Posts Tagged motor rehabilitation

[ARTICLE] Increasing upper limb training intensity in chronic stroke using embodied virtual reality: a pilot study – Full Text

Abstract

Background

Technology-mediated neurorehabilitation is suggested to enhance training intensity and therefore functional gains. Here, we used a novel virtual reality (VR) system for task-specific upper extremity training after stroke. The system offers interactive exercises integrating motor priming techniques and embodied visuomotor feedback. In this pilot study, we examined (i) rehabilitation dose and training intensity, (ii) functional improvements, and (iii) safety and tolerance when exposed to intensive VR rehabilitation.

Methods

Ten outpatient stroke survivors with chronic (>6 months) upper extremity paresis participated in a ten-session VR-based upper limb rehabilitation program (2 sessions/week).

Results

All participants completed all sessions of the treatment. In total, they received a median of 403 min of upper limb therapy, with 290 min of effective training. Within that time, participants performed a median of 4713 goal-directed movements. Importantly, training intensity increased progressively across sessions from 13.2 to 17.3 movements per minute. Clinical measures show that despite being in the chronic phase, where recovery potential is thought to be limited, participants showed a median improvement rate of 5.3% in motor function (Fugl-Meyer Assessment for Upper Extremity; FMA-UE) post intervention compared to baseline, and of 15.4% at one-month follow-up. For three of them, this improvement was clinically significant. A significant improvement in shoulder active range of motion (AROM) was also observed at follow-up. Participants reported very low levels of pain, stress and fatigue following each session of training, indicating that the intensive VR intervention was well tolerated. No severe adverse events were reported. All participants expressed their interest in continuing the intervention at the hospital or even at home, suggesting high levels of adherence and motivation for the provided intervention.

Conclusions

This pilot study showed how a dedicated VR system could deliver high rehabilitation doses and, importantly, intensive training in chronic stroke survivors. FMA-UE and AROM results suggest that task-specific VR training may be beneficial for further functional recovery both in the chronic stage of stroke. Longitudinal studies with higher doses and sample sizes are required to confirm the therapy effectiveness.

Background

Stroke affects about 17 million people per year worldwide, with an increasing rate every year [1]. Stroke survivors often suffer from physical and mental disabilities, heavily impacting their quality of life. Five years after the first stroke, nearly 66% of patients exhibit different degrees of disability and only 34% are functionally independent in their activities of daily living [2].

Motor rehabilitation after stroke

Motor dysfunction is the most prevalent impairment, with 9 out of 10 stroke survivors suffering from some form of upper limb motor disability [3], and it is a strong predictor of poor functional recovery [4]. Thus, there is a strong need for rehabilitative approaches enhancing motor recovery for stroke patients [5]. To maximize neural, motor and functional recovery, training needs to be long-lasting, challenging, repetitive, task-specific, motivating, salient, and intensive [6]. Standard motor rehabilitation after stroke typically includes neurofacilitation techniques, task-specific training and task-oriented training [7]. Further approaches include strength training, trunk restraint, somatosensory training, constraint-induced movement therapy, bilateral arm training, coordination of reach to grasp, mirror training, action observation and neuromuscular electrical stimulation [8].

Time scheduled for therapy and its frequency are determinant factors for the outcome of motor rehabilitation [9], with a recommended initial amount of at least 45 min for a minimum of 5 days per week [10]. However, the frequency of the delivered therapy usually decreases with time, with therapy being discontinued between 3 and 6 months after the vascular accident [7]. Under these rehabilitation conditions, recovery of motor function has been observed to be strongest during the first month after stroke and to slow down during subsequent months, reaching a “plateau” by 3–6 months post stroke [1112]. Clinical evidence for motor improvement in chronic stroke [13] suggests that the “plateau” may depend not only on neurobiological factors, but may also be caused by other factors such as reduction in rehabilitation services [14].

Thus, increasing therapy dose, also in the chronic phase of the disease, might be a critical factor to achieve a positive outcome. Although several guidelines for upper limb rehabilitation have been recently issued [510], the relationship between training intensity and recovery patterns is not yet fully established. Indeed, it is not fully clear how to quantify the dose increase leading to a positive outcome. Training volume, understood as the number of repetitions, seems to be a more relevant parameter of dose than just the total time allocated for therapy [9]. An important issue is how to quantify and capture this concept in a measurable parameter. Intensity of training, understood as the number of repetitions divided by the number of minutes of active therapy, might be a fundamental factor (together with amount and frequency of therapy) to quantify training efficiency. This knowledge becomes critical in order to evaluate cost-effectiveness of new technology-mediated interventions and to select the most valuable therapy procedures at the different stages of the continuum of care for stroke survivors.

Virtual reality for motor rehabilitation

Different complementary solutions have been proposed during the last decades to help increase and maintain the rehabilitation dose in the long term, mainly through continued therapy. Virtual reality (VR) based motor rehabilitation is a relatively recent approach, showing evidence of moderate effectiveness in improving upper limb and ADL function when compared to conventional therapy [15].

Many VR setups, and often generic (i.e. not developed for rehabilitation purposes) commercial off-the-shelf computer games, are used to perform a series of exercises, where patients move in front of a console and receive mostly visual feedback about their movements [161718]. This represents a limited approach, whereby the level of immersion and potential feedback is restricted to a single sensorimotor action-perception loop: the patient moves and receives only abstract visual feedback from the screen. A rather different approach implies embodied sensorimotor feedback, where movements of the patient in the real world are reproduced as movements of an anthropomorphic avatar in the virtual environment. Under such conditions, VR allows for more elaborated sensorimotor activation, which may impact the recovery process. In particular, through sensorimotor resonance mechanisms, embodied sensorimotor feedback allows the integration of motor priming techniques and cognitive principles related to body perception and action, including mirror therapy [19] and action observation [2021], which have been shown to improve functional recovery and increase cortical activation of the ipsilesional side after stroke. This embodied technology can be achieved by using motion capture technology that interprets the patient’s movements and provides multisensory (vision, audio, touch) feedback to the user about the movement performance. Such enriched VR experiences have been demonstrated to increase patients’ motivation [22] and facilitate functional recovery by engaging appropriate neural circuits in the motor system [23].

One of the VR advantages is that it enables simulated practice of functional tasks at a higher dosage than traditional therapies [15]. Lohse and colleagues recently reviewed the duration, time and frequency scheduled for different VR and computer games interventions, but training intensity (as defined above) was no reported [24]. In general, authors reported an overall median of 570 min of VR (or computer games) therapy delivered, with duration ranging from 20 to 60 min per session, and 8 to 36 sessions [24]. Otherwise, intensity of training is rarely reported for VR training (see [25] for an exception). However, this is a critical factor to estimate cost-effectiveness of VR-based interventions.

Objectives of the study

The present study aims at investigating the feasibility of admninistering intensive training in chronic stroke patients using a dedicated VR-based system that embeds real-time 3D motion capture and embodied visual feedback to deliver functional exercises designed to train impaired motor skills of the upper limb. Our primary goal was to assess (i) rehabilitation dose and training intensity in chronic patients. Additionally, we asked (ii) whether chronic stroke survivors improve functional outcomes of the upper limb when exposed to intensive VR-based therapy, and we measured (iii) safety and tolerance to such a technology-mediated intervention. We hypothesize that intensive VR-based rehabilitation may lead to high rehabilitation doses and functional improvement in chronic stroke survivors.[…]

Fig. 1a Participant performing an upper limb exercise (Grasping) with the MindMotion ™ PRO technology; b Participant doing the Reaching exercise; c Participant doing a Fruitchamp exercise

 

Continue —>  Increasing upper limb training intensity in chronic stroke using embodied virtual reality: a pilot study | Journal of NeuroEngineering and Rehabilitation | Full Text

Advertisements

, , , , , , , , , ,

Leave a comment

[Conference paper] ePHoRt Project: A Web-Based Platform for Home Motor Rehabilitation – Abstract

Abstract

ePHoRt is a project that aims to develop a web-based system for the remote monitoring of rehabilitation exercises in patients after hip replacement surgery. The tool intends to facilitate and enhance the motor recovery, due to the fact that the patients will be able to perform the therapeutic movements at home and at any time. As in any case of rehabilitation program, the time required to recover is significantly diminished when the individual has the opportunity to practice the exercises regularly and frequently. However, the condition of such patients prohibits transportations to and from medical centers and many of them cannot afford a private physiotherapist. Thus, low-cost technologies will be used to develop the platform, with the aim to democratize its access. By taking into account such a limitation, a relevant option to record the patient’s movements is the Kinect motion capture device. The paper describes an experiment that evaluates the validity and accuracy of this visual capture by a comparison to an accelerometer sensor. The results show a significant correlation between both systems and demonstrate that the Kinect is an appropriate tool for the therapeutic purpose of the project.

References

  1. 1.
    Feys, H., De Weerdt, W., Verbeke, G., et al.: Early and repetitive stimulation of the arm can substantially improve the long-term outcome after stroke: a 5-year follow-up study of a randomized trial. Stroke 35(4), 924–929 (2004)CrossRefGoogle Scholar
  2. 2.
    Cramp, M.C., Greenwood, R.J., Gill, M., et al.: Effectiveness of a community-based low intensity exercise programme for ambulatory stroke survivors. Disabil. Rehabil. 32(3), 239–247 (2010)CrossRefGoogle Scholar
  3. 3.
    Mavroidis, C., Nikitczuk, J., Weinberg, B., et al.: Smart portable rehabilitation devices. J. Neuroeng. Rehabil. 2, 18 (2005). doi:10.1186/1743-0003-2-18CrossRefGoogle Scholar
  4. 4.
    Holden, M.K., Dyar, T.A., Dayan-Cimadoro, L.: Telerehabilitation using a virtual environment improves upper extremity function in patients with stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 15(1), 36–42 (2007)CrossRefGoogle Scholar
  5. 5.
    Rand, D., Kizony, R., Weiss, P.T.L.: The Sony PlayStation II EyeToy: low-cost virtual reality for use in rehabilitation. J. Neurol. Phys. Ther. 32(4), 155–163 (2008)CrossRefGoogle Scholar
  6. 6.
    Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Efficient model-based 3D tracking of hand articulations using Kinect. In: Proceedings of the 22nd British Machine Vision Conference. University of Dundee (2011)Google Scholar
  7. 7.
    Rybarczyk, Y., Rybarczyk, P., Oliveira, N., Vernay, D.: e-ESPOIR: a user-friendly web-based tool for disability evaluation. In: Proceedings of the 11th conference of the Association for the Advancement of Assistive Technology in Europe. Maastricht (2011)Google Scholar
  8. 8.
    Mendes, P., Rybarczyk, Y., Rybarczyk, P., Vernay, D.: A web-based platform for the therapeutic education of patients with physical disabilities. In: Proceedings of the 6th International Conference of Education, Research and Innovation, Seville (2013)Google Scholar
  9. 9.
    Rodrigues, F., Rybarczyk, Y., Gonçalves, M.J.: On the use of IT for treating aphasic patients: a 3D web-based solution. In: Proceedings of the 13th International Conference on Applications of Computer Engineering, Lisbon (2014)Google Scholar
  10. 10.
    Rybarczyk, Y., Fonseca, J.: Tangible interface for a rehabilitation of comprehension in aphasic patients. In: Proceedings of the 11th conference of the Association for the Advancement of Assistive Technology in Europe, Maastricht (2011)Google Scholar
  11. 11.
    Birns, J., Bhalla, A., Rudd, A.: Telestroke: a concept in practice. Age Ageing 39(6), 666–667 (2010)CrossRefGoogle Scholar
  12. 12.
    Nguyen, K.D., Chen, I.M., Luo, Z., et al.: A wearable sensing system for tracking and monitoring of functional arm movement. IEEE/ASME Trans. Mechatron. 16(2), 213–220 (2011)CrossRefGoogle Scholar
  13. 13.
    Patel, S., Park, H., Bonato, P., et al.: A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9(1), 21–37 (2012)CrossRefGoogle Scholar
  14. 14.
    Rand, D., Eng, J.J., Tang, P.F., et al.: How active are people with stroke? use of accelerometers to assess physical activity. Stroke 40(1), 163–168 (2009)CrossRefGoogle Scholar
  15. 15.
    Biswas, D., Cranny, A., Maharatna, K.: Body area sensing networks for remote health monitoring. In: Vogiatzaki, E., Krukowski, A. (eds.) Modern Stroke Rehabilitation through e-Health-Based Entertainment, pp. 85–136. Springer, Heidelberg (2016)CrossRefGoogle Scholar
  16. 16.
    Jovanov, E., Milenkovic, A., Otto, C., De Groen, P.C.: A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. J. Neuroeng. Rehabil. 2, 6–15 (2005)CrossRefGoogle Scholar
  17. 17.
    Strath, S.J., Kaminsky, L.A., Ainsworth, B.E., et al.: Guide to the assessment of physical activity: clinical and research applications – a scientific statement from the American heart association. Circulation 128(20), 2259–2279 (2013)CrossRefGoogle Scholar
  18. 18.
    Vernay, D., Edan, G., Moreau, T., Visy, J.M., Gury, C.: OSE: a single tool for evaluation and follow-up patients with relapsing-remitting multiple sclerosis. Multiple Sclerosis 12, suppl. 1 (2006)Google Scholar
  19. 19.
    Nilsdotter, A., Bremander, A.: Measures of hip function and symptoms. Arthritis Care Res. 63, 200–207 (2011)CrossRefGoogle Scholar
  20. 20.
    Borg, G.A.: Psychophysical bases of perceived exertion. Med. Sci. Sports Exerc. 14(5), 377–381 (1982)CrossRefGoogle Scholar
  21. 21.
    Gameiro, J., Cardoso, T., Rybarczyk, Y.: Kinect-Sign: teaching sign language to listeners through a game. In: Rybarczyk, Y., et al. (eds.) Innovative and Creative Developments in Multimodal Interaction Systems, pp. 141–159. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  22. 22.
    Rybarczyk, Y., Santos, J.: Motion integration in direction perception of biological motion. In: Proceedings of the 4th Asian Conference on Vision, Matsue (2006)Google Scholar
  23. 23.
    Dutta, T.: Evaluation of the kinect sensor for 3-D kinematic measurement in the workplace. Appl. Ergonomics 43, 645–649 (2012)CrossRefGoogle Scholar
  24. 24.
    Brook, G., Barry, G., Jackson, D., Mhiripiri, D., Olivier, P., Rochester, L.: Accuracy of the microsoft kinect sensor for measuring movement in people with Parkinson’s disease. Gait Posture 39(4), 1062–1068 (2014)CrossRefGoogle Scholar
  25. 25.
    Rybarczyk, Y.: 3D markerless motion capture: a low cost approach. In: Proceedings of the 4th World Conference on Information Systems and Technologies, Recife (2016)Google Scholar
  26. 26.
    Remondino, F., Roditakis, A.: 3D reconstruction of human skeleton from single images or monocular video sequences. In: Proceedings of Joint Pattern Recognition Symposium, Magdeburg (2003)Google Scholar
  27. 27.
    Krukowski, A., Vogiatzaki, E., Rodríguez, J.M.: Patient health record (PHR) system. In: Maharatna, K., et al. (eds.) Next Generation Remote Healthcare: A Practical System Design Perspective. Springer, New York (2013). Chap. 6Google Scholar

via ePHoRt Project: A Web-Based Platform for Home Motor Rehabilitation | SpringerLink

, , , , , ,

Leave a comment

[ARTICLE] A motion intention-based upper limb rehabilitation training system to stimulate motor nerve through virtual reality – Full Text

Motor rehabilitation strategies for treating motor deficits after stroke are based on the understanding of the neural plasticity. In recent years, various upper limb rehabilitation robots have been proposed for the stroke survivors to provide relearning of motor skills by stimulating the motor nerve. However, several aspects including costing, human–robot interaction, and effective stimulation of motor nerve still remain as major issues. In this article, a new upper limb rehabilitation training system named as motion intention-based virtual reality training system is developed to close the aforementioned issues. The system identifies the user’s motion intention via force sensors mounted on the rehabilitation robot to conduct therapeutic exercises and stimulates the user’s motor nerve by introducing the illusion of immersion in virtual reality environment. The illusion of immersion is developed by creating Virtual Exoskeleton Robot model which is driven by user’s motion intention and reflecting the motion states in real time. The users can be present to the training exercises by themselves and fully engage in the virtual reality environment, so that they can relax, move, and recreate motor neuro-pathways. As preliminary phase, six healthy subjects were invited to participate in experiments. The experimental results showed that the motion intention-based virtual reality training system is effective for the upper limb rehabilitation exoskeleton and the evaluations of the developed system showed a significant reduction of the performance error in the training task.

Stroke is a major cause of acquired physical disability in adults worldwide. Motor deficits affecting the upper limb are a common manifestation of stroke and greatly contribute to decreasing the individual’s functional performance.1 It is widely appreciated that motor rehabilitation after stroke plays an essential role in reducing the individual’s physical disability.2 The rehabilitation strategies for treating motor deficits after stroke are based on the understanding of the neural plasticity which is known by the phenomenon that the human brain changes itself in response to different types of experience through the reorganization of its neuronal connections.3 To exhibit the neural plasticity, motor relearning is the most important matter because it can produce changes in synapses, neurons, and neuronal networks within specific brain regions.4 Exoskeletons are robotic systems designed to work linked with parts (or the whole) of the human body. The robotic exoskeleton structure is always maintaining contact with the human operator’s limb. It can be suitably employed in robotic-assisted rehabilitation to assist the users to proceed relearning movement training exercises. And it can also make the process of upper limb rehabilitation repeatable, with objective estimation and decrease the dependence on specialized personnel availability.

About 30 existing robotic exoskeleton devices are reviewed by Proietti et al.5 As it has been mentioned, most publications in the field of exoskeletons focused only on mechatronic design of the devices, while we do believe a paramount aspect for robots potentiality lays on the control side. So the development of innovative and improved human–robot interaction control strategies will make a certain contribution to the upper limb rehabilitation assisted by the robotic exoskeleton devices.

The virtual reality (VR) technology has been proved useful in terms of motivating and challenging patients for longer training duration and cadence, modifying patient’s participating level, and updating subjects with their training performance.6 VR-based rehabilitation protocols may significantly improve the quality of rehabilitation by offering strong functional motivations to the patient who can therefore be more attentive to the movement to be performed. VR can provide an even more stimulating video game-like rehabilitation environment when integrated with force feedback devices, thus enhancing the quality of the rehabilitation.7

An upper limb force feedback exoskeleton for robotic assisted rehabilitation in VR is presented in Frisoli et al.8 A specific VR application focused on the reaching task was developed and evaluated in the system, but the system can’t provide adjustment when the reaching is far away too much. And little details are given to the control aspects of the robotic exoskeleton. An assistive control system with a special kinematic structure of an upper limb rehabilitation robot embedded with force/torque sensors is presented by Chen et al.9 A three-dimensional (3-D) GUI system for upper limb rehabilitation using electromyography and inertia measurement unit sensor feedback is developed by Alhajjar et al.10 It encourages the patients by recording the results and providing 3-D VR arm to simulate the arm movement during the exercise. A haptic device and an inertial sensor are used to implement rehabilitation tasks proposed by Song et al.,11 the system provides the vision through the monitor and force feedback through the haptic device. Gesture therapy was presented by Sucar et al.,12 a VR-based platform for rehabilitation of the upper limb was introduced. Similarly, the patients’ use of a home-based VR system portrayed by Standen et al.13 provides a low-cost VR system that translates movements of the hand, fingers, and thumb into game play which was designed to provide a flexible and motivating approach to increasing adherence to home-based rehabilitation. It is suitable for the patients with slight independence ability, which doesn’t have to be assisted by the robotic exoskeleton.

By considering all the aforementioned limitations, motion intention-based virtual reality training system (MIVRTS) is developed by integrating motion intention identification-based upper limb therapeutic exercises and the illusion of immersion in VR. The system identifies the user’s motion intention via force sensors mounted on the rehabilitation robot to conduct therapeutic exercises and stimulates the user’s motor nerve by introducing the illusion of immersion in VR environment. The illusion of immersion is developed by creating Virtual Exoskeleton Robot model which is driven by user’s motion intention and reflecting the motion states in real time.

The rest of the article is organized as follows. “The rehabilitation robotic exoskeleton” section presents the main features of the rehabilitation robotic exoskeleton system. An overview of the developed MIVRTS system employed in this study for the validation of the exoskeleton in upper limb rehabilitation is given in “MIVRTS system” section. In “Motion intention-based application” section, the motion intention identifying method is described and an application for rehabilitation exercises is developed. “Evaluation on six participants” section explains the experiment and evaluation results, followed by conclusion described in the final section.[…]

Figure

Figure 1. 5-DOF upper limb rehabilitative exoskeleton robot. DOF: degrees of freedom.

Continue —-> A motion intention-based upper limb rehabilitation training system to stimulate motor nerve through virtual realityInternational Journal of Advanced Robotic Systems – Li Xing, Xiaofeng Wang, Jianhui Wang, 2017

, , , , , , , , ,

Leave a comment

[BOOK] Virtual Reality for Physical and Motor Rehabilitation – Google Books

Virtual Reality for Physical and Motor Rehabilitation

Front Cover

Patrice L. Tamar WeissEmily A. KeshnerMindy F. Levin
SpringerJul 24, 2014 – Medical – 232 pages

While virtual reality (VR) has influenced fields as varied as gaming, archaeology, and the visual arts, some of its most promising applications come from the health sector. Particularly encouraging are the many uses of VR in supporting the recovery of motor skills following accident or illness.

Virtual Reality for Physical and Motor Rehabilitation reviews two decades of progress and anticipates advances to come. It offers current research on the capacity of VR to evaluate, address, and reduce motor skill limitations, and the use of VR to support motor and sensorimotor function, from the most basic to the most sophisticated skill levels. Expert scientists and clinicians explain how the brain organizes motor behavior, relate therapeutic objectives to client goals, and differentiate among VR platforms in engaging the production of movement and balance. On the practical side, contributors demonstrate that VR complements existing therapies across various conditions such as neurodegenerative diseases, traumatic brain injury, and stroke. Included among the topics:

  • Neuroplasticity and virtual reality.
  • Vision and perception in virtual reality.
  • Sensorimotor recalibration in virtual environments.
  • Rehabilitative applications using VR for residual impairments following stroke.
  • VR reveals mechanisms of balance and locomotor impairments.
  • Applications of VR technologies for childhood disabilities.

A resource of great immediate and future utility, Virtual Reality for Physical and Motor Rehabilitation distills a dynamic field to aid the work of neuropsychologists, rehabilitation specialists (including physical, speech, vocational, and occupational therapists), and neurologists.

Preview this book »

Source: Virtual Reality for Physical and Motor Rehabilitation – Google Books

, , , , ,

Leave a comment

[ARTICLE] Personalized Brain-Computer Interface Models for Motor Rehabilitation – Full Text PDF

Abstract

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.

I. INTRODUCTION
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 [1]. Despite a wide range of rehabilitation therapies, including medication treatment [2], conventional physiotherapy [3], and robot physiotherapy [4], only approximately 20% of patients achieve some form of functional recovery in the first six months [5], [6].

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 [7], [8]. 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 [9], [10]. While initial evidence suggested that both approaches, BCIbased training [11] and TES [12], have a positive impact, the significance of these results over conventional physiotherapy was not always achieved by different studies [13], [14], [15].

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 [16], 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.[…]

Full Text PDF

, , , ,

Leave a comment

[Abstract] Play seriously: Effectiveness of serious games and their features in motor rehabilitation. A meta-analysis.

Abstract

BACKGROUND:

Evidence for the effectiveness of serious games (SGs) and their various features is inconsistent in the motor rehabilitation field, which makes evidence based development of SGs a rare practice.

OBJECTIVE:

To investigate the effectiveness of SGs in motor rehabilitation for upper limb and movement/balance and to test the potential moderating role of SGs features like feedback, activities, characters and background.

METHODS:

We ran a meta-analysis including 61 studies reporting randomized controlled trials (RCTs), controlled trials (CTs) or case series designs in which at least one intervention for motor rehabilitation included the use of SGs as standalone or in combination.

RESULTS:

There was an overall moderate effect of SGs on motor indices, d = 0.59, [95% CI, 0.48, 0.71], p <  0.001. Regarding the game features, only two out of 17 moderators were statistically different in terms of effect sizes: type of activity (combination of group with individual activities had the highest effects), and realism of the scenario (fantasy scenarios had the highest effects).

CONCLUSIONS:

While we showed that SGs are more effective in improving motor upper limb and movement/balance functions compared to conventional rehabilitation, there were no consistent differences between various game features in their contribution to effects. Further research should systematically investigate SGs features that might have added value in improving effectiveness.

Source: Play seriously: Effectiveness of serious games and their features in motor rehabilitation. A meta-analysis. – PubMed – NCBI

, , , , , , , ,

Leave a comment

[Abstract] A motor rehabilitation BCI with multi-modal feedback in chronic stroke patients (P5.300)

ABSTRACT

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.

Source: A motor rehabilitation BCI with multi-modal feedback in chronic stroke patients (P5.300)

, , , ,

Leave a comment

[ARTICLE] Movement visualisation in virtual reality rehabilitation of the lower limb: a systematic review – Full Text

Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Background

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 [1] and motor learning in general [2]. 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)” [3]. 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 [24]. 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 [5]. 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 [5].

A previous Cochrane Review from Laver, George, Thomas, Deutsch, and Crotty [2] 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” ([2], p. 14).

Virtual rehabilitation system provides three different types of information to the patient: movement visualisation, performance feedback and context information [6]. 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 [789] 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 [101112], although this aspect is yet to be systematically investigated [13].

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 [131415].

Continue —> Movement visualisation in virtual reality rehabilitation of the lower limb: a systematic review | BioMedical Engineering OnLine | Full Text

, , , , , , , ,

Leave a comment

[Abstract+References] Architectural Design of a Cloud Robotic System for Upper-Limb Rehabilitation with Multimodal Interaction 

Abstract

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.

Supplementary material

11390_2017_1720_MOESM1_ESM.pdf (49 kb)

ESM 1(PDF 49 kb)

Source: Architectural Design of a Cloud Robotic System for Upper-Limb Rehabilitation with Multimodal Interaction | SpringerLink

, , , , , , ,

Leave a comment

[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.

[img] Download (63MB) | Preview

Abstract

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.

Source: Ubiquitous and Wearable Computing Solutions for Enhancing Motor Rehabilitation of the Upper Extremity Post-Stroke – Maynooth University ePrints and eTheses Archive

, , , , , , , , ,

Leave a comment

%d bloggers like this: