Posts Tagged Stroke (medical condition)

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


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.
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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 .

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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 .

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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] Complex network changes during a virtual reality rehabilitation protocol following stroke: a case study


Stroke is one of the main causes of disabilities caused by injuries to the human central nervous system, yielding a wide range of mild to severe impairments that can compromise sensorimotor and cognitive functions. Although rehabilitation protocols may improve function of stroke survivors, patients often reach plateaus while undergoing therapy. Recently, virtual reality (VR) technologies have been paired with traditional rehabilitation aiming to improve function recovery after stroke. Aiming to better understand structural brain changes due to VR rehabilitation protocols, we modeled the brain as a graph and extracted three measures representing the network’s topology: degree, clustering coefficient and betweenness centrality (BC). In this single case study, our results indicate that all metrics increased on the ipsilesional hemisphere, while remaining about the same at the contrale-sional site. Particularly, the number of functional connections increased in the lesion area overtime. In addition, the BC displayed the highest variations, and in brain regions related to the patient’s cognitive and motor impairments; hence, we argue that this measure could be regarded as an indicative for brain plasticity mechanisms.
1. J-H. Shin , H. Ryu & S. H. Jang . A task-specific interactive game-based virtual reality rehabilitation system for patients with stroke: a usability test and two clinical experiments. Journal of NeuroEngineering and Rehabilitation. 2014: 11-32

2. M. S. Cameirão , S. B. i Badia , E. D. Oller & P. F. M. J. Verschure . Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation. Journal of NeuroEngineering and Rehabilitation. 2010: 7-48

3. R. M. Yerkes & J. D. Dodson . The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology. 1908. 18: 459-482

4. E. J. Calabrese . Converging concepts: Adaptive response, preconditioning, and the YerkesDodson Law are manifestations of hormesis. Ageing Research Reviews. 2008: 7(1), 820.

5. Page S. J. , Fulk G. D. , Boyne P. Clinically Important Differences for the Upper-Extremity Fugl-Meyer Scale in People With Minimal to Moderate Impairment Due to Chronic Stroke. Physical Therapy 92(6): 791798, 2012. doi: 10.2522/ptj.20110009

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7. NK. Logothetis , J. Pauls , M. Augath , T. Trinath , A. Oeltermann . Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001. 412(6843):150-7

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9. de Campos, B. M. , Coan, A. C. , Lin Yasuda, C. , Casseb, R. F. and Cendes, F. (2016), Large-scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy. Hum. Brain Mapp. doi: 10.1002/hbm.23231

10. J. D. Power , A. L. Cohen , S. M. Nelson , G. S. Wig , K. A. Barnes , J. A. Church , A. C. Vogel , T. O. Laumann , F. M. Miezin , B. L. Schlagger , S. E. Petersen . Functional network organization of the human brain. Neuron. 2011: 72(4): 665 – 678.

11. Rubinov M. and Sporns O. Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 2010, 52(3): 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003

12. M. E. J. Newman . A measure of betweenness centrality based on random walks. Soc. Netw. 2005. 27: 39 – 57.


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


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.

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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.

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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


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.
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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.

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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.

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[Abstract] Decoupling Finger Joint Motion in an Exoskeletal Hand: A Design for Robot-assisted Rehabilitation


In this study, a cable-driven exoskeleton device is developed for stroke patients to enable them to perform passive range of motion exercises and teleoperation rehabilitation of their impaired hands. Each exoskeleton finger is controlled by an actuator via two cables. The motions between the metacarpophalangeal and distal/proximal interphalangeal joints are decoupled, through which the movement pattern is analogous to that observed in the human hand. A dynamic model based on the Lagrange method is derived to estimate how cable tension varies with the angular position of the finger joints. Two discernable phases are observed, each of which reflects the motion of the metacarpophalangeal and distal/proximal interphalangeal joints. The tension profiles of exoskeleton fingers predicted by the Lagrange model are verified through a mechatronic integrated platform. The model can precisely estimate the tensions at different movement velocities, and it shows that the characteristics of two independent phases remain the same even for a variety of movement velocities. The feasibility for measuring resistance when manipulating a patient’s finger is demonstrated in human experiments. Specifically, the net force required to move a subject’s finger joints can be accounted for by the Lagrange model.


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[Abstract] The SonicHand Protocol for Rehabilitation of Hand Motor Function: a validation and feasibility study


Musical sonification therapy is a new technique that can reinforce conventional rehabilitation treatments by increasing therapy intensity and engagement through challenging and motivating exercises. Aim of this study is to evaluate the feasibility and validity of the SonicHand protocol, a new training and assessment method for the rehabilitation of hand function. The study was conducted in 15 healthy individuals and 15 stroke patients. The feasibility of implementation of the training protocol was tested in stroke patients only, who practiced a series of exercises concurrently to music sequences produced by specific movements. The assessment protocol evaluated hand motor performance during pronation/supination, wrist horizontal flexion/extension and hand grasp without sonification. From hand position data, 15 quantitative parameters were computed evaluating mean velocity, movement smoothness and angular excursions of hand/fingers. We validated this assessment in terms of its ability to discriminate between patients and healthy subjects, test-retest reliability and concurrent validity with the upper limb section of the Fugl-Meyer scale (FM), the Functional Independence Measure (FIM) and the Box & Block Test (BBT). All patients showed good understanding of the assigned tasks and were able to correctly execute the proposed training protocol, confirming its feasibility. A moderate-to-excellent intraclass correlation coefficient was found in 8/15 computed parameters. Moderate-to-strong correlation was found between the measured parameters and the clinical scales. The SonicHand training protocol is feasible and the assessment protocol showed good to excellent between-group discrimination ability, reliability and concurrent validity, thus enabling the implementation of new personalized and motivating training programs employing sonification for the rehabilitation of hand function.

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[Abstract + References] Synergy-Based FES for Post-Stroke Rehabilitation of Upper-Limb Motor Functions


Functional electrical stimulation (FES) is capable of activating muscles that are under-recruited in neurological diseases, such as stroke. Therefore, FES provides a promising technology for assisting upper-limb motor functions in rehabilitation following stroke. However, the full benefits of FES may be limited due to lack of a systematic approach to formulate the pattern of stimulation. Our preliminary work demonstrated that it is feasible to use muscle synergy to guide the generation of FES patterns.In this paper, we present a methodology of formulating FES patterns based on muscle synergies of a normal subject using a programmable multi-channel FES device. The effectiveness of the synergy-based FES was tested in two sets of experiments. In experiment one, the instantaneous effects of FES to improve movement kinematics were tested in three patients post ischemic stroke. Patients performed frontal reaching and lateral reaching tasks, which involved coordinated movements in the elbow and shoulder joints. The FES pattern was adjusted in amplitude and time profile for each subject in each task. In experiment two, a 5-day session of intervention using synergy-based FES was delivered to another three patients, in which patients performed task-oriented training in the same reaching movements in one-hour-per-day dose. The outcome of the short-term intervention was measured by changes in Fugl–Meyer scores and movement kinematics. Results on instantaneous effects showed that FES assistance was effective to increase the peak hand velocity in both or one of the tasks. In short-term intervention, evaluations prior to and post intervention showed improvements in both Fugl–Meyer scores and movement kinematics. The muscle synergy of patients also tended to evolve towards that of the normal subject. These results provide promising evidence of benefits using synergy-based FES for upper-limb rehabilitation following stroke. This is the first step towards a clinical protocol of applying FES as therapeutic intervention in stroke rehabilitation.

I. Introduction

Muscle activation during movement is commonly disrupted due to neural injuries from stroke. A major challenge for stroke rehabilitation is to re-establish the normal ways of muscle activation through a general restoration of motor control, otherwise impairments may be compensated by the motor system through a substitution strategy of task control [1]. In post-stroke intervention, new technologies such as neuromuscular electrical stimulation (NMES) or functional electrical stimulation (FES) offer advantages for non-invasively targeting specific groups of muscles [2]–[4] to restore the pattern of muscle activation. Nevertheless, their effectiveness is limited by lack of a systematic methodology to optimize the stimulation pattern, to implement the optimal strategy in clinical settings, and to design a protocol of training towards the goal of restoring motor functions. This pioneer study addresses these issues in clinical application with a non-invasive FES technology.

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1. M. F. Levin, J. A. Kleim, and S. L. Wolf, “What do motor ‘recovery’ and ‘compensation’ mean in patients following stroke?” Neurorehabilitation Neural Repair, vol. 23, no. 4, pp. 313–319, 2008.

2. G. Alon, A. F. Levitt, and P. A. McCarthy, “Functional electrical stimulation (FES) may modify the poor prognosis of stroke survivors with severe motor loss of the upper extremity: A preliminary study,” Amer. J. Phys. Med. Rehabil., vol. 87, no. 8, pp. 627–636, 2008.

3. W. Rong, “A neuromuscular electrical stimulation (NMES) and robot hybrid system for multi-joint coordinated upper limb rehabilitation after stroke,” J. Neuroeng. Rehabil., vol. 14, no. 1, p. 34, Dec. 2017.

4. J. J. Daly, “Recovery of coordinated gait: Randomized controlled stroke trial of functional electrical stimulation (FES) versus no FES, with weight-supported treadmill and over-ground training,” Neurorehabilitation Neural Repair, vol. 25, no. 7, pp. 588–596, Sep. 2011.

5. R. Nataraj, M. L. Audu, R. F. Kirsch, and R. J. Triolo, “Comprehensive joint feedback control for standing by functional neuromuscular stimulation—A simulation study,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 18, no. 6, pp. 646–657, Dec. 2010.

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7. H. Rouhani, M. Same, K. Masani, Y. Q. Li, and M. R. Popovic, “PID controller design for FES applied to ankle muscles in neuroprosthesis for standing balance,” Frontiers Neurosci., vol. 11, p. 347, Jun. 2017.

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