Posts Tagged motor learning

[ARTICLE] The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a study protocol for a randomized controlled trial to test early and long-term efficacy and to identify determinants of response – Full Text

Abstract

Background

Stroke is a leading cause of long-term disability. Cost-effective post-stroke rehabilitation programs for upper limb are critically needed. Brain-Computer Interfaces (BCIs) which enable the modulation of Electroencephalography (EEG) sensorimotor rhythms are promising tools to promote post-stroke recovery of upper limb motor function. The “Promotoer” study intends to boost the application of the EEG-based BCIs in clinical practice providing evidence for a short/long-term efficacy in enhancing post-stroke hand functional motor recovery and quantifiable indices of the participants response to a BCI-based intervention. To these aims, a longitudinal study will be performed in which subacute stroke participants will undergo a hand motor imagery (MI) training assisted by the Promotoer system, an EEG-based BCI system fully compliant with rehabilitation requirements.

Methods

This longitudinal 2-arm randomized controlled superiority trial will include 48 first ever, unilateral, subacute stroke participants, randomly assigned to 2 intervention groups: the BCI-assisted hand MI training and a hand MI training not supported by BCI. Both interventions are delivered (3 weekly session; 6 weeks) as add-on regimen to standard intensive rehabilitation. A multidimensional assessment will be performed at: randomization/pre-intervention, 48 h post-intervention, and at 1, 3 and 6 month/s after end of intervention. Primary outcome measure is the Fugl-Meyer Assessment (FMA, upper extremity) at 48 h post-intervention. Secondary outcome measures include: the upper extremity FMA at follow-up, the Modified Ashworth Scale, the Numeric Rating Scale for pain, the Action Research Arm Test, the National Institute of Health Stroke Scale, the Manual Muscle Test, all collected at the different timepoints as well as neurophysiological and neuroimaging measures.

Discussion

We expect the BCI-based rewarding of hand MI practice to promote long-lasting retention of the early induced improvement in hand motor outcome and also, this clinical improvement to be sustained by a long-lasting neuroplasticity changes harnessed by the BCI-based intervention. Furthermore, the longitudinal multidimensional assessment will address the selection of those stroke participants who best benefit of a BCI-assisted therapy, consistently advancing the transfer of BCIs to a best clinical practice.

Trial registration

Name of registry: BCI-assisted MI Intervention in Subacute Stroke (Promotoer).

Trial registration number: NCT04353297; registration date on the ClinicalTrial.gov platform: April, 15/2020.

Peer Review reports

Background

Stroke is a major public health and social care concern worldwide [1]. The upper limb motor impairment commonly persists after stroke, and it represents the major contribution to long-term disability [2]. It has been estimated that the main clinical predictor of whether a patient would come back to work is the degree of upper extremity function [3]. Despite the intensive rehabilitation, the variability in the nature and extent of upper limb recovery remains a crucial factor affecting rehabilitation outcomes [4].

Electroencephalography (EEG)-based Brain-Computer Interface (BCI) is an emerging technology that enables a direct translation of brain activity into motor action [5]. Recently, EEG-based BCIs have been recognized as potential tools to promote functional motor recovery of upper limbs after stroke (for review see [6]). Several randomized controlled trials have shown that stroke patients can learn to modulate their EEG sensorimotor rhythms [7] to control external devices and this practice might facilitate neurological recovery both in subacute and chronic stroke phase [8,9,10].

We were previously successful in the design and validation of an EEG sensorimotor rhythms–based BCI combined with realistic visual feedback of upper limb to support hand motor imagery (MI) practice in stroke patients [1112]. Our previous pilot randomized controlled study [8] with the participation of 28 subacute stroke patients with severe motor deficit, suggested that 1 month BCI-assisted MI practice as an add-on intervention to the usual rehabilitation care was superior with respect to the add-on, 1 month MI training alone (ie., without BCI support) in improving hand functional motor outcomes (indicated by the significantly higher mean score at upper extremity Fugl-Meyer scale in the BCI with respect to control group). A greater involvement of the ipsilesional hemisphere, as reflected by a stronger motor-related EEG oscillatory activity and connectivity in response to MI of the paralyzed trained hand was also observed only in the BCI-assisted MI training condition. These promising findings corroborated the idea that a relatively low-cost technique (i.e. EEG-based BCI) can be exploited to deliver an efficacious rehabilitative intervention such as MI training and prompted us to undertake a translational effort by implementing an all-in-one BCI-supported MI training station– the Promotoer [13].

Yet, important questions remain to be addressed in order to improve the clinical viability of BCIs such as defining whether the expected early improvements in functional motor outcomes induced by the BCI-assisted MI training in subacute stroke [8] can be sustained in a long-term as it has been shown for other BCI-based approaches in chronic stroke patients [1014]. This requires advancements in the knowledge on brain functional re-organization early after stroke and on how this re-organization would correlate with the functional motor outcome (evidence-base medicine). Last but not least, the definition of the determinants of the patients response to treatment is paramount to optimize the process of personalized medicine in rehabilitation. We will address these questions by carrying out a randomized trial to eventually establish the fundamentals for a cost-effective use of EEG-based BCI technology to deliver a rehabilitative intervention such as the MI in hospitalized stroke patients.

Aim and hypotheses

The “Promotoer” study is a randomized controlled trial (RCT) designed to provide evidence for a significant early improvement of hand motor function induced by the BCI-assisted MI training operated via the Promotoer and for a persistency (up to 6 months) of such improvement. Task-specific training was reported to induce long-term improvements in arm motor function after stroke [15,16,17]. Thus, our hypothesis is that the BCI-based rewarding of hand MI tasks would promote long-lasting retention of early induced positive effect on motor performance with respect to MI tasks practiced in an open loop condition (ie, without BCI). Accordingly, the primary aim of the “Promotoer” RCT will be first to determine whether the BCI based intervention (MI-BCI) administered by means of a BCI system fully compatible with a clinical setting (the Promotoer), is superior to a non-BCI assisted MI training (MI Control) in improving hand motor function outcomes in sub-acute stroke patients admitted to the hospital for their standard rehabilitation care; secondly, we will test whether the efficacy of BCI-based intervention on hand motor function outcomes is sustained long-term after the end of intervention (6 months follow-up). A further hypothesis is that such clinical improvement would be sustained by a long-lasting neuroplasticity changes as harnessed by the BCI–based intervention. This hypothesis rises from current evidence for an early enhancement of post-stroke plastic changes enabled by BCI-based trainings [8,9,10]. To test this hypothesis, a longitudinal assessment of the brain network organization derived from advanced EEG signal processing (secondary objective) will be performed.

The heterogeneity of stroke makes prediction of treatment responders a great challenge [18]. The potential value of a combination of neurophysiological and neuroimaging biomarkers with the clinical assessment in predicting post-stroke motor recovery has been recently highlighted [19]. Our hypothesis is that the longitudinal combined functional, neurophysiological and neuroimaging assessment over 6 months from the intervention will allow for insights into biomarkers and potential predictors of patients response to the BCI-Promotoer training (secondary aim). To this purpose, well-recognized factors contributing to recovery after stroke such as the relation between clinical profile, lesion characteristics and patterns of post-stroke motor cortical re-organization (eg., ipsilesional/contralesional primary and non-primary motor areas, cortico-spinal tract integrity, severity of motor deficits at baseline; for review see [19]) will be taken into account.[…}

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figure2
The Promotoer system. The Promoter is equipped with a computer, a commercial wireless EEG/EMG system (g.MOBIlab, g.tec medical engineering GmbH Austria), a screen for the therapist feedback (for the electroencephalographic – EEG activity and electromyographic- EMG activity monitoring) and screen for the ecological feedback to the participant; this ecological feedback is delivered by means of a custom software program that provides for (personalized) visual representation of the participant’s own hands. As such, this software allows the therapists to create an artificial reproduction of a given participant’s hand and forearm by adjusting a digitally created image in shape, size, skin color and orientation to match as much as possible the real hand and arm of the participant. Real-time feedback is provided by means of BCI2000 software [40]. The degree of EEG desynchronization over selected electrodes within selected frequencies (BCI control features) determines the vertical velocity of the cursor on the therapist’s screen and it operates the “virtual” hand software accordingly. The image is original as it is owned by the authors

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[ARTICLE] Learning and transfer of complex motor skills in virtual reality: a perspective review – Full Text

Abstract

The development of more effective rehabilitative interventions requires a better understanding of how humans learn and transfer motor skills in real-world contexts. Presently, clinicians design interventions to promote skill learning by relying on evidence from experimental paradigms involving simple tasks, such as reaching for a target. While these tasks facilitate stringent hypothesis testing in laboratory settings, the results may not shed light on performance of more complex real-world skills. In this perspective, we argue that virtual environments (VEs) are flexible, novel platforms to evaluate learning and transfer of complex skills without sacrificing experimental control. Specifically, VEs use models of real-life tasks that afford controlled experimental manipulations to measure and guide behavior with a precision that exceeds the capabilities of physical environments. This paper reviews recent insights from VE paradigms on motor learning into two pressing challenges in rehabilitation research: 1) Which training strategies in VEs promote complex skill learning? and 2) How can transfer of learning from virtual to real environments be enhanced? Defining complex skills by having nested redundancies, we outline findings on the role of movement variability in complex skill acquisition and discuss how VEs can provide novel forms of guidance to enhance learning. We review the evidence for skill transfer from virtual to real environments in typically developing and neurologically-impaired populations with a view to understanding how differences in sensory-motor information may influence learning strategies. We provide actionable suggestions for practicing clinicians and outline broad areas where more research is required. Finally, we conclude that VEs present distinctive experimental platforms to understand complex skill learning that should enable transfer from therapeutic practice to the real world.

Introduction

The goal of rehabilitation interventions for clients with neurological impairments is to (re)learn motor skills during therapeutic practice and transfer those improvements to functional activities in daily life. Researchers and clinicians seek to understand the content and structure of practice that facilitates such learning and transfer for different tasks, environmental contexts and clinical populations []. Although (re)learning activities of daily living is the focus of neurological rehabilitation, much of the evidence base for therapeutic interventions stems from basic or clinical research on simple experimentally-controlled tasks, such as reaching to a target in the horizontal plane or learning a finger tapping sequence. While these simplified tasks are very different from the tasks of daily life, they facilitate precise quantification of performance variables and stringent hypothesis testing, providing insights into basic principles of motor control and learning. However, their deliberately reduced testbeds lack a feature that is pervasive in real-world tasks: the affordance of multiple options to achieve a movement goal []. Hence, principles of learning derived from these simple movement paradigms may not translate into useful transfer-oriented principles for rehabilitation [].

With some exceptions, e.g., Constraint-Induced Movement Therapy [], few rehabilitation interventions can consistently demonstrate evidence for transfer from practiced tasks to non-treatment contexts. This is also true for the rehabilitation-based use of virtual environments (VEs): computer hardware and software systems that generate simulations of real or imagined environments with which participants interact using their own movements []. VEs differ according to viewing medium, level of immersion, and type of interaction []. While practice in a variety of VEs offers promising evidence for skill acquisition as compared to conventional interventions in many rehabilitation populations, [e.g. ] the focus has been predominantly on training simplified movements. This may be one reason why successful transfer of skill learning to non-practiced tasks and real-life contexts often remains a challenge []. As such, the design of both virtual and conventional interventions requires greater understanding of how humans acquire, retain and transfer real-world skills. We propose that VEs themselves can serve as useful experimental platforms to gain this knowledge as they allow the study of these complex skills with sufficient experimental control to draw scientifically tractable conclusions [].

Complex real-world tasks have nested redundancy

In the motor learning literature, the adjective “complex” is often treated synonymously with “difficult” []. For example, a task can be labelled as difficult or complex when reaction time or movement time are relatively long, when skill improvement requires long hours of practice, or when the task poses high demands on the learner’s attention and memory []. To sharpen the discussion, we reserve the term ‘complex’ for tasks with nested redundancy. Redundancy is present when there is a greater number of execution variables than variables that define the result of the task. The well-known example for motor redundancy is pointing to a target with one’s fingertip, which can be achieved with many different joint configurations, because the arm (without the hand) has 7 degrees of freedom, while the target is defined in 3 degrees of freedom.

However, real-world tasks have another level of redundancy that lies in the task itself. Imagine you are asked to point to a line, where each location on the line is equally correct. Here, the task itself allows an infinite number of “solutions”. And of course, each of those solutions can be achieved with an infinite number of joint configurations. Further, each of the points on the target line can be reached with an infinite number of trajectories from the starting point towards the target line. It is these nested redundancies that characterize the challenge and the richness of real-world tasks. Figure 1 illustrates these nested redundancies with the example of hammering a target on an anvil. The traces are the original recordings of Bernstein from the 1930s, showing the tip of a hammer in the sagittal plane []. The added simplified arm with three joints can take on infinite configurations for any position of the hammer endpoint in the 2D plane (intrinsic redundancy). Next, the trajectories of the repeated endpoint actions take on many different shapes, in fact infinitely many shapes, while all of them hit the anvil (extrinsic redundancy). Finally, the anvil or target itself is not a point but a line, where any contact is regarded as a successful hit (task redundancy) []. Examples for these nested redundancies are ubiquitous in real life, from combing one’s hair to cutting a steak with a knife and fork. Performers must choose (implicitly or explicitly) from an infinite range of possible solutions, each leading to successful task accomplishment []. We define such actions as ‘complex’ skills. To gain insight into these ever-present control challenges and opportunities, scientific inquiry must move beyond simple tasks where redundancy has been purposefully removed and begin to examine more complex tasks.

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Fig. 1

Fig. 1Nested redundancies in a hammering task

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[ARTICLE] Locomotor skill acquisition in virtual reality shows sustained transfer to the real world – Full Text

Abstract

Background

Virtual reality (VR) is a potentially promising tool for enhancing real-world locomotion in individuals with mobility impairment through its ability to provide personalized performance feedback and simulate real-world challenges. However, it is unknown whether novel locomotor skills learned in VR show sustained transfer to the real world. Here, as an initial step towards developing a VR-based clinical intervention, we study how young adults learn and transfer a treadmill-based virtual obstacle negotiation skill to the real world.

Methods

On Day 1, participants crossed virtual obstacles while walking on a treadmill, with the instruction to minimize foot clearance during obstacle crossing. Gradual changes in performance during training were fit via non-linear mixed effect models. Immediate transfer was measured by foot clearance during physical obstacle crossing while walking over-ground. Retention of the obstacle negotiation skill in VR and retention of over-ground transfer were assessed after 24 h.

Results

On Day 1, participants systematically reduced foot clearance throughout practice by an average of 5 cm (SD 4 cm) and transferred 3 cm (SD 1 cm) of this reduction to over-ground walking. The acquired reduction in foot clearance was also retained after 24 h in VR and over-ground. There was only a small, but significant 0.8 cm increase in foot clearance in VR and no significant increase in clearance over-ground on Day 2. Moreover, individual differences in final performance at the end of practice on Day 1 predicted retention both in VR and in the real environment.

Conclusions

Overall, our results support the use of VR for locomotor training as skills learned in a virtual environment readily transfer to real-world locomotion. Future work is needed to determine if VR-based locomotor training leads to sustained transfer in clinical populations with mobility impairments, such as individuals with Parkinson’s disease and stroke survivors.

Background

In recent years, virtual reality (VR) has been increasingly used to provide engaging, interactive, and task-specific locomotor training [1,2,3,4,5,6,7,8]. These studies have simulated walking in different environments such as parks or streets [34], walking on a slope [3], or walking while avoiding obstacles [3,4,57]. VR-based locomotor training frequently includes obstacle negotiation because it is an essential locomotor skill in the community [457] and tripping over obstacles is a common cause of falls in many patient populations [9]. The clinical application of VR-based training interventions is predicated on the idea that practice in VR will lead to lasting changes in trained skills and that these changes will influence real-world behavior. Therefore, understanding how locomotor skills acquired in VR are retained and how these skills generalize to the real world is critical for determining the long-term utility of VR for locomotor rehabilitation.

The presence of lasting changes in a motor skill as a result of practice is a hallmark of motor learning and this retention process has been examined across a wide variety of real and virtual learning contexts. Retention of motor skills has been examined in response to VR training, particularly in fields such as flight and medical procedural training. For example, complex surgical and medical skills are performed faster and more accurately during a retention session following a single day of VR-based training [10,11,12,13]. Retention of locomotor skills is often explored in studies that analyze how people adapt to external perturbations such as a split-belt treadmill which has separate belts for the right and left legs [14,15,16], elastic force fields [17], robotic exoskeletons [18], or added loads [19]. For instance, studies of split-belt treadmill adaptation have revealed that the increases in step length asymmetry observed during initial exposure to the belts moving at different speeds significantly decreased with subsequent exposures to the device [14,15,16]. A recent study by Krishnan and colleagues also investigated locomotor skill learning during a tracking task in which participants were instructed to match a pre-defined target of hip and knee trajectories as accurately as possible during the swing phase of the gait [20]. They found that the reduction in tracking error achieved through practice is retained the following day. Although motor skill learning in VR and locomotor learning have been examined in isolation, it remains to be seen how locomotor skills are acquired and retained following training in a virtual environment.

Skill transfer, which is defined as “the gain or loss in the capability for performance in one task as a result of practice or experience on some other task” [21], is another key feature of motor learning. Skill transfer is particularly critical when skill acquisition occurs in a context that differs from the environment in which the skill is to be expressed. One way in which skill transfer has been evaluated during motor learning is by measuring how the adaptation of reaching in a robot-generated force field generalizes to unconstrained reaching. This work has shown that adaptation to reaching in a curl-field leads to increased curvature during reaching in free space [2223]. Moreover, studies of treadmill-based locomotor skill learning often evaluate transfer of learned skills from treadmill walking to over-ground. For example, during split-belt treadmill adaptation, the learned changes in interlimb symmetry partially transfer to over-ground walking [24]. Further, VR-based training of obstacle negotiation on a treadmill led to increased walking speeds in the lab [57] and community [4]. However, the evaluation of transfer in these VR-based training studies was based on outcome measures such as walking speed that did not reflect the objective of the training task, which was the control of foot clearance obstacle negotiation. Therefore, it remains to be seen if the elements of skill from VR-training transfer to over-ground walking.

Underlying individual differences in learning can influence motor skill retention and transfer to new environments. For example, a recent study demonstrated that healthy older adults and people post-stroke who acquire a motor sequence skill at a faster rate also show greater retention of that skill [25]. Similarly, the rate of skill acquisition for a reaching task during early training predicts faster trial completion time at 1-month follow-up [26]. Lastly, the magnitude of improvements in reaching speed during skill acquisition predicts long-term changes in reaching speed in healthy individuals [27]. Studies of individual differences in transfer have most often sought to understand how the practice of a skill with one limb influences performance of the same skill with the untrained limb. For example, interlimb transfer of motor skills acquired through visuomotor adaptation varies with handedness [28] and individual differences in baseline movement variability [29]. However, far less work has sought to understand how individual differences in skill acquisition affect the transfer of learned skills to new environments. Overall, the influence of individual differences in skill acquisition on locomotor skill retention and sustained transfer has yet to be determined.

Here, we determined how individual differences in locomotor skill learning during virtual reality treadmill-based training influence retention and transfer of learned skills to over-ground walking in the real world. We used a VR-based version of a previously established precision obstacle negotiation task [3031] and asked 1) whether healthy young adults could learn to minimize clearance during virtual obstacle negotiation, 2) if the learned skill transferred to over-ground walking, 3) if the learned skill was retained in both VR and the real world after 24 h, and 4) if individual differences in the amount or rate of skill acquisition could predict retention and transfer. We hypothesized that 1) participants would reduce foot clearance in VR during practice on Day 1 and that 2) the reduced foot clearance in VR would transfer to over-ground obstacle negotiation. We also hypothesized that 3) the reduction in foot clearance in VR and over-ground would be retained in each environment after a 24-h retention period. Lastly, given that the rate and magnitude of the performance improvement during skill acquisition have been established as predictors of skill retention in previous studies, we also hypothesized that 4) these measures would predict retention of the learned skill in VR and over-ground. Given the growing use of VR for motor skill learning, our results may provide a unique opportunity to understand the factors that influence how training in VR might lead to long-term improvements in skilled locomotion. […]

 

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[Abstract] Task-oriented Motor Learning in Upper Extremity Rehabilitation Post Stroke

Abstract

Background: Upper extremity deficits are the most popular symptoms following stroke. Task-oriented training has the ability to increase motor area excitability in the brain, which can stimulate the recovery of motor control.

Objective: This study was aimed to examine the efficiency of the task-oriented approach on paretic upper extremity following a stroke, and to identify efficient treatment dosage in those populations.

Method: We searched through PubMed, Scopus, Physiotherapy Evidence Database (PEDro), National Rehabilitation Information (REHABDATA), and Web of Science databases. Randomized clinical trials (RCTs) and pseudo-RCTs those investigating upper extremity in patients with stroke published in English language were selected. Different scales and measurement methods to assess range of motion, strength, spasticity, and upper extremity function were considered. The quality assessment of included articles was evaluated utilizing the PEDro scale. Effect sizes were calculated.

Results: Six RCTs were included in the present study. The quality assessment for included studies ranged from 6 to 8 with 6.5 as a median. A total of 456 post-stroke patients, 41.66% of which were women, were included in all studies. The included studies demonstrated a meaningful influence of task-oriented training intervention on the hemiplegic upper limb motor functions but not spasticity post-stroke.

Conclusion: Task-oriented training does not produce a superior effect than other conventional physical therapy interventions in treating upper extremity in patients with stroke. There is no evidence supporting the beneficial effect of task-oriented on spasticity. Task-oriented training with the following dosage 30 to 90 minutes/session, 2 to 3 sessions weekly for 6 to 10 weeks may improve motor function and strength of paretic upper extremity post-stroke.

via Task-oriented Motor Learning in Upper Extremity Rehabilitation Post Stroke – Anas R. Alashram, Giuseppe Annino, Nicola Biagio Mercuri,

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[ARTICLE] What is the impact of user affect on motor learning in virtual environments after stroke? A scoping review – Full Text

Abstract

Purpose

The purported affective impact of virtual reality (VR) and active video gaming (AVG) systems is a key marketing strategy underlying their use in stroke rehabilitation, yet little is known as to how affective constructs are measured or linked to intervention outcomes. The purpose of this scoping review is to 1) explore how motivation, enjoyment, engagement, immersion and presence are measured or described in VR/AVG interventions for patients with stroke; 2) identify directional relationships between these constructs; and 3) evaluate their impact on motor learning outcomes.

Methods

A literature search was undertaken of VR/AVG interventional studies for adults post-stroke published in Medline, PEDro and CINAHL databases between 2007 and 2017. Following screening, reviewers used an iterative charting framework to extract data about construct measurement and description. A numerical and thematic analytical approach adhered to established scoping review guidelines.

Results

One hundred fifty-five studies were included in the review. Although the majority (89%; N = 138) of studies described at least one of the five constructs within their text, construct measurement took place in only 32% (N = 50) of studies. The most frequently described construct was motivation (79%, N = 123) while the most frequently measured construct was enjoyment (27%, N = 42). A summative content analysis of the 50 studies in which a construct was measured revealed that constructs were described either as a rationale for the use of VR/AVGs in rehabilitation (76%, N = 38) or as an explanation for intervention results (56%, N = 29). 38 (76%) of the studies proposed relational links between two or more constructs and/or between any construct and motor learning. No study used statistical analyses to examine these links.

Conclusions

Results indicate a clear discrepancy between the theoretical importance of affective constructs within VR/AVG interventions and actual construct measurement. Standardized terminology and outcome measures are required to better understand how enjoyment, engagement, motivation, immersion and presence contribute individually or in interaction to VR/AVG intervention effectiveness.

Introduction

An increasing evidence base supports the use of virtual reality (VR) and active video gaming (AVG) systems to promote motor learning in stroke rehabilitation [1234]. However, practical and logistical barriers to VR/AVG implementation in clinical sites have been well described [567]. To support their use, researchers and developers often emphasize the potential advantages of VR/AVG systems over conventional interventions, including that these technologies may enhance a patient’s affective experience in therapy for the purpose of facilitating recovery [891011]. Examining the role of affective factors for motor learning is an emerging area of emphasis in rehabilitation [212131415].

VR/AVG use may enhance patients’ motivation to participate in rehabilitation as well as their engagement in therapeutic tasks. Motivation encourages action toward a goal by eliciting and/or sustaining goal-directed behavior [16]. Motivation can be intrinsic (derived from personal curiosity, importance or relevance of the goal) or extrinsic (elicited via external reward) [17]. Engagement is a cognitive and affective quality or experience of a user during an activity [16]. Many characteristics of VR/AVG play can contribute to user motivation and engagement, such as novelty, salient audiovisual graphics, interactivity, feedback, socialization, optimal challenge [14], extrinsic rewards, intrinsic curiosity or desire to improve in the game, goal-oriented tasks, and meaningful play [18].

Motivation and engagement are hypothesized to support motor learning either indirectly, through increased practice dosage leading to increased repetitive practice, or directly, via enhanced dopaminergic mechanisms influencing motor learning processes [1516]. Yet evidence is required to support these claims. A logical first step is to understand how these constructs are being measured within VR/AVG intervention studies. Several studies have used practice dosage or intensity as an indicator of motivation or engagement [192021]. To the authors’ knowledge, few have specifically evaluated the indirect mechanistic pathway by correlating measurement of patient motivation or engagement in VR/AVGs with practice dosage or intensity. While participants in VR/AVG studies report higher motivation as compared to conventional interventions [222324], conclusions regarding the relationship between motivation and intervention outcomes are limited by lack of consistency and rigour in measurement, including the use of instruments with poor psychometric properties [2223].

The body of research exploring the direct effects of engagement or motivation on motor learning is still in its infancy. Lohse et al. [16] were the first to evaluate whether a more audiovisually enriched as compared to more sterile version of a novel AVG task contributed to skill acquisition and retention in typically developing young adults, finding that participants who played under the enriching condition had greater generalized learning and complex skill retention. Self-reported engagement (User Engagement Scale; UES) was higher in the enriched group, but the only difference in self-reported motivation was in the Effort subscale of the Intrinsic Motivation Inventory (IMI), where the enriched group reported less effort as compared to the sterile group. The authors did not find a significant correlation between engagement, motivation and retention scores. A follow-up study using electroencephalography did not replicate the finding that the more enriched practice condition enhanced learning, it did show that more engaged learners had increased information processing, as measured by reduced attentional reserve [25].

Enjoyment, defined as ‘the state or process of taking pleasure in something’ [26], has less frequently been the subject of study in motor learning research, but has become popular as a way of describing patient interaction with VR/AVGs. Enjoyment may be hypothesized to be a precursor to both motivation and engagement. Given that the prevailing marketing of VR/AVGs is that they are ‘fun’ and ‘enjoyable’ [131427], it is important to evaluate its measurement in the context of other constructs.

Motivation, engagement and enjoyment in VR/AVGs may be influenced by the additional constructs of immersion and presence. Immersion is defined as “the extent to which the VR system succeeds in delivering an environment which refocuses a user’s sensations from the real world to a virtual world” [1328]. Immersion is considered as an objective construct referring to how the computational properties of the technology can deliver an illusion of reality through hardware, software, viewing displays and tracking capabilities [2930]. A recent systematic review [13] could not conclusively state effect of immersion on user performance. Immersion is distinct from presence, defined as the “psychological product of technological immersion” [31]. Presence is influenced by many factors, including the characteristics of the user, the VR/AVG task, and the VR/AVG system [28]. While presence is thought to be related to enhanced motivation and performance [32], relationships between this and other constructs of interest require exploration. Table 1 outlines definitions of constructs of interest to this scoping review.

Table 1

Construct definitions

Construct

Definition

Reference

Motivation

Motivation encourages action toward a goal by eliciting and/or sustaining goal-directed behavior.

[16]

Engagement

Engagement is a cognitive and affective quality or experience of a user during an activity.

[16]

Enjoyment

The state or process of taking pleasure in something.

[26]

Immersion

The extent to which the VR system succeeds in delivering an environment which refocuses a user’s sensations from the real world to a virtual world.

[1328]

Presence

The psychological product of technological immersion.

[31]

The purpose of this scoping review is to explore the impact of these affective constructs on motor learning after stroke. This greater understanding will enhance the clinical rationale for VR/AVG use and inform directions for subsequent research. Specifically, our objectives were to:

  1. 1.

    Describe how VR/AVG studies measure or report client enjoyment, motivation, engagement, immersion and presence.

  2. 2.

    Evaluate the extent to which motivation, enjoyment, engagement, immersion, and presence impact motor learning.

  3. 3.

    Propose directional relationships between enjoyment, motivation, engagement, immersion, presence and motor learning.

[…]

 

Continue —> What is the impact of user affect on motor learning in virtual environments after stroke? A scoping review | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 2Proposed relationships between the five constructs and motor learning

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[SlideShare] Theories of motor learning

Published on Apr 11, 2018

Motor learning is the understanding of acquisition and/or modification of movement. 
As applied to patients, motor learning involves the reacquisition of previously learned movement skills that are lost due to pathology or sensory, motor, or cognitive impairments. This process is often referred to as recovery of function. 

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[ARTICLE] The effect of virtual reality-based balance training on motor learning and postural control in healthy adults: a randomized preliminary study – Full Text

Abstract

Background

Adults with sedentary lifestyles seem to face a higher risk of falling in their later years. Several causes, such as impairment of strength, coordination, and cognitive function, influence worsening health conditions, including balancing ability. Many modalities can be applied to improve the balance function and prevent falling. Several studies have also recorded the effects of balance training in elderly adults for fall prevention. Accordingly, the aim of this study is to define the effect of virtual reality-based balance training on motor learning and postural control abilities in healthy adults.

Methods

For this study, ten subjects were randomly allocated into either the conventional exercise (CON) or the virtual reality (VR) group. The CON group underwent physical balance training, while the VR group used the virtual reality system 4 weeks. In the VR group, the scores from three game modes were utilized to describe the effect of motor learning and define the learning curves that were derived with the power law function. Wilcoxon Signed Ranks Test was performed to analyze the postural control in five standing tasks, and data were collected with the help of a force plate.

Results

The average score was used to describe the effect of motor learning by deriving the mathematical models for determining the learning curve. Additionally, the models were classified into two exponential functions that relied on the aim and requirement skills. A negative exponential function was observed in the game mode, which requires the cognitive-motor function. In contrast, a positive exponential function was found in the game with use of only the motor skill. Moreover, this curve and its model were also used to describe the effect of learning in the long term and the ratio of difficulty in each game. In the balance performance, there was a significant decrease in the center of pressure parameters in the VR group, while in the CON group, there was a significant increase in the parameters during some foot placements, especially in the medio-lateral direction.

Conclusion

The proposed VR-based training relies on the effect of motor learning in long-term training though different kinds of task training. In postural analysis, both exercise programs are emphasized to improve the balance ability in healthy adults. However, the virtual reality system can promote better outcomes to improve postural control post exercising.

Trial registration Retrospectively registered on 25 April 2018. Trial number TCTR20180430005

Electronic supplementary material

The online version of this article (10.1186/s12938-018-0550-0) contains supplementary material, which is available to authorized users.

Background

The incidence of falls can occur in people of all ages and is not exclusively restricted to the elderly population []. Although the causes of falls are different for each age group, the decline in balance ability is a major factor for the high risk of falls. In older people, the decline in balance ability may occur due to physiological deterioration, pathological factors, problems of ambulation, and endurance reduction []. In addition, the physical activity level of children and middle-aged adults has decreased due to the development of technology, which has resulted in restriction of movement. This has led to the worsening of health conditions due to the deterioration of the neurotransmitter system [] and muscle mass and strength [], giving rise to chronic diseases [] as well as cognitive decline [], which may induce a higher risk of falls in the future. People who suffer from these tend to get injured easily, which results in worsening of self-efficacy and functional dysfunction, even though they are disturbed by a small disturbance []. Increasing physical activity, such as exercise, has a positive effect on several aspects, including postural stability and falling prevention [].

Exercising is important, as it improves humans’ individual or systematic system, which is related to balance performance []. Exercises employ help prevent physiological deterioration by increasing strength and endurance of the body. For example, challenging the sensory system during postural tasks can enhance balance ability by reweighting the functional sensory inputs []. However, significant differences have been observed among various exercise programs, and some exercises have little effect on the balance function []. Balance exercise programs may be made ineffective because of several reasons. First, various physiological systems are used to achieve the postural task []. Second, the activities, which require balancing ability, can be achieved by coordinating between motor skills and cognitive activities []. Moreover, the training program with clinical guidelines is more effective than the program without any instruction []. Therefore, a combination of the exercise approach and the feedback during training process is used to improve the body’s functional ability, including balance performance [].

Using the gaming with the biofeedback system, such as the virtual reality (VR) system, is widely used for rehabilitation []. It is due to the fact that the VR system can make the treatment more interesting, reduce the difficulty of rehabilitation, and increase safety []. One advantage of VR-based training is that this technology allows altering the neural organization, encouraging neuroplastic changes in neurological patients [], reducing the fear of falling, and transferring into the real-world task through motor learning []. However, some VR-based balance training requires a specific balance platform, including Wii Fit balance board, to supply the sensory feedback information that may be restricted during the training process due to the requirement of a specific movement []. For this reason, popular sensors, e.g., the Microsoft Kinect sensor, have been used to show improvement in balance ability in several studies. This is due to the fact that Kinect sensor provides three-dimensional positions without using markers. These positions are used as input for the VR-system to improve balance function and reduce the fear of falling in older adults [].

In several studies, there were significant differences in clinical balance measures among participants who had trained with the help of conventional balance exercises, including the VR system []. Additionally, most studies focused on their applications in improving balance for patients with neurological disorders [] or elderly people []. Therefore, the aim of this study is to investigate the effects of VR-based balance training in healthy adults through motor learning and postural control. The questions included in the proposed study are (a) how does the VR-based balance exercise rely on the effect of motor learning? (b) how do the different exercise modalities influence the impairment of balance ability through comparison of balance performance before and after exercise? We hypothesize that the VR system affects postural control through motor learning. In addition, both balance exercise programs influence the postural control, but the balance performance in the VR-based balance exercise is better than the outcome of the conventional exercise.

Methods

Participants

The experiment in this study was designed as the pilot study. Community-dwelling healthy adults around the area of Mahidol University were recruited for the study. The inclusion criteria were (a) 40–60 years of age, (b) no history of injuries or diseases that influence balance function, (c) no intake of medications that affect postural control system, at least 12 h prior to the experiment, (d) no alcohol consumption 12 h prior to the experiment. The exclusion criteria were (a) individuals with dependent ambulation, (b) individuals who cannot communicate in the Thai language, and (c) individuals who have any disease that affects balance function.

Prior to data collection, all participants signed informed consent, which was approved by the Mahidol University Central Institutional Review Board (MU-IRB: 2014/112.1508). Demographic data and health information of the participants were obtained, following which they were randomly categorized into two groups, the virtual reality exercise (VR) group and the conventional balance exercise (CON) group, by blindly drawing a sealed piece of paper. The VR group (n = 5) received the dual-task virtual-reality balance training system (DTVRBT), while the CON group (n = 5) was assigned the conventional balance exercise.

Protocol

The experimental protocol comprised three steps: the pre-test of balance performance, the balance training session, and the post-test for the evaluation of the balance ability after training. In the study, five standing tasks, including standing unsupported with eyes open (EO) and close (EC) conditions, standing with both feet together, tandem, and one-leg stance were evaluated. Results of balance evaluation in each task were collected for 10 s/trial, with three trials, and the testing focused on the dominant leg in tandem and the one-leg stance. The total of time duration for data analysis was 30 s. In this study, the MatScan® model 3150 (Massachusetts, USA) was used to assess the center-of-pressure (CoP) in the anterior–posterior (AP) and medio-lateral (ML) directions with the sampling rate was 64 Hz. The data of each subject was exported with the Sway Analysis Module (SAM™). The training session started after 1 week of completion of the pre-test, and the post-test was performed within 1 week of finishing the training session. All participants received twelve 45-min sessions of training in the DTVRBT or the conventional balance exercise program. Moreover, three sessions were held per week for a period of 4 weeks. The same physical therapist conducted the training for both groups.

Dual-task virtual reality balance training system

The DTVRBT consists of a laptop and the Kinect sensor (Washington, USA) as shown in Fig. Fig.1.1. This sensor can construct 3D images from the functional integration of two components, an RGB camera and an infrared sensor []. The 3D information from this sensor allows users to interact with the object in the virtual environment. In this study, the virtual environment was created with the Unity3D® version 5.3.2. (San Francisco, USA).

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Fig. 1
The process of interaction in the virtual environment by the Kinect sensor

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Continue —-> The effect of virtual reality-based balance training on motor learning and postural control in healthy adults: a randomized preliminary study

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[WEB SITE] The Newest Technology Impacting Physical Therapy – Virtual Reality

Digital technologies are increasingly becoming more important in the medical sphere every single day. Already, technologies like telemedicine have proven to be an effective method of treatment and has quickly gained in popularity over the years proving that technology can swiftly replace old systems with new, more efficient methods of treatment. Now, a new technology called Virtual Reality is coming into the therapy world and is changing it forever.
 
HOW IS VR IMPACTING PHYSICAL THERAPY?
Virtual Reality (VR) is now being used by physical therapists for the successful treatment of stroke victims, walking disorders and back pain. The technology has been shown to improve the patients motor learning and coordination skills by using gamified, immersive environments that have been created to help patients suffering from pain and injury relearn the use of their limbs in a way that is motivating and fun.
 
WHY IT MATTERS
Lets be honest, most times physical therapy can be a painful and overwhelming experience. With VR, the patient is able to experience a three-dimensional world surrounding their vision that is quite convincing to the sense’s. Many people report that they actually feel present in another environment, separate from reality. This is the element that aids in eliminating pain during exercises and encourages more repeat workouts. For example, if the patient was exercising their lower limbs, they would be able to appear as if they are walking on a beach or in a forest instead of on a boring treadmill. In another scenario, if the patient was exercising their upper extremities, then they could experience the thrill of being rewarded for successfully climbing up a mountain. Games like this provide motivation at home for the patient to continue exercising, enabling them with visual data on how they are improving their range of motion. These type of “experiences” help to keep the patient on the right path outside of the treatment room, which is a huge bonus because studies have shown that only 30% of exercises get accomplished after leaving rehabilitation.
 
 
What this will do to the therapy industry is still unknown but it is obvious that there is enormous potential for this technology to impact it in a large way.

 

via The Newest Technology Impacting Physical Therapy – Virtual Reality

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[Abstract] Robotic and Sensor Technology for Upper Limb Rehabilitation

Abstract

Robotic and sensor-based neurologic rehabilitation for the upper limb is an established concept for motor learning and is recommended in many national guidelines. The complexity of the human hands and arms and the different activities of daily living are leading to an approach in which robotic and sensor-based devices are used in combination to fulfill the multiple requirements of this intervention.

A multidisciplinary team of the Fondazione Don Carlo Gnocchi (FDG), an Italian nonprofit foundation, which spans across the entire Italian territory with 28 rehabilitation centers, developed a strategy for the implementation of robotic rehabilitation within the FDG centers. Using an ad hoc form developed by the team, 4 robotic and sensor-based devices were identified among the robotic therapy devices commercially available to treat the upper limb in a more comprehensive way (from the shoulder to the hand). Encouraging results from a pilot study, which compared this robotic approach with a conventional treatment, led to the deployment of the same set of robotic devices in 8 other FDG centers to start a multicenter randomized controlled trial. Efficiency and economic factors are just as important as clinical outcome.

The comparison showed that robotic group therapy costs less than half per session in Germany than standard individual arm therapy with equivalent outcomes. To ensure access to high-quality therapy to the largest possible patient group and lower health care costs, robot-assisted group training is a likely option.

 

via Robotic and Sensor Technology for Upper Limb Rehabilitation – ScienceDirect

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[TEDx Talks] A critical window for recovery after stroke – John Krakauer – Johns Hopkins University – YouTube

Δημοσιεύτηκε στις 8 Απρ 2015

Dr. John Krakauer, a Professor of Neurology and Neuroscience at Johns Hopkins University, co-founded the KATA project that combines concepts of neurology and neuroscience with interactive entertainment and motion capture technology to learn how lesions affect motor learning and to aid patients in recovering from brain injury.
Dr. John Krakauer is a Professor of Neurology and Neuroscience, the Director of the Center for the Study of Motor Learning and Brain Repair, and the Director of Brain, Learning, Animation, and Movement Lab (BLAM) at Johns Hopkins. He received his undergraduate and master’s degree from Cambridge University and earned his medical degree from Columbia University College of Physicians and Surgeons, where he was elected to Alpha Omega Alpha Medical Honor Society. His clinical and research expertise is in stroke, ischemic cerebrovascular disease, cerebral aneurysms, arteriovenous malformations, and venous and sinus thrombosis.
He co-founded the KATA project that combines concepts of neurology and neuroscience with interactive entertainment and motion capture technology to learn how lesions affect motor learning and to aid patients in recovering from brain injury.
This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx

 

via A critical window for recovery after stroke | John Krakauer | TEDxJohnsHopkinsUniversity – YouTube

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