Posts Tagged motor rehabilitation
We propose to fuse two currently separate research lines on novel therapies for stroke rehabilitation: brain-computer interface (BCI) training and transcranial electrical stimulation (TES). Speciﬁcally, we show that BCI technology can be used to learn personalized decoding models that relate the global conﬁguration of brain rhythms in individual subjects (as measured by EEG) to their motor performance during 3D reaching movements. We demonstrate that our models capture substantial across-subject heterogeneity, and argue that this heterogeneity is a likely cause of limited effect sizes observed in TES for enhancing motor performance. We conclude by discussing how our personalized models can be used to derive optimal TES parameters, e.g., stimulation site and frequency, for individual patients.
Motor deﬁcits are one of the most common outcomes of stroke. According to the World Health Organization, 15 million people worldwide suffer a stroke each year. Of these, ﬁve million are permanently disabled. For this third, upper limb weakness and loss of hand function are among the most devastating types of disabilities, which affect the quality of their daily life . Despite a wide range of rehabilitation therapies, including medication treatment , conventional physiotherapy , and robot physiotherapy , only approximately 20% of patients achieve some form of functional recovery in the ﬁrst six months , .
Current research on novel therapies includes neurofeedback training based on brain-computer interface (BCI) technology and transcranial electrical stimulation (TES). The former approach attempts to support cortical reorganization by providing haptic feedback with a robotic exoskeleton that is congruent to movement attempts, as decoded in real-time from neuroimaging data , . The latter type of research aims to reorganize cortical networks in a way that supports motor performance, because post-stroke alterations of cortical networks have been found to correlate with the severity of motor deﬁcits , . While initial evidence suggested that both approaches, BCIbased training  and TES , have a positive impact, the signiﬁcance of these results over conventional physiotherapy was not always achieved by different studies , , .
One potential explanation for the difﬁculty to replicate the initially promising ﬁndings is the heterogeneity of stroke patients. Different locations of stroke-induced structural changes
are likely to result in substantial across-patient variance in the functional reorganization of cortical networks. As a result, not all patients may beneﬁt from the same neurofeedback or stimulation protocol. We thus propose to fuse these two research themes and use BCI technology to learn personalized models that relate the conﬁguration of cortical networks to each patient’s motor deﬁcits. These personalized models may then be used to predict which TES parameters, e.g., spatial location and frequency band, optimally support rehabilitation in each individual patient.
In this study, we address the ﬁrst step towards personalized TES for stroke rehabilitation. Using a transfer learning framework developed in our group , we show how to create personalized decoding models that relate the EEG of healthy subjects during a 3D reaching task to their motor performance in individual trials. We further demonstrate that the resulting decoding models capture substantial acrosssubject heterogeneity, thereby providing empirical support for the need to personalize models. We conclude by reviewing our ﬁndings in the light of TES studies to improve motor performance in healthy subjects, and discuss how personalized TES parameters may be derived from our models.[…]
Objective: Apply BCI technology to improve stroke rehabilitation therapy
Background: Brain-computer interfaces (BCI) measure brain activity to generate control signals for external devices in real-time. BCIs are especially well suited for motor rehabilitation. Motor imagery BCIs can analyze patients’ sensorimotor regions and control conditionally gated feedback devices that allow the patient to regain motor functions.
Design/Methods: Patients with sub-acute stroke were trained for 25 30-minute sessions in which they imagined left or right hand movement. A computer avatar indicated which hand the patient should imagine moving (80 trials left hand; 80 trials right). The BCI system analyzed EEG in real time, deciphered intention for left or right hand movement, and triggered functional electrical stimulation that elicited movement in the corresponding hand and in the computer avatar only when the patient produced the correct corresponding EEG pattern. Motor function improvements were assessed with a 9-hole PEG test.
Results: In a chronic stroke patient the 9-hole PEG test showed an improvement in affected left hand movement from 1 min 30 seconds to 52 sec after 24 training sessions (healthy right hand: 26 sec). BCI accuracy increased from 70% to 98.5 % across sessions. Mean accuracy for the first 3 sessions was 81%; 88% for the last 3. Before training, the patient could not lift his affected arm. After training the patient could reach his mouth to feed himself.
Conclusions: BCI accuracy is an objective marker of a patient’s participation in the task; 50% means that patient doesn’t follow (or cannot follow) the task. This patient’s continued improvement and high final accuracy indicates motivated participation. Most importantly, there was objective improvement in motor function within only 25 training sessions. We attribute these results to the conditionally gated reward from the BCI (inducing Hebbian plasticity), and mirror neuron system activation by the avatar.
Disclosure: Dr. Guger has received personal compensation for activities with g.tec Medical Engineering GmbH as an employee. Dr. Coon has nothing to disclose. Dr. Swift has nothing to disclose.
[ARTICLE] Movement visualisation in virtual reality rehabilitation of the lower limb: a systematic review – Full Text
Virtual reality (VR) based applications play an increasing role in motor rehabilitation. They provide an interactive and individualized environment in addition to increased motivation during motor tasks as well as facilitating motor learning through multimodal sensory information. Several previous studies have shown positive effect of VR-based treatments for lower extremity motor rehabilitation in neurological conditions, but the characteristics of these VR applications have not been systematically investigated. The visual information on the user’s movement in the virtual environment, also called movement visualisation (MV), is a key element of VR-based rehabilitation interventions. The present review proposes categorization of Movement Visualisations of VR-based rehabilitation therapy for neurological conditions and also summarises current research in lower limb application.
A systematic search of literature on VR-based intervention for gait and balance rehabilitation in neurological conditions was performed in the databases namely; MEDLINE (Ovid), AMED, EMBASE, CINAHL, and PsycInfo. Studies using non-virtual environments or applications to improve cognitive function, activities of daily living, or psychotherapy were excluded. The VR interventions of the included studies were analysed on their MV.
In total 43 publications were selected based on the inclusion criteria. Seven distinct MV groups could be differentiated: indirect MV (N = 13), abstract MV (N = 11), augmented reality MV (N = 9), avatar MV (N = 5), tracking MV (N = 4), combined MV (N = 1), and no MV (N = 2). In two included articles the visualisation conditions included different MV groups within the same study. Additionally, differences in motor performance could not be analysed because of the differences in the study design. Three studies investigated different visualisations within the same MV group and hence limited information can be extracted from one study.
The review demonstrates that individuals’ movements during VR-based motor training can be displayed in different ways. Future studies are necessary to fundamentally explore the nature of this VR information and its effect on motor outcome.
Virtual reality (VR) in neurorehabilitation has emerged as a fairly recent approach that shows great promise to enhance the integration of virtual limbs in one`s body scheme  and motor learning in general . Virtual Rehabilitation is a “group [of] all forms of clinical intervention (physical, occupational, cognitive, or psychological) that are based on, or augmented by, the use of Virtual Reality, augmented reality and computing technology. The term applies equally to interventions done locally, or at a distance (tele-rehabilitation)” . The main objectives of intervention for facilitating motor learning within this definition are to (1) provide repetitive and customized high intensity training, (2) relay back information on patients’ performance via multimodal feedback, and (3) improve motivation [2, 4]. VR therapies or interventions are based on real-time motion tracking and computer graphic technologies displaying the patients’ behaviour during a task in a virtual environment.
The interaction of the user and Virtual environment can be described as a perception and action loop . This motor performance is displayed in the virtual environment and subsequently, the system provides multimodal feedback related to movement execution. Through external (e.g. vision) and internal (proprioception) senses the on-line sensory feedback is integrated into the patient’s mental representation. If necessary, the motor plan is corrected in order to achieve the given goal .
A previous Cochrane Review from Laver, George, Thomas, Deutsch, and Crotty  on Virtual Reality for stroke rehabilitation showed positive effects of VR intervention for motor rehabilitation in people post-stroke. However, grouped analysis from this review on recommendation for VR intervention provides inconclusive evidence. The author further comments that “[…] virtual reality interventions may vary greatly […], it is unclear what characteristics of the intervention are most important” (, p. 14).
Virtual rehabilitation system provides three different types of information to the patient: movement visualisation, performance feedback and context information . During a motor task the patient’s movements are captured and represented in the virtual environment (movement visualisation). According to the task success, information about the accomplished goal or a required movement alteration is transmitted through one or several sensory modalities (performance feedback). Finally, these two VR features are embedded in a virtual world (context information) that can vary from a very realistic to an abstract, unrealistic or reduced, technical environment.
Performance feedback often relies on theories of motor learning and is probably the most studied information type within VR-based motor rehabilitation. Moreover, context information is primarily not designed with a therapeutic purpose. Movement observation, however, plays an important role for central sensory stimulation therapies, such as mirror therapy or mental training. The observation or imagination of body movements facilitates motor recovery [7, 8, 9] and provides new possibilities for cortical reorganization and enhancement of functional mobility. Thus, it appears that movement visualisation may also play an important role in motor rehabilitation [10, 11, 12], although this aspect is yet to be systematically investigated .
The main goal of the present review is to identify various movement visualisation groups in VR-based motor interventions for lower extremities, by means of a systematic literature search. Secondarily, the included studies are further analysed for their effect on motor learning. This will help guide future research in rehabilitation using VR.
An interim analysis of the review published in 2013 showed six MV groups for upper and lower extremity training and additional two MV groups directed only towards lower extremity training. In this paper, we analysed only studies involving lower limb training, leading to a revision and expansion of the previously published MV groups findings [13, 14, 15].
[Abstract+References] Architectural Design of a Cloud Robotic System for Upper-Limb Rehabilitation with Multimodal Interaction
The rise in the cases of motor impairing illnesses demands the research for improvements in rehabilitation therapy. Due to the current situation that the service of the professional therapists cannot meet the need of the motor-impaired subjects, a cloud robotic system is proposed to provide an Internet-based process for upper-limb rehabilitation with multimodal interaction.
In this system, therapists and subjects are connected through the Internet using client/server architecture. At the client site, gradual virtual games are introduced so that the subjects can control and interact with virtual objects through the interaction devices such as robot arms. Computer graphics show the geometric results and interaction haptic/force is fed back during exercising. Both video/audio information and kinematical/physiological data are transferred to the therapist for monitoring and analysis.
In this way, patients can be diagnosed and directed and therapists can manage therapy sessions remotely. The rehabilitation process can be monitored through the Internet. Expert libraries on the central server can serve as a supervisor and give advice based on the training data and the physiological data. The proposed solution is a convenient application that has several features taking advantage of the extensive technological utilization in the area of physical rehabilitation and multimodal interaction.
[Thesis] Ubiquitous and Wearable Computing Solutions for Enhancing Motor Rehabilitation of the Upper Extremity Post-Stroke
Coffey, Aodhan L. (2016) Ubiquitous and Wearable Computing Solutions for Enhancing Motor Rehabilitation of the Upper Extremity Post-Stroke. PhD thesis, National University of Ireland Maynooth.
A stroke is the loss of brain function caused by a sudden interruption in the blood supply of the brain. The extent of damage caused by a stroke is dependent on many factors such as the type of stroke, its location in the brain, the extent of oxygen deprivation and the criticality of the neural systems affected. While stroke is a non-cumulative disease, it is nevertheless a deadly pervasive disease and one of the leading causes of death and disability worldwide. Those fortunate enough to survive stroke are often left with some form of serious long-term disability. Weakness or paralysis on one side of the body, or in an individual limb is common after stroke. This affects independence and can greatly limit quality of life.
Stroke rehabilitation represents the collective effort to heal the body following stroke and to return the survivor to as normal a life as possible. It is well established that rehabilitation therapy comprising task-specific, repetitive, prolonged movement training with learning is an effective method of provoking the necessary neuroplastic changes required which ultimately lead to the recovery of function after stroke. However, traditional means of delivering such treatments are labour intensive and constitute a significant burden for the therapist limiting their ability to treat multiple patients. This makes rehabilitation medicine a costly endeavour that may benefit from technological contributions. As such, stroke has severe social and economic implications, problems exasperated by its age related dependencies and the rapid ageing of our world. Consequently these factors are leading to a rise in the number living with stroke related complications. This is increasing the demand for post stroke rehabilitation services and places an overwhelming amount of additional stress on our already stretched healthcare systems.
Therefore, new innovative solutions are urgently required to support the efforts of healthcare professionals in an attempt to alleviate this stress and to ultimately improve the quality of care for stroke survivors. Recent innovations in computer and communication technology have lead to a torrent of research into ubiquitous, pervasive and distributed technologies, which might be put to great use for rehabilitative purpose. Such technology has great potential utility to support the rehabilitation process through the delivery of complementary, relatively autonomous rehabilitation therapy, potentially in the comfort of the patient’s own home.
This thesis describes concerted work to improve the current state and future prospects of stroke rehabilitation, through investigations which explore the utility of wearable, ambient and ubiquitous computing solutions for the development of potentially transformative healthcare technology. Towards this goal, multiple different avenues of the rehabilitation process are explored, tackling the full chain of processes involved in motor recovery, from brain to extremities. Subsequently, a number of cost effective prototype devices for use in supporting the ongoing rehabilitation process were developed and tested with healthy subjects, a number of open problems were identified and highlighted, and tentative solutions for home-based rehabilitation were put forward. It is envisaged that the use of such technology will play a critical role in abating the current healthcare crisis and it is hoped that the ideas presented in this thesis will aid in the progression and development of cost effective, efficacious rehabilitation services, accessible and affordable to all in need.
[Stroke Rehabilitation Clinician Handbook] 4. Motor Rehabilitation – 4A. Lower Extremity and Mobility – Full Text PDF
4.1 Motor Recovery of the Lower Extremity Post Stroke
Factors that Predict Motor Recovery
Motor deficits post-stroke are the most obvious impairment (Langhorne et al. 2012) and have a disabling impact on valued activities and independence. Motor deficits are defined as “a loss or limitation of function in muscle control or movement or a limitation of movement” (Langhorne et al. 2012; Wade 1992). Given its importance, a large proportion of stroke rehabilitation efforts are directed towards the recovery of movement disorders. Langhorne et al. (2012) notes that motor recovery after stroke is complex with many treatments designed to promote recovery of motor impairment and function.
The two most important factors which predict motor recovery are:
- Stroke Severity: The most important predictive factor which reduces the capacity for brain reorganization.
- Age: Younger patients demonstrate greater neurological and functional recovery and hence have a better prognosis compared to older stroke patients (Adunsky et al. 1992; Hindfelt & Nilsson 1977; Marini et al. 2001; Nedeltchev et al. 2005).
Changes in walking ability and gait pattern often persist long-term and include increased tone, gait asymmetry, changes in muscle activation and reduced functional abilities (Wooley 2001; Robbins et al. 2006; Pizzi et al. 2007, Pereira et al. 2012). Ambulation post stroke is often less efficient and associated with increased energy expenditure (Pereira et al. 2012). Hemiplegic individuals have been reported to utilize 50-67% more metabolic energy that normal individuals when walking at the same velocity (Wooley et al. 2001).
For mobility outcome, trunk balance is an additional predictor of recovery (Veerbeek et al. 2011). Nonambulant patients who regained sitting balance and some voluntary movement of the hip, knee and/or ankle within the first 72 hours post stroke predicted 98% chance of regaining independent gait within 6 months. In contrast, those who were unable to sit independently for 30 seconds and could not contract the paretic lower limb within the first 72 hours post stroke had a 27% probability of achieving independent gait.
This single volume brings together both theoretical developments in the field of motor control and their translation into such fields as movement disorders, motor rehabilitation, robotics, prosthetics, brain-machine interface, and skill learning. Motor control has established itself as an area of scientific research characterized by a multi-disciplinary approach. Its goal is to promote cooperation and mutual understanding among researchers addressing different aspects of the complex phenomenon of motor coordination. Topics covered include recent theoretical advances from various fields, the neurophysiology of complex natural movements, the equilibrium-point hypothesis, motor learning of skilled behaviors, the effects of age, brain injury, or systemic disorders such as Parkinson’s Disease, and brain-computer interfaces.
In this issue of Cell Reports, Ossmy and Mukamel (2016) show that virtual reality enhances learning of new motor sequences through practice with one hand and synchronous feedback of the other hand moving. The approach holds promise for motor rehabilitation.
Everyday tasks require sequences of movements with one or both hands. For example, many times a day, you may use one hand to unzip your pocket, find your keys, and unlock your car. We take such practiced sequences for granted because they are so well practiced as to seem effortless. However, imagine that you have had a stroke and struggle to control one of your hands. In such cases, learning or relearning even simple motor sequences would be frustratingly difficult. You might be able to take advantage of the fact that motor learning shows some transfer between hands—learn the task with the good right hand and your problematic left hand may also benefit—but such effects are modest. But what if there were a way to boost your motor sequence learning with the help of virtual reality?
In this issue of Cell Reports, Ori Ossmy and Roy Mukamel at Tel-Aviv University show that virtual reality can help people learn movement sequences rapidly ( Ossmy and Mukamel, 2016). Healthy participants performed an arbitrary sequence of digit movements as rapidly as possible for 30 seconds with one hand and then with the other. Then they spent five minutes practicing the sequence with real movements of the right hand in a virtual reality setup. Finally, they repeated the test sequence with each hand (Figure 1).
Computer systems such as virtual environments and serious games are being used as a tool to enhance the process of user rehabilitation. These systems can help motivate and provide means to assess the user’s performance undertaking an exercise session. To do that, these systems incorporate motion tracking and gesture recognition devices, such as natural interaction devices like Kinect and Nintendo Wii. These devices, originally developed for the games market, allowed the development of low cost and minimally invasive rehabilitation systems, allowing the treatment to be taken to the patient’s residence. With the advent of natural interaction based on electromyography, devices that use electromyographic signals can also be used to construct these systems. The aim of this work is to show how electromyographic signals could be used as a tool to capture user gestures and incorporated into home-based rehabilitation systems by adopting a low-cost device to capture these gestures. The process of creation of a serious game to show some of these concepts is also present.