Archive for category Paretic Hand

[Abstract + References] Upper Limb Rehabilitation Therapies Based in Videogames Technology Review

Abstract

Worldwide, stroke is the third cause of physical disability, rehabilitation therapy is a main topic of focus for the recovery of life quality. Rehabilitation of these patients presents great challenges since many of them do not find the motivation to perform the necessary exercises, or do not have the economic resources or the adequate support to receive physiotherapy. For several years now, an alternative that has been in development is game-based rehabilitation, since this could be used in a hospital environment and eventually at patients home. The aim of this review is to present the advances in videogames technology to be used for rehabilitation and training purposes- in preparation for prosthetics fitting or Neuroprosthesis control training–, as well as the devices that are being used to make this alternative more tangible. Videogames technology rehabilitation still has several challenges to work on, more research and development of platforms to have a larger variety of games to engage with different age-range patients is still necessary.
1. Y. X. Hung , P. C. Huang , K. T. Chen , and W. C. Chu , “ What do stroke patients look for in game-based rehabilitation: A survey study ,” Med. (United States) , vol. 95 , no. 11 , pp. 1 – 10 , 2016 .

2. E. Vogiatzaki , Y. Gravezas , N. Dalezios , D. Biswas , A. Cranny , and S. Ortmann , “ Telemedicine System for Game-Based Rehabilitation of Stroke Patients in the FP7- ‘ StrokeBack ’ Project ,” 2014 .

3. W. Johnson , O. Onuma , and S. Sachdev , “ Stroke: a global response is needed ,” Bull. World Heal. Organ ., vol. 94 p. 634 – 634A , 2016 .

4. A. Tabor , S. Bateman , E. Scheme , D. R. Flatla , and K. Gerling , “ Designing Game-Based Myoelectric Prosthesis Training ,” in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems – CHI ’17 , 2017 , pp. 1352 – 1363 .

5. B. Lange et al. , “ Interactive game-based rehabilitation using the Microsoft Kinect ,” Proc. – IEEE Virtual Real ., no. November 2016 , pp. 171 – 172 , 2012 .

6. C. Prahm , I. Vujaklija , F. Kayali , P. Purgathofer , and O. C. Aszmann , “ Game-Based Rehabilitation for Myoelectric Prosthesis Control ,” JMIR Serious Games , vol. 5 , no. 1 , pp. 1 – 13 , 2017 .

7. B. D. Winslow , M. Ruble , and Z. Huber , “ Mobile, Game-Based Training for Myoelectric Prosthesis Control ,” Front. Bioeng. Biotechnol .,vol. 6 , no. July , pp. 1 – 8 , 2018 .

8. “ The SENIAM Project ,” 2019 . [Online]. Available: http://www.seniam.org . [Accessed: 21-Jan-2019 ].

9. M. B. I. Reaz , M. S. Hussain , and F. Mohd-Yasin , “ Techniques of EMG signal analysis: Detection, processing, classification and applications ,” Biol. Proced. Online , vol. 8 , no. 1 , pp. 11 – 35 , 2006 .

10. R. S. Armiger and R. J. Vogelstein , “ Air-Guitar Hero: A real-time video game interface for training and evaluation of dexterous upper-extremity neuroprosthetic control algorithms ,” Circuits Syst. Conf. BIOCAS 2008 , pp. 121 – 124 , 2008 .

11. H. Oppenheim , R. S. Armiger , and R. J. Vogelstein , “ WiiEMG: A real-time environment for control of the Wii with surface electromyography ,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems , 2010 , pp. 957 – 960 .

12. G. I. Yatar and S. A. Yildirim , “ Wii Fit balance training or progressive balance training in patients with chronic stroke: a randomised controlled trial ,” J. Phys. Ther. Sci ., vol. 27 , no. 4 , pp. 1145 – 1151 , 2015 .

13. N. Norouzi-Gheidari , M. F. Levin , J. Fung , and P. Archambault , “ Interactive virtual reality game-based rehabilitation for stroke patients ,” in 2013 International Conference on Virtual Rehabilitation, ICVR 2013 2013 .

14. B. Lange , C. Chang , E. Suma , B. Newman , A. S. Rizzo , and M. Bolas , “ Development and Evaluation of Low Cost Game-Based Balance Rehabilitation Tool Using the Microsoft Kinect Sensor ,” 2011 , pp. 1831 – 1834 .

15. Y. Chen et al. , “ Game Analysis, Validation, and Potential Application of EyeToy Play and Play 2 to Upper-Extremity Rehabilitation ,” no. December , 2014 .

16. P. Visconti , F. Gaetani , G. A. Zappatore , and P. Primiceri , “ Technical features and functionalities of Myo armband: An overview on related literature and advanced applications of myoelectric armbands mainly focused on arm prostheses ,” Int. J Smart Sens. Intell. Syst ., vol. 11 , no. 1 , pp. 1 – 25 , 2018 .

17. S. S. Esfahlani and G. Wilson , “ Development of Rehabilitation System (ReHabgame) through Monte-Carlo Tree Search Algorithm ,” 2018 , pp. 1 – 8 .

18. “ Welcome to Myo Support ,” 2019 . [Online]. Available: https://support.getmyo.com/hc/en-us [Accessed: 19-Jan-2019 ].

19. “ PAULA 1.2 | Myo Software | Myo Hands and Components |Upper Limb Prosthetics | Prosthetics | Ottobock US Healthcare .”[Online]. Available: https://professionals.ottobockus.com/Prosthetics/Upper-Limb-Prosthetics/Myo-Hands-and-Components/Myo-Software/PAULA-1-2/p/646C52~5V1~82 [Accessed: 21-Jan-2019 ].

20. J. Lewis , P. Merritt , M. Bowler , and D. Brown , “ Evaluation of the suitability of games based stroke rehabilitation using the Novint Falcon ,” 2018 , no. August .

21. G. Ghazaei , A. Alameer , P. Degenaar , G. Morgan , and K. Nazarpour , “ Deep learning-based artificial vision for grasp classification in myoelectric hands ,” J. Neural Eng ., vol. 14 , no. 3 , 2017 .

22. B. Terlaak , H. Bouwsema , C. K. V. D. Sluis , and R. M. Bongers , “ Virtual training of the myosignal ,” PLoS One , vol. 10 , no. 9 , 2015 .

23. J. W. Burke , M. D. J. McNeill , D. K. Charles , P. J. Morrow , J. H. Crosbie , and S. M. McDonough , “ Designing Engaging, Playable Games for Rehabilitation ,” in 8th International Conference on Disability, Virtual Reality and Associated Technologies (ICDVRAT) , 2010 , pp. 195 – 201 .

 

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[Abstract + References] eConHand: A Wearable Brain-Computer Interface System for Stroke Rehabilitation

Abstract

Brain-Computer Interface (BCI) combined with assistive robots has been developed as a promising method for stroke rehabilitation. However, most of the current studies are based on complex system setup, expensive and bulky devices. In this work, we designed a wearable Electroencephalography(EEG)-based BCI system for hand function rehabilitation of the stroke. The system consists of a customized EEG cap, a small-sized commercial amplifer and a lightweight hand exoskeleton. In addition, visualized interface was designed for easy use. Six healthy subjects and two stroke patients were recruited to validate the safety and effectiveness of our proposed system. Up to 79.38% averaged online BCI classification accuracy was achieved. This study is a proof of concept, suggesting potential clinical applications in outpatient environments.

2. E. Donchin , K. Spencer and R. Wijesinghe , “The mental prosthesis: assessing the speed of a P300-based brain-computer interface”, IEEE Transactions on Rehabilitation Engineering, vol. 8, no. 2, pp. 174-179, 2000.

3. D. McFarland and J. Wolpaw , “Brain-Computer Interface Operation of Robotic and Prosthetic Devices”, Computer, vol. 41, no. 10, pp. 52-56, 2008.

4. Xiaorong Gao , Dingfeng Xu , Ming Cheng and Shangkai Gao , “A bci-based environmental controller for the motion-disabled”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 2, pp. 137-140, 2003.

5. A. Ramos-Murguialday , D. Broetz , M. Rea et al “Brain-machine interface in chronic stroke rehabilitation: A controlled study”, Annals of Neurology, vol. 74, no. 1, pp. 100-108, 2013.

6. F. Pichiorri , G. Morone , M. Petti et al “Brain-computer interface boosts motor imagery practice during stroke recovery”, Annals of Neurology, vol. 77, no. 5, pp. 851-865, 2015.

7. M. A. Cervera , S. R. Soekadar , J. Ushiba et al “Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis”, Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651-663, 2018.

8. K. Ang , K. Chua , K. Phua et al “A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke”, Clinical EEG and Neuroscience, vol. 46, no. 4, pp. 310-320, 2014.

9. N. Bhagat , A. Venkatakrishnan , B. Abibullaev et al “Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors”, Frontiers in Neuroscience, vol. 10, pp. 122, 2016.

10. J. Webb , Z. G. Xiao , K. P. Aschenbrenner , G. Herrnstadt , and C. Menon , “Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface”, in Biomedical Robotics and Biomechatronics (BioRob), 2012 4th IEEE RAS & EMBS International Conference, pp. 1299-1304, 2012.

11. A. L. Coffey , D. J. Leamy , and T. E. Ward , “A novel BCI-controlled pneumatic glove system for home-based neurorehabilitation”, in Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, pp. 3622-3625, 2014.

12. D. Bundy , L. Souders , K. Baranyai et al “Contralesional Brain-Computer Interface Control of a Powered Exoskeleton for Motor Recovery in Chronic Stroke Survivors”, Stroke, vol. 48, no. 7, pp. 1908-1915, 2017.

13. X. Shu , S. Chen , L. Yao et al “Fast Recognition of BCI-Inefficient Users Using Physiological Features from EEG Signals: A Screening Study of Stroke Patients”, Frontiers in Neuroscience, vol. 12, pp. 93, 2018.

14. A. Delorme , T. Mullen , C. Kothe et al “EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing”, Computational Intelligence and Neuroscience, vol. 2011, pp. 1-12, 2011.

15. G. Schalk , D. McFarland , T. Hinterberger , N. Birbaumer and J. Wolpaw , “BCI2000: A General-Purpose Brain-Computer Interface (BCI) System”, IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 1034-1043, 2004.

16. M. H. B. Azhar , A. Casey , and M. Sakel , “A cost-effective BCI assisted technology framework for neurorehabilitation”, The Seventh International Conference on Global Health Challenges, 18th-22nd November, 2018. (In Press)

17. C. M. McCrimmon , M. Wang , L. S. Lopes et al “A small, portable, battery-powered brain-computer interface system for motor rehabilitation”, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2776-2779, 2016.

18. J. Meng , B. Edelman , J. Olsoe et al “A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance”, Frontiers in Neuroscience, vol. 12, pp. 227, 2018.

19. T. Mullen , C. Kothe , Y. Chi et al “Real-time neuroimaging and cognitive monitoring using wearable dry EEG”, IEEE Transactions on Biomedical Engineering, vol. 62, no. 11, pp. 2553-2567, 2015.

 

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[Abstract + References] Electromyographic indices of muscle fatigue of a severely paralyzed chronic stroke patient undergoing upper limb motor rehabilitation

Abstract

Modern approaches to motor rehabilitation of severe upper limb paralysis in chronic stroke decode movements from electromyography for controlling rehabilitation orthoses. Muscle fatigue is a phenomenon that influences these neurophysiological signals and may diminish the decoding quality. Characterization of these potential signal changes during movement patterns of rehabilitation training could therefore help improve the decoding accuracy. In the present work we investigated how electromyographic indices of muscle fatigue in the Deltoid Anterior muscle evolve during typical forward reaching movements of a rehabilitation training in healthy subjects and a stroke patient. We found that muscle fatigue in healthy subjects changed the neurophysiological signal. In the patient, however, no consistent change was observed over several sessions.
1. V. L. Feigin , B. Norrving , M. G. George , J. L. Foltz , A. Roth Gregory , and G. A. Mensah , “Prevention of stroke: a strategic global imperative,” Nat. Rev. Neurol., vol. 107, pp. 501–512, 2016.

2. A. Ramos-Murguialday et al , “Brain-machine interface in chronic stroke rehabilitation: a controlled study,” Ann. Neurol., vol. 74, no. 1, pp. 100–108, 2013.

3. A. Sarasola-Sanz et al , “A hybrid brain-machine interface based on EEG and EMG activity for the motor rehabilitation of stroke patients,” IEEE Int Conf Rehabil Robot, vol. 2017, pp. 895–900, Jul. 2017.

4. R. M. Enoka and J. Duchateau , “Muscle fatigue: what, why and how it influences muscle function,” J Physiol, vol. 586, no. 1, pp. 11–23, Jan. 2008.

5. M. González-Izal , A. Malanda , E. Gorostiaga , and M. Izquierdo , “Electromyographic models to assess muscle fatigue,” J. Electromyogr. Kinesiol., vol. 22, no. 4, pp. 501–512, Aug. 2012.

6. A. Sarasola Sanz et al , “EMG-based multi-joint kinematics decoding for robot-aided rehabilitation therapies,” in 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), 2015.

7. P. V. Komi and P. Tesch , “EMG frequency spectrum, muscle structure, and fatigue during dynamic contractions in man,” Eur. J Appl Physiol, vol. 42, no. 1, pp. 41–50, Sep. 1979.

8. D. R. Rogers and D. T. MacIsaac , “A comparison of EMG-based muscle fatigue assessments during dynamic contractions,” J. Electromyogr. Kinesiol., vol. 23, no. 5, pp. 1004–1011, Oct. 2013.

9. B. Bigland-Ritchie , E. F. Donovan , and C. S. Roussos , “Conduction velocity and EMG power spectrum changes in fatigue of sustained maximal efforts,” J Appl Physiol Respir Env. Exerc Physiol, vol. 51, no. 5, pp. 1300–1305, Nov. 1981.

10. G. V. Dimitrov , T. I. Arabadzhiev , K. N. Mileva , J. L. Bowtell , N. Crichton , and N. A. Dimitrova , “Muscle Fatigue during Dynamic Contractions Assessed by New Spectral Indices,” Med. Sci. Sports Exerc., 2006.

11. N. A. Riley and M. Bilodeau , “Changes in upper limb joint torque patterns and EMG signals with fatigue following a stroke,” Disabil Rehabil, vol. 24, no. 18, pp. 961–969, Dec. 2002.

12. M. J. Campbell , A. J. McComas , and F. Petito , “Physiological changes in ageing muscles,” J. Neurol. Neurosurg. Psychiatry, vol. 36, no. 2, pp. 174–182, 1973.

 

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[Abstract + References] Self-paced movement intention recognition from EEG signals during upper limb robot-assisted rehabilitation

Abstract

Currently, one of the challenges in EEG-based brain-computer interfaces (BCI) for neurorehabilitation is the recognition of the intention to perform different movements from same limb. This would allow finer control of neurorehabilitation and motor recovery devices by end-users [1]. To address this issue, we assess the feasibility of recognizing two self-paced movement intentions of the right upper limb plus a rest state from EEG signals recorded during robot-assisted rehabilitation therapy. In addition, the work proposes the use of Multi-CSP features and deep learning classifiers to recognize movement intentions of the same limb. The results showed performance peaked greater at (80%) using a novel classification models implemented in a multiclass classification scenario. On the basis of these results, the decoding of the movement intention could potentially be used to develop more natural and intuitive robot assisted neurorehabilitation therapies
1. S. R. Soekadar , N. Birbaumer , M. W. Slutzky , and L. G. Cohen , “Brain machine interfaces in neurorehabilitation of stroke,” Neurobiology of Disease, vol. 83, pp. 172-179, 2015.

2. P. Ofner , A. Schwarz , J. Pereira , and G. R. Müller-Putz , “Upper limb movements can be decoded from the time-domain of low-frequency EEG,” PLoS One, vol. 12, no. 8, p. e0182578, Aug 2017, poNE-D- 17-04785[PII].

3. F. Shiman , E. Lopez-Larraz , A. Sarasola-Sanz , N. Irastorza-Landa , M. Spler , N. Birbaumer , and A. Ramos-Murguialday , “Classification of different reaching movements from the same limb using EEG,” Journal of Neural Engineering, vol. 14, no. 4, p. 046018, 2017.

4. J. Pereira , A. I. Sburlea , and G. R. Müller-Putz , “EEG patterns of self- paced movement imaginations towards externally-cued and internally- selected targets,” Scientific Reports, vol. 8, no. 1, p. 13394, 2018.

5. R. Vega , T. Sajed , K. W. Mathewson , K. Khare , P. M. Pilarski , R. Greiner , G. Sanchez-Ante , and J. M. Antelis , “Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals,” Artif. Intell. Research, vol. 6, no. 1, p. 37, 2017.

6. I. Figueroa-Garcia et al , “Platform for the study of virtual task- oriented motion and its evaluation by EEG and EMG biopotentials,” in 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Aug 2014, pp. 1174–1177.

7. B. Graimann and G. Pfurtscheller , “Quantification and visualization of event-related changes in oscillatory brain activity in the timefrequency domain,” in Event-Related Dynamics of Brain Oscillations, ser. Progress in Brain Research, C. Neuper and W. Klimesch , Eds. Elsevier, 2006, vol. 159, pp. 79 – 97.

8. G. Pfurtscheller and F. L. da Silva , “Event-related EEG/MEG synchronization and desynchronization: basic principles,” Clinical Neurophysiology, vol. 110, no. 11, pp. 1842 – 1857, 1999.

9. G. Dornhege , B. Blankertz , G. Curio , and K. Muller , “Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 6, pp. 993–1002, 2004.

10. X. Yong and C. Menon , “EEG classification of different imaginary movements within the same limb,” PLOS ONE, vol. 10, no. 4, pp. 1–24, 04 2015.

11. L. G. Hernandez , O. M. Mozos , J. M. Ferrandez , and J. M. Antelis , “EEG-based detection of braking intention under different car driving conditions,” Frontiers in Neuroinformatics, vol. 12, p. 29, 2018. [Online]. Available: https://www.frontiersin.org/article/10.3389/fninf.2018.00029

12. L. G. Hernandez and J. M. Antelis , “A comparison of deep neural network algorithms for recognition of EEG motor imagery signals,” in Pattern Recognition, 2018, pp. 126–134.

13. M. Abadi et al , “TensorFlow: Large-scale machine learning on heterogeneous systems,” 2015, software available from tensorflow.org. [Online]. Available: https://www.tensorflow.org/

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[ARTICLE] Elements virtual rehabilitation improves motor, cognitive, and functional outcomes in adult stroke: evidence from a randomized controlled pilot study – Full Text

 

Abstract

Background

Virtual reality technologies show potential as effective rehabilitation tools following neuro-trauma. In particular, the Elements system, involving customized surface computing and tangible interfaces, produces strong treatment effects for upper-limb and cognitive function following traumatic brain injury. The present study evaluated the efficacy of Elements as a virtual rehabilitation approach for stroke survivors.

Methods

Twenty-one adults (42–94 years old) with sub-acute stroke were randomized to four weeks of Elements virtual rehabilitation (three weekly 30–40 min sessions) combined with treatment as usual (conventional occupational and physiotherapy) or to treatment as usual alone. Upper-limb skill (Box and Blocks Test), cognition (Montreal Cognitive Assessment and selected CogState subtests), and everyday participation (Neurobehavioral Functioning Inventory) were examined before and after inpatient training, and one-month later.

Results

Effect sizes for the experimental group (d = 1.05–2.51) were larger compared with controls (d = 0.11–0.86), with Elements training showing statistically greater improvements in motor function of the most affected hand (p = 0.008), and general intellectual status and executive function (p ≤ 0.001). Proportional recovery was two- to three-fold greater than control participants, with superior transfer to everyday motor, cognitive, and communication behaviors. All gains were maintained at follow-up.

Conclusion

A course of Elements virtual rehabilitation using goal-directed and exploratory upper-limb movement tasks facilitates both motor and cognitive recovery after stroke. The magnitude of training effects, maintenance of gains at follow-up, and generalization to daily activities provide compelling preliminary evidence of the power of virtual rehabilitation when applied in a targeted and principled manner.

Trial registration

this pilot study was not registered.

Introduction

Stroke is one of the most common forms of acquired brain injury (ABI), with around 60,000 new and recurrent strokes occurring every year in Australia alone [1]. The clinical outcome of stroke is variable but often includes persistent upper-limb motor deficits, including weakness, discoordination, and reduced speed and mobility [2], and cognitive impairments in information processing and executive function [34]. Not surprisingly, stroke is a leading cause of disability worldwide, and the burden of stroke across all levels of the International Classification of Functioning (ICF) – body structures/function, activity, and participation – underlines the importance of interventions that can impact multiple domains of functioning [56].

Recovery of functional performance following stroke remains a significant challenge for rehabilitation specialists [78], but may be enhanced by innovation in the use of new technologies like virtual reality [9101112]. A critical goal is to find compelling ways of engaging individuals in their therapy by creating meaningful, stimulating and intensive forms of training [13]. The term, virtual rehabilitation (VR), is used to describe a form of training wherein patients interact with virtual or augmented environments, presented with the aid of technology [1415]. The technologies can be either commercial systems (e.g. Nintendo Wii, Xbox Kinect) or those customised specifically for rehabilitation. VR offers a number of advantages over traditional therapies, including the ability to engage individuals in the simulated practice of functional tasks at higher doses [1617], automated assessment of performance over time, flexibility in the scaling of task constraints, and a variety of reward structures to help maintain compliance [18].

While evaluation research is still in its infancy, recent systematic reviews and meta-analyses show that VR can enhance upper-limb motor outcomes in stroke [101119], yielding treatment effects of medium-to-large magnitude [1011], and complementing conventional approaches to rehabilitation. VR has been shown to engender high levels of engagement in stroke patients undergoing physical therapy [2021] and training of even moderate intensity can afford functional benefits at the activity/skill level [919]. In the specific case of upper-limb VR, however, there is little available evidence that these benefits transfer to participation [9]. Furthermore, most available data is on patients in chronic stages of recovery, with less on acute stroke [9]. Notwithstanding this, use of VR has begun to emerge in clinical practice, recommended in Australian and international stroke guidelines as a viable adjunct in therapy to improve motor and functional outcomes [222324].

Until recently, most VR systems have been designed to improve motor functions, with cognitive outcomes often a secondary consideration in evaluation studies [91011]. Notwithstanding this, treatments that target both motor and cognitive functions are indicated for stroke, given evidence that cognitive and motor systems overlap at a structural and functional level [2526], and work synergistically in a “perception-action cycle” [27] in stroke patients undergoing rehabilitation [28]. Recent studies provide preliminary evidence of improved attention and memory in stroke patients following motor-oriented VR [29303132], amounting to a small-to-medium effect on cognition [9]. When designed to address aspects of cognitive control and planning, VR has the potential to enhance dual-task control, resulting in better generalization of trained skills to daily functioning [33].

While evaluation research is still in its infancy, several recent customized systems (like Elements, the system evaluated here) have been deliberately designed to exploit factors known to enhance training intensity and motor learning. Informed by neuroscience and learning theory [for a recent review see 12], the Elements VR system was designed to enhance neuro-plastic recovery processes via: (1) an enriched therapeutic environment affording a natural form of user interaction via tangible computing and surface displays [34], which engage both the cognitive attention of participants and their motivation to explore training tasks; (2) concurrent augmented feedback (AF) on performance [35] offering participants additional information on the outcome of their actions to assist in re-building a sense of body position in space (aka body schema) and ability to predict/plan future actions; and (3) scaling of task challenges to the current level of motor and cognitive function [36], ensuring dynamic scaffolding of participants’ information processing and response capabilities. The Elements system, described in detail below and in earlier publications [3738], consists of a large (42 in.) tabletop surface display, tangible user interfaces, and software for presenting both goal-directed and exploratory virtual environments. Previous evaluations of the system in patients with traumatic brain injury showed improvements in both motor and cognitive performance, with transfer to activities of daily living [3739]. However, the impact of Elements in other forms of ABI, such as stroke, has not been evaluated.

The broad aim of current study was to evaluate the efficacy of the Elements VR interactive tabletop system for rehabilitation of motor and cognitive functions in sub-acute stroke, compared with treatment as usual (TAU). We were particularly interested in motor and cognitive outcomes, their relationship, and the transfer and maintenance of treatment effects. Training-related changes at the activity/skill level on standardized measures of motor and cognitive performance were investigated, together with functional changes. By offering an engaging, principled and customized form of interaction, we predicted that the Elements system would effect (i) greater changes on both motor and cognitive outcomes than with TAU alone; (ii) sustained benefits, as assessed over a short follow-up period, and (iii) transfer to everyday functional performance (i.e. participation).[…]

 

Continue —> Elements virtual rehabilitation improves motor, cognitive, and functional outcomes in adult stroke: evidence from a randomized controlled pilot study | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 1

 

Fig. 1

Examples of the Elements (a) goal-directed Bases task with visual augmented feedback, and (b) exploratory Squiggles task

 

 

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[NEWS] Robotic Rehab Aims for the Home Market in Q3

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MotusNova

Motus Nova is expanding its list of partner hospitals and clinics using its FDA-approved robotic stroke therapy system. It also plans to introduce its system to the consumer market for home use in Q3 2019.

Twenty-five hospitals in the Atlanta area within Emory Healthcare, the Grady Health System, and the Wellstar Health System are now using the Motus Nova rehabilitation therapy system, which is designed to use Artificial Intelligence (AI) to accelerate recovery from neurological injuries such as strokes.

The system features a Hand Mentor and Foot Mentor, which are sleeve-like robots that fit over a stroke survivor’s impaired hand or foot. Equipped with an active-assist air muscle and a suite of sensors and accelerometers, they provide clinically appropriate assistance and resistance while individual’s perform the needed therapeutic exercises.

A touchscreen console provides goal-directed biofeedback through interactive games—which Motus Nova calls “theratainment”—that make the tedious process of neuro rehab engaging and fun.

“It’s a system that has proven to be a valuable partner to stroke therapy professionals, where it complements skilled clinical care by augmenting the repetitive rehabilitation requirements of stroke recovery and freeing the clinician to do more nuanced care and assessment,” says Nick Housley, director of clinical research for Atlanta-based Motus Nova, in a media release.

“And while we continue to fill orders for the system to support therapy in the clinic and hospital, we also are looking to use our system to fill the gap patients often experience in receiving the needed therapy once they go home.”

Clinical studies show that neuroplasticity begins after approximately many 10’s to 100’s of hours of active guided rehab. The healing process can take months or years, and sometimes the individuals might never fully recover. Yet the typical regimen for stroke survivors is only two to three hours of outpatient therapy per week for a period of three to four months.

“These constraints were instituted by the Centers for Medicare & Medicaid Services (CMS) in determining Medicare reimbursement without a full understanding of the appropriate dosing required for stroke recovery, and many private insurers have adopted the policy, as well,” states David Wu, Motus Nova’s CEO.

Motus Nova plans to offer a more practical model, the release continues.

“By making the system available for home use at a reasonable weekly rate as long as the patient needs it, the individual can perform therapy anytime,” Wu adds. “A higher dosage of therapy can be achieved without the inconvenience of scheduling appointments with therapists or traveling to and from a clinic, and without the high cost of going to an outpatient center every time the individual wants to do therapy.”

While the system gathers data about individual performance, AI tailors the regimen to maximize user gains, discover new approaches, minimize side effects and help the stroke survivor realize his or her full potential more quickly.

“By optimizing factors such as frequency, intensity, difficulty, encouragement, and motivation, the AI system builds a personalized medicine plan uniquely tailored to each individual user of the system,” Housley comments.

“Our system is durable, too, proven in clinical trials to deliver an engaging physical therapy experience over thousands of repetitions. We look forward to making it available on a much wider scale in the coming months.”

[Source(s): Motus Nova, PR Newswire]

 

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[Abstract] An Adaptive Iterative Learning Based Impedance Control For Robot-Aided Upper-limb Passive Rehabilitation

In this paper, an anthropomorphic arm is introduced and used to the upper-limb passive rehabilitation therapy. The anthropomorphic arm is constructed via pneumatic artificial muscles so that it may assist patients suffering upper-limb diseases to achieve mild therapeutic exercises. Due to the uncertain dynamic environment, external disturbances and model uncertainties, a combined control is proposed to stabilize and to enhance the adaptivity of the system. In the combined control, an iterative learning control is used to realize accurate position tracking. Meanwhile, an adaptive iterative learning based impedance control is proposed to execute the appropriate contact force during the therapy of the upper-limb. The advantage of the combined control is that it doesn’t depend on the accurate model of systems and it may deal with highly nonlinear system which has strong coupling and redundancies. The convergence of the adaptive iterative learning based impedance control is emphasized analyzed. Numerical simulations are performed to verify the proposed control method. In addition, real experiments are executed on the Southwest anthropomorphic arm.

Source :
https://www.frontiersin.org/articles/10.3389/frobt.2019.00041/abstract  

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[NEWS] NEOFECT Redesigns Smart Board for Home

Published on May 8, 2019

SmartBoardforHome

NEOFECT has redesigned its Smart Board for Home in reply to feedback from patients recovering from stroke and other musculoskeletal conditions and neurological disorders.

The new Smart Board for Home NextGen includes a smaller surface to help patients use it at home more easily, a redesigned handle to better stabilize the user’s hand and arm, and updated gamified software.

The board size has been reduced from 42 inches to 32 inches so it can fit on most tables. To accommodate the weakened grip of many stroke patients, the redesigned handle includes more straps to better stabilize the user’s arm, ensure appropriate measurement for the post-game metrics, and provide a more secure, comfortable experience, according to the company in a media release.

“We took patient feedback and completely revamped the Smart Board for Home NextGen,” says Scott Kim, co-founder and CEO of San Francisco-based NEOFECT USA.

“This new model still has all the fun, measurable qualities patients can use at home, but now we’ve reduced even more barriers so that people of all abilities can gain back function in their hands and upper arms.”

Patients play games on the Smart Board for Home NextGen by placing their forearm in a cradle and moving their arm across the board. All movements are virtually mimicked on a Bluetooth-connected screen in real time. The gamified software also features an updated AI-powered algorithm to curate a more customized experience for each patient.

The Smart Board for Home NextGen games mimic real-world motions to rehabilitate users’ upper arms and shoulders, including new games like “Air Hawk” and “Tennis.”

Additionally, NEOFECT is developing a dual-player game for patients to use at home, which will be available in summer 2019.

[Source(s): NEOFECT, Business Wire]

Source:
http://www.rehabpub.com/2019/05/neofect-redesigns-smart-board-home/

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[ARTICLE] Intensive upper limb neurorehabilitation in chronic stroke: outcomes from the Queen Square programme – Full Text

Abstract

Objective Persistent difficulty in using the upper limb remains a major contributor to physical disability post-stroke. There is a nihilistic view about what clinically relevant changes are possible after the early post-stroke phase. The Queen Square Upper Limb Neurorehabilitation programme delivers high-quality, high-dose, high-intensity upper limb neurorehabilitation during a 3-week (90 hours) programme. Here, we report clinical changes made by the chronic stroke patients treated on the programme, factors that might predict responsiveness to therapy and the relationship between changes in impairment and activity.

Methods Upper limb impairment and activity were assessed on admission, discharge, 6 weeks and 6 months after treatment, with modified upper limb Fugl-Meyer (FM-UL, max-54), Action Research Arm Test (ARAT, max-57) and Chedoke Arm and Hand Activity Inventory (CAHAI, max-91). Patient-reported outcome measures were recorded with the Arm Activity Measure (ArmA) parts A (0–32) and B (0–52), where lower scores are better.

Results 224 patients (median time post-stroke 18 months) completed the 6-month programme. Median scores on admission were as follows: FM-UL = 26 (IQR 16–37), ARAT=18 (IQR 7–33), CAHAI=40 (28-55), ArmA-A=8 (IQR 4.5–12) and ArmA-B=38 (IQR 24–46). The median scores 6 months after the programme were as follows: FM-UL=37 (IQR 24–48), ARAT=27 (IQR 12–45), CAHAI=52 (IQR 35–77), ArmA-A=3 (IQR 1–6.5) and ArmA-B=19 (IQR 8.5–32). We found no predictors of treatment response beyond admission scores.

Conclusion With intensive upper limb rehabilitation, chronic stroke patients can change by clinically important differences in measures of impairment and activity. Crucially, clinical gains continued during the 6-month follow-up period.

Introduction

Stroke remains common1 and persistent difficulty in using the upper limb is a major contributor to ongoing physical disability.2 The general consensus remains that most spontaneous recovery of the upper limb occurs over the first 3 months after stroke and current levels of rehabilitation result in little improvement after that, particularly at the level of impairment.3 Improving outcomes through higher dose (time in rehabilitation or number of repetitions) and intensity (dose per session) of rehabilitation is an attractive option.4 However, clinical trials of higher dose upper limb rehabilitation have generally not produced the magnitude of improvement that will change clinical practice,5 whether delivered in the early6 or chronic stages post-stroke.7–9 A common factor in these trials is that the dose (in hours) of additional therapy remained relatively low (18–36 hours). Despite scepticism that stroke patients could tolerate much higher doses,8 one study managed to deliver 300 hours of upper limb therapy to chronic stroke patients over 12 weeks and reported changes in measures of both impairment and activity that were far greater than those in lower dose studies.10 Three hundred hours represents an order of magnitude higher than any dose of rehabilitation offered in previous upper limb rehabilitation trials and deserves further consideration. However, this idea is challenging because of the logistics of setting up such a trial in healthcare settings where the ethic of high-dose, high-intensity rehabilitation is not supported. In this context, it is important to report the findings of clinical services that are able to deliver higher doses than conventionally seen. The Queen Square Upper Limb (QSUL) Neurorehabilitation programme is a single-centre clinical service that provides 90 hours of timetabled treatment focusing on the post-stroke upper limb in chronic (>6 months post-stroke) stroke patients. Here, we report (i) outcomes for patients admitted to this programme at the National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Trust (UCLH), including 6-month follow-up data to look at whether any clinical benefits were maintained, (ii) the characteristics of the patients admitted and any predictors of response and (iii) the relationship between changes in impairment and activity.[…]

Continue —> https://jnnp.bmj.com/content/90/5/498

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[Abstract] Brain-machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review

Abstract

BACKGROUND:

Technologies such as brain-computer interfaces are able to guide mental practice, in particular motor imagery performance, to promote recovery in stroke patients, as a combined approach to conventional therapy.

OBJECTIVE:

The aim of this systematic review was to provide a status report regarding advances in brain-computer interface, focusing in particular in upper limb motor recovery.

METHODS:

The databases PubMed, Scopus, and PEDro were systematically searched for articles published between January 2010 and December 2017. The selected studies were randomized controlled trials involving brain-computer interface interventions in stroke patients, with upper limb assessment as primary outcome measures. Reviewers independently extracted data and assessed the methodological quality of the trials, using the PEDro methodologic rating scale.

RESULTS:

From 309 titles, we included nine studies with high quality (PEDro ≥ 6). We found that the most common interface used was non-invasive electroencephalography, and the main neurofeedback, in stroke rehabilitation, was usually visual abstract or a combination with the control of an orthosis/robotic limb. Moreover, the Fugl-Meyer Assessment Scale was a major outcome measure in eight out of nine studies. In addition, the benefits of functional electric stimulation associated to an interface were found in three studies.

CONCLUSIONS:

Neurofeedback training with brain-computer interface systems seem to promote clinical and neurophysiologic changes in stroke patients, in particular those with long-term efficacy.

via: https://www.ncbi.nlm.nih.gov/pubmed/30609208

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