assessment Hand (CMSAH) [8] and the Quick-DASH [10]. Then, each outcomes measure have been classified according to the International Classification of Functioning, Disability and Health (ICF) [14] (Figure. 1).

Effects and effectiveness

Figure 1. Outcomes measures classified with the CIF, p-values and effect size

The results in terms of effects and effectiveness of the interventions are listed in the Figure 1. Mostly, the use of robotic gloves increased joint mobility and functional capacity of the upper limb in terms of performance rapidity. According to muscular strength, functional capacity of the upper limb assessed by questionnaire, and global functional capacity, the results are heterogeneous and do not allow conclusion on the effectiveness of intervention using this technology.

Usability, feasibility and satisfaction

Four studies also assessed the usability, feasibility or satisfaction of the users after trying the soft robotic glove [10-13] using the Usefulness-Satisfaction-and-Ease-of-Use questionnaire [11], observations [4,10], System Usability Scale [12,13], Intrinsic Motivation Inventory [13], cost analysis [10]. Studies concluded that the use of soft robotic gloves is foreseen as being feasible and acceptable by participants and rehabilitation professionals [10-13] and as increasing engagement in rehabilitation program [11,13]. Most of the studies support the fact that the soft robotic gloves are easy to use [10, 1,13]. However, the robotic glove was found to be more useful when performing gross motor tasks when compared to fine motor tasks [12], the presence of a zipper on the glove made it difficult to put on [13], and the choice of material, especially its thickness, was found to interfere with hand and finger sensations [13]. A preference for the rental of these devices has been demonstrated [11]. The most important features highlighted in the studies included: easy to clean, comfortable, easy to put on and take off. Last, a decreased in rehabilitation cost linked to the use of a soft robotic device at home may be anticipated [10].

DISCUSSION

This systematic review of the literature confirms an increased interest over the last decade in the development and use of soft robotic gloves for rehabilitation of individuals with hand hemiparesis following a neurological event. Overall, the use of soft robotics devices in rehabilitation treatment is feasible, safe, and acceptable by patients while its effects and effectiveness appear promising. However, the strength of the currently available evidence remains limited and given the wide variety of soft robotic glove attributes, study designs and interventions, and outcomes measures alongside the small sample sizes tested, it is impossible to highlight which soft robotic glove or intervention protocol would be the most appropriate to obtain the best clinical results.  Stronger evidence linked to the effects or effectiveness, in addition to comprehensive stakeholder perspectives (e.g., patients, rehabilitation professionals), especially on the usability, are needed to ensure a successful transition from the laboratory to clinical practice.

CONCLUSIONS

This systematic review maps currently available evidence on the use of soft robotic gloves as a rehabilitation intervention while considering effectiveness and usability. This technology is a promising solution to optimize sensorimotor capabilities, hand-related functional abilities and facilitate participation in everyday activities while overcoming some clinical constraints. Additional research in this area should be encouraged to strengthen current evidence.

REFERENCES

[1] Chollet, F., DiPiero, V., Wise, R. J. S., Brooks, D. J., Dolan, R. J., & Frackowiak, R. S. J. (1991). The functional anatomy of motor recovery after stroke in humans: a study with positron emission tomography. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 29(1), 63-71.

[2] Hubbard, I. J., Parsons, M. W., Neilson, C., & Carey, L. M. (2009). Task‐specific training: evidence for and translation to clinical practice. Occupational therapy international, 16(3‐4), 175-189.

[3] Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Annals of internal medicine, 151(4), 264-269.

[4]Brokaw, E. B., Black, I., Holley, R. J., & Lum, P. S. (2011). Hand Spring Operated Movement Enhancer (HandSOME): a portable, passive hand exoskeleton for stroke rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(4), 391-399.

[5] Cappello, L., Meyer, J. T., Galloway, K. C., Peisner, J. D., Granberry, R., Wagner, D. A., … & Walsh, C. J. (2018). Assisting hand function after spinal cord injury with a fabric-based soft robotic glove. Journal of neuroengineering and rehabilitation, 15(1), 59.

[6] Chen, J., Nichols, D., Brokaw, E. B., & Lum, P. S. (2017). Home-based therapy after stroke using the hand spring operated movement enhancer (HandSOME). IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(12), 2305-2312.

[7] Scott, S., Yu, T., White, T. K., Van Harlinger, W., Ganzalez, Y., Llanos, I., & Kozel, A. F. (2018). A robotic hand device safety study for people with cervical spinal cord injury. Federal practitioner, 35(3), S21-S24.

[8] Thielbar, K. O., Triandafilou, K. M., Fischer, H. C., O’Toole, J. M., Corrigan, M. L., Ochoa, J. M., … & Kamper, D. G. (2017). Benefits of using a voice and EMG-Driven actuated glove to support occupational therapy for stroke survivors. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(3), 297-305.

[9] Bernocchi, P., Mulè, C., Vanoglio, F., Taveggia, G., Luisa, A., & Scalvini, S. (2018). Home-based hand rehabilitation with a robotic glove in hemiplegic patients after stroke: a pilot feasibility study. Topics in stroke rehabilitation, 25(2), 114-119.

[10]Vanoglio, F., Bernocchi, P., Mulè, C., Garofali, F., Mora, C., Taveggia, G., … Luisa, A. (2017). Feasibility and efficacy of a robotic device for hand rehabilitation in hemiplegic stroke patients: a randomized pilot-controlled study. Clinical rehabilitation, 31(3), 351-360.

[11] Yap, H. K., Lim, J. H., Nasrallah, F., & Yeow, C. H. (2017). Design and preliminary feasibility study of a soft robotic glove for hand function assistance in stroke survivors. Frontiers in neuroscience, 11, 547.

[12] Prange-Lasonder, G. B., Radder, B., Kottink, A. I., Melendez-Calderon, A., Buurke, J. H., & Rietman, J. S. (2017, July). Applying a soft-robotic glove as assistive device and training tool with games to support hand function after stroke: Preliminary results on feasibility and potential clinical impact. In Rehabilitation Robotics (ICORR), 2017 International Conference on (pp. 1401-1406). IEEE.

[13] Radder, B., Prange-Lasonder, G. B., Kottink, A. I., Melendez-Calderon, A., Buurke, J. H., & Rietman, J. S. (2018). Feasibility of a wearable soft-robotic glove to support impaired hand function in stroke patients. Journal of rehabilitation medicine, 50(7), 598-606.

[14] World Health Organization. (2001). International classification of functioning, disability and health: ICF. Geneva: World Health Organization.

ACKNOWLEDGMENTS

Supported by the Initiative for the Development of New Technologies and Innovative Practices in Rehabilitation and by the Université de Montréal (Direction des affaires internationales).

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[THESIS] The effectiveness of acupuncture in upper extremity motor function rehabilitation in post stroke patient–systematic literature review

The aim of this thesis is to find out the effects of acupuncture in upper extremity motor function rehabilitation in stroke patient. A form of systematic literature review is used to complete the thesis research. The component of the theoretical part includes background of stroke, such as pathology and complications. Upper extremity motor Function rehabilitation in stroke patient will be presented, as well as the basics of acupuncture, which extended to the definition, acupuncture mechanism, and evidence-based acupuncture. The effects of acupuncture in upper extremity motor function rehabilitation for stroke patient will be discussed.

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[ARTICLE] Brain oscillatory activity as a biomarker of motor recovery in chronic stroke – Full Text

Abstract

In the present work, we investigated the relationship of oscillatory sensorimotor brain activity to motor recovery. The neurophysiological data of 30 chronic stroke patients with severe upper‐limb paralysis are the basis of the observational study presented here. These patients underwent an intervention including movement training based on combined brain–machine interfaces and physiotherapy of several weeks recorded in a double‐blinded randomized clinical trial. We analyzed the alpha oscillations over the motor cortex of 22 of these patients employing multilevel linear predictive modeling. We identified a significant correlation between the evolution of the alpha desynchronization during rehabilitative intervention and clinical improvement. Moreover, we observed that the initial alpha desynchronization conditions its modulation during intervention: Patients showing a strong alpha desynchronization at the beginning of the training improved if they increased their alpha desynchronization. Patients showing a small alpha desynchronization at initial training stages improved if they decreased it further on both hemispheres. In all patients, a progressive shift of desynchronization toward the ipsilesional hemisphere correlates significantly with clinical improvement regardless of lesion location. The results indicate that initial alpha desynchronization might be key for stratification of patients undergoing BMI interventions and that its interhemispheric balance plays an important role in motor recovery.

1 INTRODUCTION

Stroke is a major global health problem. The number of stroke victims has been rising in the past years all around the world. Millions of stroke survivors are left with very limited motor function or complete paralysis and depend on assistance (Feigin et al., 2016). Therapeutic approaches such as constraint‐induced movement therapy are not applicable to the group of patients with severe limb weakness (Birbaumer, Ramos‐Murguialday, & Cohen, 2008). However, brain–machine interface (BMI) training has demonstrated efficacy in promoting motor recovery in chronic paralyzed stroke patients (Ramos‐Murguialday et al., 2013), and long term effects (Ramos‐Murguialday et al., 2019). Subsequent work has replicated and confirmed BMI efficacy. Arm and hand movements are trained using a body actuator (e.g., orthotic robots) that is controlled by oscillatory activity of the brain (Ang et al., 2014; Frolov et al., 2017; Kim, Kim, & Lee, 2016; Leeb et al., 2016; Mokienko et al., 2016; Ono et al., 2014). Brain signals can thus travel to the limb muscles along an alternative pathway. Contingently linking movement‐related patterns of brain activity and visuo‐proprioceptive feedback of the movement supports associative learning (Ramos‐Murguialday et al., 2012; Sirigu et al., 1995).

Changes in sensorimotor brain oscillations involved in planning and execution of movements were used as control signals for the BMI in the aforementioned studies. The sensorimotor rhythm (SMR) is an oscillation within the alpha frequency range of the EEG during a motionless resting state over the central‐parietal brain regions. Movement planning, imagination and execution lead to its suppression. In the present work, we investigate EEG brain oscillations of the alpha frequency, ranging from 8 to 12 Hz, over the motor cortex, and we term them “alpha oscillations.”

Biomarkers could be defined as indicators “of disease state that can be used as a measure of underlying molecular/cellular processes that may be difficult to measure directly in humans” (Boyd et al., 2017). When dealing with a condition as heterogeneous as stroke validated biomarkers of recovery could help plan treatments and support efficient allocation of resource while maximizing outcome for the patients. Alpha brain oscillations have been evaluated as markers of ischaemia and predictors of clinical outcome in acute patients (Finnigan & van Putten, 2013; Rabiller, He, Nishijima, Wong, & Liu, 2015). Desynchronization in the alpha frequency range has also been investigated as a marker of stroke and a predictor of recovery in the same patient group. Tangwiriyasakul, Verhagen, Rutten, and Putten (2014) showed that the recovery of motor function was accompanied by an increase of alpha desynchronization on the ipsilesional side. In subacute patients presenting mild to moderate motor deficits recovery lead to a similar increase of alpha desynchronization on the affected hemisphere (Platz, Kim, Engel, Kieselbach, & Mauritz, 2002). Furthermore, first attempts investigated correlations of alpha desynchronization with motor improvements in chronically impaired patients (Kaiser et al., 2012). In a controlled study, a group of subacute patients with severe deficits used motor imagery, guided by a brain–computer interface, in addition to their regular physiotherapeutic rehabilitation protocol. They showed a higher probability for motor improvements with increased alpha desynchronization (Pichiorri et al., 2015).

In the present work, we investigated what changes in the oscillatory activity of the brain a proprioceptive BMI coupled with physiotherapy produces over the course of a training intervention and if these correlate with recovery in severely paralyzed chronic stroke patients. We hypothesized that functional motor improvements are accompanied by an ipsilesional increase and a contralesional decrease in alpha desynchronization indicating reorganization of compensatory brain activity from the contralesional to the ipsilesional hemisphere. We intend to establish alpha oscillatory activity as a biomarker of motor impairment and as a building block of statistical models of stroke neurorehabilitation.[…]

 

Continue —->  Brain oscillatory activity as a biomarker of motor recovery in chronic stroke – Ray – – Human Brain Mapping – Wiley Online Library

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Figure 1
Schematics of the data acquisition phase and the offline analysis for EEG and EMG. Neurophysiological data was acquired using a 16 channel EEG cap and 4 bipolar EMG electrodes on each arm. EEG data were cleaned from eye movement artifacts and trials containing other artifacts (e.g., cranial EMG, head movements, and so on). EMG data were analyzed to detect compensatory muscle contractions on the healthy upper limb and on the paretic side during resting intervals to identify these trials as contaminated because the muscle activity is a sign of undesired EEG activity. Only data free of artifacts were used for the final analysis of EEG oscillatory activity

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[Abstract] Robotic-assisted therapy with bilateral practice improves task and motor performance in the upper extremities of chronic stroke patients: A randomised controlled trial.

Abstract

BACKGROUND/AIM:

Task-specific repetitive training, a usual care in occupational therapy practice, and robotic-aided rehabilitation with bilateral practice are used to improve upper limb motor and task performance. The difference in effects of two strategies requires exploration. This study compared the impact of robotic-assisted therapy with bilateral practice (RTBP) and usual task-specific training facilitated by therapists on task and motor performance for stroke survivors.

METHODS:

Forty-three community-dwelling stroke survivors (20 males; 23 females; 53.3 ± 13.1 years; post-stroke duration 14.2 ± 10.9 months) were randomised into RTBP and usual care. All participants received a 10-minute per-protocol sensorimotor stimulation session prior to interventions as part of usual care. Primary outcome was different in the amount of use (AOU) and quality of movement (QOM) on the Motor Activity Log (MAL) scale at endpoint. Secondary outcomes were AOU and QOM scores at follow-up, and pre-post and follow-up score differences on the Fugl-Meyer Assessment (FMA) and surface electromyography (sEMG). Friedman and Mann-Whitney U tests were used to calculate difference.

RESULTS:

There were no baseline differences between groups. Both conditions demonstrated significant within-group improvements in AOU-MAL and FMA scores following treatment (P < 0.05) and improvements in FMA scores at follow-up (P < 0.05). The training-induced improvement in AOU (30.0%) following treatment was greater than the minimal detectable change (16.8%) in the RTBP group. RTBP demonstrated better outcomes in FMA wrist score (P = 0.003) and sEMG of wrist extensor (P = 0.043) following treatment and in AOU (P < 0.001), FMA total score (P = 0.006), FMA wrist score (P < 0.001) and sEMG of wrist extensor (P = 0.017) at follow-up compared to the control group. Control group boost more beneficial effects on FMA hand score (P = 0.049) following treatment.

CONCLUSIONS:

RTBP demonstrated superior upper limb motor and task performance outcomes compared to therapists-facilitated task training when both were preceded by a 10-minute sensorimotor stimulation session.

 

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[ARTICLE] Mechanisms Of Functional Adaptation Of Post Stroke Patients During Upper Limb Rehabilitation – Full Text

INTRODUCTION

Task oriented approach training of the patient with the arm weight unloading with feedback through the mirror.

Figure 1. Arm weight support training

Stroke is a leading cause of disability of the adult population worldwide. Successful recovery of upper limb motor function occurs only in 20% of cases [1]. Upper limb motor recovery is a most challenging goal, due to lack of patient’s motivation, training intensity and pathological synergy which is very difficult to correct using traditional methods. Poststroke upper limb paresis, spasticity and caused by them pathological synergies is the main problem on the way to daily living activities recovery. The problem of pathological synergies correction and transformation in rehabilitation practice are linked with the complexity of the required motor training approach [2]. A combination of cost-efficient, task-oriented, isolated and complex movement training with biofeedback is required to make synergy a compensatory mechanism for daily activities instead of pathological synkinesia.A promising but insufficiently studied method is virtual reality (VR), as well as its combination with other techniques like arm weight support training. Motor training in virtual reality (VR) with arm weight support creates the necessary facilitated environment for motor skills relearning [3].

MATERIALS AND METHODS.

45 patients (27 males and 18 females) with medium age 55 [45;65] years were enrolled in this study. All patients had one supratentorial lesion due to ischemic or hemorrhagic stroke (confirmed by MRI). Medium stroke age was 7 [4;12] months. All patients had moderate to severe upper limb paresis measured by Medical Research Council Scale for Muscle Strength and Fugl-Meyer assessment of physical performance (FMAS) upper extremity subscore 45 [35;55]. All patients received 2 weeks of a rehabilitation course, 5 days per week, 45 minutes daily.

Upper limb exoskeleton with weight support system and functional tasks in virtual environment.

Figure 2. Virtual reality with arm weight support training

Main group (n=25)  received 10 training sessions 45  minutes each on Armeo Spring system with separately adjusted weight support for shoulder and forearm and VR imitation of daily living activities such as reaching and grasping. The session includes 10 games like exercises and consistent increase of degrees of freedom from shoulder to the wrist. This condition allows teaching the patient voluntarily prevent pathologic synergy while performing a motor task.

The control group (n=20) received conventional therapy sessions with arm weight support (a system of pulleys), visual feedback (via mirror) and comparable set of tasks – reaching, grasping, manipulating objects.

The reaching test paradigm for motion analysis.

Figure 3. The reaching test.

For primary outcome assessment was used Fugl-Meyer assessment scale for upper limb, Action Research Arm Test (ARAT), Ashworth scale and Frenchay arm test. For motion analysis was used Russian Motion Capture System (Biosoft 3D). The paradigm for biomechanical analysis was presented with the functional reaching test. The reaching test was performed before and after the training course. Sitting at the table patient had to reach and grasp an empty glass located in front of him on the distance of extended healthy arm. For primary outcome were chosen reaching trajectory and arm kinematics, but patients were instructed to focus on the grasping movement to keep reaching movement more automatic. Normal reaching pattern was investigated on 10 healthy volunteers.

RESULTS.

FM and ARAT results on the main and control group before and after rehabilitation course.FM and ARAT results on the main and control group before and after rehabilitation course.
Figure 4. FM and ARAT scales before and after rehabilitation.
Table 1. Time of reaching test.
  Before rehabilitation After rehabilitation p-level
Moderate paresis, Ме [25%;75%] 1,5 [1,24; 1,71] 1,26 [0,9; 1,62] p=0,045
Severe paresis, Ме [25%;75%] 2,25 [1,65; 3,76] 2,66 [1,11; 3,05] p=0,043
Normal, Ме [25%;75%] 0,96 [0,87; 1,16]

In our study, the clinical assessment (FM and ARAT scales) showed that paretic hand recovery was found more in patients with moderate and severe paresis. Statistically significant improvements in the arm motor function (FMAS) were found in both groups. However, subsection analysis revealed that the patients of the main group compared to the control group had a more significant improvement in wrist movements. In ARAT was found that in patients with moderate paresis significant improvements occur in both main and control groups. In patients with severe paresis, improvements were observed only in the main group.

However, after motion analysis, a different stereotype of movement recovery was found in different groups of patients. In patients with severe paresis, an increase in the deviation of the movement pattern from the physiological movement was observed. At the same time, the normalization of the motor pattern was noted in patients with moderate paresis.

Table 2. Kinematics parameters in sever hand paresis.
Movement Before rehabilitation, Ме [25%;75%] After rehabilitation, Ме [25%;75%] p-level
Elbow extension 124 [116;126] 112 [109; 125] 0,01
Shoulder  flexion 36 [27; 41] 21 [20; 32] 0,02
Shoulder abduction 10 [10; 17] 19 [18; 22] 0,04
Velocity shoulder abduction 17 [13; 20] 48 [39; 65] 0,02
Velocity elbow extension 39 [26; 69] 29 [18,39] 0,02

The time of reaching test execution in patients with severe paresis after rehabilitation was longer than before and exceeded the normal time more than twice. Curiously,  these changes in patients with severe paresis were associated with an increase in functionality in the paretic arm (p>0,05).

The kinematic parameters such as elbow extension, shoulder abduction and angular velocity in shoulder and elbow joints after rehabilitation were worsened. After a rehabilitation course was founded decreasing of the angular velocity of the elbow joint extension, increasing of the angular velocity of the shoulder joint, decreasing of the flexion in the shoulder joint and angular speed of the elbow joint extension.

The analysis of trunk movements in severe paresis patients was shown that after rehabilitation course the trunk compensatory strategy was increased (trunk was mowed forward when patient reach the glass). These changes were associated with an increase in functionality in the paretic arm (p>0,05).

CONCLUSIONS.

Table 3. Body displacement in reaching test.
Shoulder displacement Before rehabilitation, Ме [25%;75%] After rehabilitation, Ме [25%;75%]
Healthy shoulder 23 [19,8; 57,44] 66 [49;81]
Paretic shoulder 169 [88; 178] 215 [162; 229]

If we summarized data of clinical and biomechanical parameters we see, that patients with severe paresis formed the new compensatory strategy of motion. Because of the significant changes in functional recovery are combined with worsened of biomechanical parameters.

It is believed that it is the resistance to pathological synergies and the forced training in physiological movement is the most effective method. However, correction of pathological synergies allows developing the most energy-efficient stereotype of movements for patients with regard to their individual capabilities. Combined VR and weight support training can be more effective to restore the impaired motor function after stroke than conventional weight support training. This approach contributes to the motor pattern reorganization through biomechanical and visual feedback, projected into the virtual space.

REFERENCES

[1] Beebe J.A., Lang C.E. Active range of motion predicts upper extremity function 3 months after stroke. Stroke. 2009 40 (5): 1772–1779.

[2] Cirstea M.C., Levin M.F. Compensatory strategies for reaching in stroke. Brain. 2000 123 (5): 940–953.

[3] Laver K.E., George S.,J.E. Thomas, M. Deutsch. Crotty Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev.  2015 12 (2): 83.

via Mechanisms Of Functional Adaptation Of Post Stroke Patients During Upper Limb Rehabilitation.

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[ARTICLE] The Integration of Brain-Computer Interface (BCI) as Control Module for Functional Electrical Stimulation (FES) Intervention in Post-Stroke Upper Extremity Rehabilitation – Full Text

ABSTRACT

One of the prevalent disabilities after stroke is the loss of upper extremity motor function, leading survivors to suffer from an increased dependency in their activities of daily living and a general decrease in their overall quality of life. Therefore, the restoration of upper extremity function to improve survivors’ independency is crucial. Conventional stroke rehabilitation interventions, while effective, fall short of helping individuals achieve maximum recovery potential. Functional Electrical Stimulation (FES), both with passive and active approaches, has been found to moderately increase function in the affected limbs. This paper discusses a novel EEG-Based BCI-FES system that provides FES stimulation to the affected limbs based on the brain activity patterns of the patient specifically in the sensory motor cortex, while the patient imagines moving the affected limb. This system allows the synchronization of brain activity with peripheral movements, which may lead to brain reorganization and restoration of motor function by affecting motor learning or re-learning, and therefore induce brain plasticity to restore normal-like brain function.

INTRODUCTION

Stroke is one of the leading causes of severe motor disability, with approximately 800,000 individuals each year are experiencing a new or recurrent stroke in the US alone (1). Advances in healthcare and medical technology, and the high incidence of stroke and its increasing rate in the growing elderly population, have contributed to a relatively large population of stroke survivors currently estimated at 4 million individuals in the United States alone (1). These survivors are left with several devastating long-term neurological impairments.

The most apparent defect after a stroke is motor impairments, with impairment of upper extremity (UE) functions standing as the most disabling motor deficit. Approximately 80% of survivors suffering from UE paresis, and only about one-tenth of the them regain complete functional recovery (2). Stroke survivors generally suffer from a decrease in their quality of life, and an increase dependency in their activities of daily living. Statistically, close to one quarter of the stroke survivors become dependent in activities of daily living (3). Thus, the optimal restoration of arm and hand function is crucial to improve their independence.

Recently, several remarkable advancements in the medical management of stroke have been made. However, a widely applicable or effective medical treatment is still missing, and most post-stroke care will continue to depend on rehabilitation interventions (4). The available UE stroke rehabilitation interventions can be categorized as: conventional physical and occupational therapy, constraint-induced movement therapy (CIT), functional electrical stimulation (FES), and robotic-aided and sensor-based therapy systems (5). Although an increased effort has been made to enhance the recovery process following a stroke, survivors generally do not reach their full recovery potential. Thus, the growing population of stroke survivors is in a vital need for innovative strategies in stroke rehabilitation, especially in the domain of UE motor rehabilitation. This paper presents an innovative integration of a brain-computer interface (BCI) system to actively control the delivery of FES. Early research and product development activities are advancing the reality of this becoming a mainstream intervention option.

PASSIVE VS. ACTIVE DELIVERY OF FES

The use of FES on the impaired arm is an accepted intervention for stroke rehabilitation aiming to improve motor function. A systematic review with meta-analysis of 18 randomized control trials found that FES had a moderate effect on activity compared with no intervention or placebo and a large effect on UE activity compared to control groups, suggesting that FES should be used in stroke rehabilitation to improve the ability to perform activities (6). Specifically, improvements in UE motor function after intensive FES intervention can be ascribed to the increased ability to voluntarily contract impaired muscles, the reduction in spasticity and improved muscle tone in the stimulated muscles, and the increased range of motion in all joints (7). These improvements in UE after FES could be attributable to multiple neural mechanisms, with one mechanism suggesting that proprioceptive sensory input and visual perception of the movement could promote neural reorganization and motor learning (8). A potential limiting factor to the application of FES is that the stimulation is administered manually, usually from a therapist, without any regard to the concurrent brain activity of the patient. This makes the delivery a passive process with no to minimal coordination with the mental task required to happen concurrently from the patient.

On the other hand, electromyography (EMG)-triggered FES systems made the delivery of FES an active process. Such systems are activated through detecting a preset electrical threshold in certain muscles, which provide the user (patient) the ability to actively control the delivery of FES and make the delivery concurrent with the patient’s brain activity. However, a systematic review of 8 randomized controlled trials (n=157) that assessed the effects of EMG-triggered neuromuscular electrical stimulation for improving hand function in stroke patients found no statistically significant differences in effects when compared to patients receiving usual care (9). A possibility to explain the shortcoming of EMG-triggered FES systems, is that the ability of the brain to generate and send efficient neural signals to the peripheral nervous system is disrupted after stroke, which could affect the control mechanism of these systems. Thus, the synchronization of FES with brain activity maybe critical for the optimization of recovery.

AN ACTIVE EEG-BASED BCI-FES SYSTEM

BCI technology can be used to actively control the FES application through detecting the brain neural activity directly when imagining or attempting a movement. Performing or mentally imagining (or as it commonly called motor imagery) a movement results in the generation of neurophysiological phenomena called event-related desynchronization or synchronization (ERD or ERS). ERD or ERS can be observed from Mu (9–13 Hz) or Beta rhythms (22–29 Hz) over the primary sensorimotor area contralateral to the imagined part of the body (10). These rhythms can be detected using electroencephalography (EEG). Therefore, an EEG based BCI system can be utilized to provide automated FES neurofeedback through detecting either actual movement or motor imagery (MI) and can be used to train the voluntary modulation of these rhythms. The ability to modulate these rhythms alongside the real-time neurofeedback from the FES application may induce neuroplastic change in a disrupted motor system to allow for more normal motor-related brain activity, and thus promote functional recovery. Figure 1 provides an overview of the BCI-FES system.

Any BCI-FES intervention session includes two screening tasks: an open-loop screening followed by a closed-loop task. The open-loop screening task is used to identify appropriate EEG-based control features to guide all subsequent closed-loop tasks. In the open-loop screening task, subjects are instructed to perform attempted movement of either hand by following on-screen cues of “right”, “left”, and “rest”. The attempted movement can vary across subjects, depending on the subject’s baseline abilities and recovery goals. For example, subjects can perform opening and closing of the hand or wrist flexion/extension movements. During this screening task, no feedback is provided to the subject.

figure 2 shows a screenshot of the closed-loop task interface, with a ball at the center and a target to the right, in order to provide a cue for the user to move his/her right hand.

Figure 2. Screenshot of Closed-loop Task

Data from the open-loop screening task will then be analyzed to identify appropriate EEG-based control features by determine the EEG channels the presents the largest r-squared values within the frequency ranges of the Mu and Beta rhythms for each attempted movement using left or right hand (11). The identified channels and the specific frequency bins will then be used to control the signals for the closed-loop neurofeedback task.

In the closed-loop screening task, a real-time visual feedback is given to the subject in a form of a game. A ball appears on the center of a computer monitor with a vertical rectangle (target) to either the right or left side of the screen (Figure 2). The movement of the ball is controlled by the BCI system in which the detection of an attempted movement in either hand will be translated into moving the ball toward the same side. For example, if the target appeared on the left side of the screen and the BCI system detected a movement attempt of the user’s left hand, the ball then moves toward the left. Users are instructed to perform or attempt the same movement that they used during the open-loop task. The FES electrodes are placed on the subject’s affected side over a specific muscle of the forearm. The selection of which muscle to be innervated with FES is dependent on the rehabilitation goal for the subject. For example, if a subject is having a difficulty extending his/her wrist, the FES electrodes are placed over the extensor muscles of the impaired forearm.

The FES neurofeedback is triggered when cortical activity related to attempted movement of the impaired limb is detected by the BCI system, and the subject is cued to attempt movement of the impaired hand. Thus, since both ball movement and FES are controlled by the same set of EEG signals, FES is only applied when the ball moves correctly toward the target on the affected side of the body. This triggering of the FES ensures that only consistent, desired patterns of brain activity associated with attempted movement of the impaired hand are rewarded with feedback from the FES device.

DISCUSSION

The growing population of stroke survivors constitutes an increasing need for new strategies in stroke rehabilitation. Thus, it is imperative to explore novel intervention technologies that present promise to aid in the recovery process of this population. Some studies suggest that noninvasive EEG-based BCI systems hold a potential for facilitating upper extremities motor recovery after stroke (12,13). Although several groups had gave up on the idea of using non-invasive EEG-based BCI systems for control, there might be several implementations of these systems in the context of rehabilitation that yet need to be explored. The active EEG-based BCI-FES system is one example. However, more research and clinical studies are needed to investigate the efficacy of the system in order to be accepted and integrated into regular stroke rehabilitation practice.

REFERENCES

(1) Norrving B, Kissela B. The global burden of stroke and need for a continuum of care. Neurology 2013 Jan 15;80(3 Suppl 2):S5-12.

(2) Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: a systematic review. The Lancet Neurology 2009;8(8):741-754.

(3) Sanchez RJ, Liu J, Rao S, Shah P, Smith R, Rahman T, et al. Automating arm movement training following severe stroke: functional exercises with quantitative feedback in a gravity-reduced environment. IEEE Transactions on neural systems and rehabilitation engineering 2006;14(3):378-389.

(4) Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. The Lancet 2011;377(9778):1693-1702.

(5) Loureiro RC, Harwin WS, Nagai K, Johnson M. Advances in upper limb stroke rehabilitation: a technology push. Med Biol Eng Comput 2011;49(10):1103.

(6) Howlett OA, Lannin NA, Ada L, McKinstry C. Functional electrical stimulation improves activity after stroke: a systematic review with meta-analysis. Arch Phys Med Rehabil 2015;96(5):934-943.

(7) Kawashima N, Popovic MR, Zivanovic V. Effect of intensive functional electrical stimulation therapy on upper-limb motor recovery after stroke: case study of a patient with chronic stroke. Physiotherapy Canada 2013;65(1):20-28.

(8) Wang R. Neuromodulation of effects of upper limb motor function and shoulder range of motion by functional electric stimulation (FES). Operative Neuromodulation: Springer; 2007. p. 381-385.

(9) Meilink A, Hemmen B, Seelen H, Kwakkel G. Impact of EMG-triggered neuromuscular stimulation of the wrist and finger extensors of the paretic hand after stroke: a systematic review of the literature. Clin Rehabil 2008;22(4):291-305.

(10) Ang KK, Guan C. EEG-Based Strategies to Detect Motor Imagery for Control and Rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2017;25(4):392-401.

(11) Wilson JA, Schalk G, Walton LM, Williams JC. Using an EEG-based brain-computer interface for virtual cursor movement with BCI2000. J Vis Exp 2009 Jul 29;(29). pii: 1319. doi(29):10.3791/1319.

(12) Caria A, Weber C, Brötz D, Ramos A, Ticini LF, Gharabaghi A, et al. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology 2011;48(4):578-582.

(13) Young BM, Nigogosyan Z, Remsik A, Walton LM, Song J, Nair VA, et al. Changes in functional connectivity correlate with behavioral gains in stroke patients after therapy using a brain-computer interface device. Frontiers in neuroengineering 2014;7:25.

ACKNOWLEDGMENT

This project is supported in part by UW-Madison Institute for Clinical and Translational Research, and College of Health Sciences, UW-Milwaukee.

 

via The Integration of Brain-Computer Interface (BCI) as Control Module for Functional Electrical Stimulation (FES) Intervention in Post-Stroke Upper Extremity Rehabilitation

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[Chapter 10] UPPER EXTREMITY MOTOR REHABILITATION INTERVENTIONS – EBRSR PDF File.

Chapter 10: Upper Extremity Motor Rehabilitation Interventions Table of contents
Key points ……………………………………………………………………………………………………………….. 4
Modified Sackett Scale ……………………………………………………………………………………………… 8
New to the 19th edition of the Evidence-based Review of Stroke Rehabilitation…………….. 9
Outcome measures definitions………………………………………………………………………………….11
Motor Function……………………………………………………………………………………………… 11
Dexterity ……………………………………………………………………………………………………… 15
Activities of daily living…………………………………………………………………………………… 17
Spasticity …………………………………………………………………………………………………….. 21
Range of motion …………………………………………………………………………………………… 23
Proprioception ……………………………………………………………………………………………… 24
Stroke severity……………………………………………………………………………………………… 25
Muscle strength ……………………………………………………………………………………………. 26
Therapy based interventions …………………………………………………………………………………….27
Neurodevelopmental techniques …………………………………………………………………….. 27
Bilateral arm training……………………………………………………………………………………… 32
Strength training …………………………………………………………………………………………… 41
Task-specific training…………………………………………………………………………………….. 47
Constraint-Induced Movement Therapy (CIMT) ………………………………………………… 53
Trunk restraint ……………………………………………………………………………………………… 69
Stretching programs ……………………………………………………………………………………… 72
Orthotics ……………………………………………………………………………………………………… 75
Mirror Therapy……………………………………………………………………………………………… 79
Mental practice …………………………………………………………………………………………….. 86
Action observation ………………………………………………………………………………………… 91
Music therapy ………………………………………………………………………………………………. 95
Technology based interventions ……………………………………………………………………………….98
Telerehabilitation ………………………………………………………………………………………….. 98
Robotics…………………………………………………………………………………………………….. 100
Virtual reality………………………………………………………………………………………………. 114
Brain computer interfaces…………………………………………………………………………….. 123
EMG biofeedback ……………………………………………………………………………………….. 127
Sensorimotor stimulation………………………………………………………………………………………..132
Neuromuscular electrical stimulation (NMES) …………………………………………………. 132
Transcutaneous electrical nerve stimulation (TENS)………………………………………… 148
Thermal stimulation …………………………………………………………………………………….. 152
Muscle vibration………………………………………………………………………………………….. 155
Additional afferent and peripheral stimulation methods …………………………………….. 158
Invasive central nervous system stimulation ……………………………………………………………163
Invasive cortical and nerve electrode implant stimulation………………………………….. 163
Non-invasive brain stimulation ………………………………………………………………………………..166
Repetitive Transcranial Magnetic Stimulation (rTMS)……………………………………….. 166
Theta burst stimulation (TBS)……………………………………………………………………….. 177
http://www.ebrsr.com Page 3
Transcranial Direct Current Stimulation (tDCS)……………………………………………….. 182
Pharmaceuticals …………………………………………………………………………………………………….197
Botulinum toxin…………………………………………………………………………………………… 197
Steroids …………………………………………………………………………………………………….. 207
Cerebrolysin ………………………………………………………………………………………………. 209
Levodopa…………………………………………………………………………………………………… 211
Atorvastatin………………………………………………………………………………………………… 213
Antidepressants………………………………………………………………………………………….. 215
Central nervous system stimulants………………………………………………………………… 218
Complementary and alternative medicine…………………………………………………………………221
Acupuncture ………………………………………………………………………………………………. 221
Electroacupuncture and transcutaneous electrical acupoint stimulation………………. 227
Meridian acupressure and massage therapy…………………………………………………… 232
References …………………………………………………………………………………………………………….235

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[Abstract] Autonomous Use of the Home Virtual Rehabilitation System: A Feasibility and Pilot Study

Objective: This article describes the findings of a study examining the ability of persons with strokes to use home virtual rehabilitation system (HoVRS), a home-based rehabilitation system, and the impact of motivational enhancement techniques on subjects’ motivation, adherence, and motor function improvements subsequent to a 3-month training program.

Materials and Methods: HoVRS integrates a Leap Motion controller, a passive arm support, and a suite of custom-designed hand rehabilitation simulations. For this study, we developed a library of three simulations, which include activities such as flexing and extending fingers to move a car, flying a plane with wrist movement, and controlling an avatar running in a maze using reaching movements. Two groups of subjects, the enhanced motivation (EM) group and the unenhanced control (UC) group, used the system for 12 weeks in their homes. The EM group trained using three simulations that provided 8–12 levels of difficulty and complexity. Graphics and scoring opportunities increased at each new level. The UC group performed the same simulations, but difficulty was increased utilizing an algorithm that increased difficulty incrementally, making adjustments imperceptible.

Results: Adherence to both the EM and UC protocols exceeded adherence to home exercise programs described in the stroke rehabilitation literature. Both groups demonstrated improvements in upper extremity function. Intrinsic motivation levels were better for the EM group and motivation levels were maintained for the 12-week protocol.

Conclusion: A 12-week home-based training program using HoVRS was feasible. Motivational enhancement may have a positive impact on motivation, adherence, and motor outcome.

 

via Autonomous Use of the Home Virtual Rehabilitation System: A Feasibility and Pilot Study | Games for Health Journal

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[VIDEO] Mirror Therapy Combined With Electrical Stimulation Using SaeboStim Micro – YouTube

https://www.saebo.com

Saebo, Inc. is a medical device company primarily engaged in the discovery, development and commercialization of affordable and novel clinical solutions designed to improve mobility and function in individuals suffering from neurological and orthopedic conditions. With a vast network of Saebo-trained clinicians spanning six continents, Saebo has helped over 100,000 clients around the globe achieve a new level of independence.

In 2001, two occupational therapists had one simple, but powerful goal – to provide neurological clients access to transformative and life changing products.

At the time, treatment options for improving arm and hand function were limited. The technology that did exist was expensive and inaccessible for home use. With inadequate therapy options often leading to unfavorable outcomes, health professionals routinely told their clients that they have “reached a plateau” or “no further gains can be made”. The founders believed that it was not the clients who had plateaued, but rather their treatment options had plateaued.

Saebo’s commitment – “No Plateau in Sight” – was inspired by this mentality; and the accessible, revolutionary solutions began.

Saebo’s revolutionary product offering was based on the latest advances in rehabilitation research. From the SaeboFlex which allows clients to incorporate their hand functionally in therapy or at home, to the SaeboMAS, an unweighting device used to assist the arm during daily living tasks and exercise training, “innovation” and “affordability” can now be used in the same sentence.

Over the last ten years, Saebo has grown into a leading global provider of rehabilitative products created through the unrelenting leadership and the strong network of clinicians around the world. As we celebrate our history and helping more than 100,000 clients regain function, we are growing this commitment to affordability and accessibility even further by making our newest, most innovative products more accessible than ever.

via Mirror Therapy Combined With Electrical Stimulation Using SaeboStim Micro – YouTube

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