Archive for category REHABILITATION

[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.
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: . [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: [Accessed: 19-Jan-2019 ].

19. “ PAULA 1.2 | Myo Software | Myo Hands and Components |Upper Limb Prosthetics | Prosthetics | Ottobock US Healthcare .”[Online]. Available: [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] 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

6. Ogawa S , Lee TM , Kay AR , Tank DW . Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A. 1990; 87(24):9868-72. doi: 10.1073/pnas.87.24.9868

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

8. M.D. Fox , M. E. Raichle . Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci. 2007. 8(9):700-11.

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.

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


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


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:

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 [Online]. Available:

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[NEWS] Brain-controlled, non-invasive muscle stimulation allows chronic paraplegics to walk

Brain-controlled, non-invasive muscle stimulation allows chronic paraplegics to walk again and exhibit partial motor recovery



In another major clinical breakthrough of the Walk Again Project, a non-profit international consortium aimed at developing new neuro-rehabilitation protocols, technologies and therapies for spinal cord injury, two patients with paraplegia regained the ability to walk with minimal assistance, through the employment of a fully non-invasive brain-machine interface that does not require the use of any invasive spinal cord surgical procedure. The results of this study appeared on the May 1 issue of the journal Scientific Reports.

The two patients with paraplegia (AIS C) used their own brain activity to control the non-invasive delivery of electrical pulses to a total of 16 muscles (eight in each leg), allowing them to produce a more physiological walk than previously reported, requiring only a conventional walker and a body weight support system as assistive devices. Overall, the two patients were able to produce more than 4,500 steps using this new technology, which combines a non-invasive brain-machine interface, based on a 16-channel EEG, to control a multi-channel functional electrical stimulation system (FES), tailored to produce a much smoother gait pattern than the state of the art of this technique.

“What surprised us was that, in addition to allowing these patients to walk with little help, one of them displayed a clear motor improvement by practicing with this new approach. Patients required approximatively 25 sessions to master the training before they were able to walk using this apparatus,” said Solaiman Shokur one of the authors of the study.

The two patients that used this new rehabilitation approach had previously participated in the long-term neurorehabilitation study carried out using the Walk Again Project Neurorehabilitation (WANR) protocol. As reported in a recent publication from the same team (Shokur et al., PLoS One, Nov. 2018), all seven patients who participated in that protocol for a period of 28 months improved their clinical status, from complete paraplegia (AIS A or B, meaning no motor functions below the level of the injury, according to the ASIA classification) to partial paraplegia (AIS C, meaning partial recovery of sensory and motor function below the injury level). This significant neurological recovery included major clinical improvements in sensory discrimination (tactile, nociception, vibration, and pressure), voluntary motor control of abdomen and leg muscles, and important gains in autonomic control, such as bladder, bowel, and sexual functions.

“The last two studies published by the Walk Again Project clearly indicate that partial neurological and functional recovery can be induced in chronic spinal cord injury patients by combining multiple non-invasive technologies that are based around the concept of using a brain-machine interface to control different types of actuators, like virtual avatars, robotic walkers, or muscle stimulating devices, to allow the total involvement of patients in their own rehabilitation routine,” said Miguel Nicolelis, scientific director of the Walk Again Project and one of the authors of the study.

In a recent report by another group, one AIS C and two AIS D patients were able to walk thanks to the employment of an invasive method for spinal cord electrical stimulation, which required a spinal surgical procedure. In contrast, in the present study two AIS C patients – which originally were AIS A (see Supplemental Material below)- and a third AIS B subject, who recently achieved similar results, were able to regain a significant degree of autonomous walking without the need for such invasive treatments. Instead, these patients only received electrical stimulation patterns delivered to the skin surface of their legs, so that a total of eight muscles in each limb could be electrically stimulated in a physiologically accurate sequence. This was done in order to produce a smoother and more natural pattern of locomotion.

“Crucial for this implementation was the development of a closed-loop controller that allowed real-time correction of the patients’ walking pattern, taking into account muscle fatigue and external perturbations, in order to produce a predefined gait trajectory. Another major component of our approach was the use of a wearable haptic display to deliver tactile feedback to the patients´ forearms in order to provide them with a continuous source of proprioceptive feedback related to their walking,” said Solaiman Shokur.

To control the pattern of electrical muscle stimulation in each leg, these patients utilized an EEG-based brain-machine interface. In this setup, patients learned to alternate the generation of “stepping motor imagery” activity in their right and left motor cortices, in order to create alternated movements of their left and right legs.

According to the authors, the patients exhibited not only “less dependency on walking assistance, but also partial neurological recovery, with substantial rates of motor improvement in one of them.” The improvement in motor control in this last AIS C patient was 9 points in the lower extremity motor score (LEMS), which was comparable with that observed using invasive spinal cord stimulation.

Based on the results obtained over the past 5 years, the WAP now intends to combine all its neurorehabilitation tools into a single integrated, non-invasive platform to treat spinal cord injury patients. This platform will allow patients to begin training soon after the injury occurs. It will also allow the employment of a multi-dimensional integrated brain-machine interface capable of simultaneously controlling virtual and robotic actuators (like a lowerlimb exoskeleton), a multi-channel non-invasive electrical muscle stimulation system (like the FES used in the present study), and a novel non-invasive spinal cord stimulation approach. In this final configuration, this WAP platform will incorporate all these technologies together in order to maximize neurological and functional recovery in the shortest possible time, without the need of any invasive procedure.

According to Dr. Nicolelis, “there is no silver bullet to treat spinal cord injuries. More and more, it looks like we need to implement multiple techniques simultaneously to achieve the best neurorehabilitation results. In this context, it is also imperative to consider the occurrence of cortical plasticity as a major component in the planning of our rehabilitation approach.”


The other authors of this paper are Aurelie Selfslagh, Debora S.F. Campos, Ana R. C. Donati, Sabrina Almeida, Seidi Y. Yamauti, Daniel B. Coelho and Mohamed Bouri. This project was developed through a collaboration between the Neurorehabilitation Laboratory of the Associação Alberto Santos Dumont para Apoio à Pesquisa (AASDAP), the headquarters of the Walk Again Project, the Biomechanics and Motor Control Laboratory at the Federal University of ABC (UFABC), and the Laboratory of Robotic System at the Swiss Institute of Technology of Lausanne (EPFL). It was funded by a grant from the Brazilian Financing Agency for Studies and Projects (FINEP) 01.12.0514.00, Ministry of Science, Technology, Innovation and Communications (MCTIC), to AASDAP.

Supplemental Material:

Supporting Research Studies:


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[Abstract] The Work Disability Functional Assessment Battery (WD-FAB) – Physical Medicine and Rehabilitation Clinics


Accuracy in measuring function related to one’s ability to work is central to public confidence in a work disability benefits system. In the United States, national disability programs are challenged to adjudicate millions of work disability claims each year in a timely and accurate manner. The Work Disability Functional Assessment Battery (WD-FAB) was developed to provide work disability agencies and other interested parties a comprehensive and efficient approach to profiling a person’s function related to their ability to work. The WD-FAB is grounded by the International Classification of Functioning, Disability, and Health conceptual framework.


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[REVIEW] Strategies to implement and monitor in-home transcranial electrical stimulation in neurological and psychiatric patient populations: a systematic review – Full Text



Transcranial electrical stimulation is a promising technique to facilitate behavioural improvements in neurological and psychiatric populations. Recently there has been interest in remote delivery of stimulation within a participant’s home.


The purpose of this review is to identify strategies employed to implement and monitor in-home stimulation and identify whether these approaches are associated with protocol adherence, adverse events and patient perspectives.


MEDLINE, Embase Classic + Embase, Emcare and PsycINFO databases and clinical trial registries were searched to identify studies which reported primary data for any type of transcranial electrical stimulation applied as a home-based treatment.


Nineteen published studies from unique trials and ten on-going trials were included. For published data, internal validity was assessed with the Cochrane risk of bias assessment tool with most studies exhibiting a high level of bias possibly reflecting the preliminary nature of current work. Several different strategies were employed to prepare the participant, deliver and monitor the in-home transcranial electrical stimulation. The use of real time videoconferencing to monitor in-home transcranial electrical stimulation appeared to be associated with higher levels of compliance with the stimulation protocol and greater participant satisfaction. There were no severe adverse events associated with in-home stimulation.


Delivery of transcranial electrical stimulation within a person’s home offers many potential benefits and appears acceptable and safe provided appropriate preparation and monitoring is provided. Future in-home transcranial electrical stimulation studies should use real-time videoconferencing as one of the approaches to facilitate delivery of this potentially beneficial treatment.


Transcranial electrical stimulation (tES) is a technique used to modulate cortical function and human behaviour. It involves weak current passing through the scalp via surface electrodes to stimulate the underlying brain. A common type of tES is transcranial direct current stimulation (tDCS). Several studies have demonstrated tDCS is capable of modulating cortical function, depending on the direction of current flow [123]. When the anode is positioned over a cortical region, the current causes depolarisation of the neuronal cells, increasing spontaneous firing rates [4]. Conversely, positioning the cathode over the target cortical region causes hyperpolarisation and a decrease in spontaneous firing rates [4]. This modulation of cortical activity can be observed beyond the period of stimulation and is thought to be mediated by mechanisms which resemble long term potentiation and depression [5]. Along similar lines, transcranial alternating current stimulation (tACS) and transcranial random noise stimulation (tRNS) are also forms of tES. Both tACS and tRNS are thought to interact with ongoing oscillatory cortical rhythms in a frequency dependent manner to influence human behaviour [678].

The ability of tES to selectively modulate cortical activity offers a promising tool to induce behavioural change. Indeed, several studies have demonstrated that tES may be a favourable approach to reduce impairment following stroke [9], improve symptoms of neglect [10], or reduce symptoms of depression [11]. While these results appear promising, there remains debate around technical aspects of stimulation along with individual participant characteristics that may influence the reliability of a stimulation response [1213141516171819202122]. However, current evidence does suggest that effects of stimulation may be cumulative, with greater behavioural improvements observed following repeated stimulation sessions [20]. Furthermore, tES has shown potential as a tool for maintenance stimulation, with potential relapses of depression managed by stimulation which continued over several months [2324]. Therefore, it may be that repeated stimulation sessions will become a hallmark of future clinical and research trials aiming to improve behavioural outcomes. This would require participants to attend frequent treatment sessions applied over a number of days, months or years. Given that many participants who are likely to benefit from stimulation are those with higher levels of motor or cognitive impairment, the requirement to travel regularly for treatment may present a barrier, limiting potential clinical utility or ability to recruit suitable research participants [25]. In addition, regular daily treatments would also hinder those who travel from remote destinations to receive this potentially beneficial neuromodulation. Therefore, there is a requirement to consider approaches to safely and effectively deliver stimulation away from the traditional locations of research departments or clinical facilities.

One benefit of tES over other forms of non-invasive brain stimulation, such as repetitive transcranial magnetic stimulation, is the ability to easily transport the required equipment. This opportunity may allow for stimulation to be delivered in a participant’s home, which could represent the mode of delivery for future clinical applications. However, it may be unreasonable to expect that a participant would be capable of managing delivery of tES alone and would likely require some form of training and/or monitoring [25]. Although tES is considered relatively safe [26], stimulation should be delivered within established guidelines to avoid adverse events [27]. Inappropriate delivery of stimulation could result in neural damage, detrimental behavioural effects, irritation, burns or lesions of the skin [282930313233]. Therefore, in order to deliver stimulation safely to the appropriate cortical region, it is likely that in-home stimulation may require some form of monitoring [25].

It is currently unclear what the best approach is to implement and monitor in-home tES. An early paper proposed several guidelines to perform in home tES [34]. However, these guidelines were not based on evidence from published clinical trials as there were none available at the time of publication. One recent systematic review sought to discuss current work in this area and highlighted the need for further research to investigate safety, technical monitoring and assessment of efficacy [35]. Given the recent, and growing, interest in home-based brain stimulation, we felt it was now pertinent to conduct a review to specifically identify strategies employed to implement and monitor the use of in-home tES in neurological and psychiatric populations. The secondary questions were to report protocol adherence, adverse events and patient perspectives of in-home tES. Understanding optimal treatment fidelity for in-home brain stimulation will be instrumental to achieving higher levels of tES useability and acceptance within a participant’s home.[…]


via Strategies to implement and monitor in-home transcranial electrical stimulation in neurological and psychiatric patient populations: a systematic review | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 2 Cochrane risk of bias tool was used to assess quality of included studies

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[ARTICLE] Benefits and challenges with gamified multi-media physiotherapy case studies: a mixed method study – Full Text



The use of gamification in higher education context has become popular in recent years with one aim of enhancing learning motivation, yet, it is unknown how physiotherapy students perceive gamified education experience. Using gamification together with multi-media patient case studies, this study explored whether and how gamified education motivated physiotherapy students’ learning. It also investigated how other factors such as class design and mechanics affected gamified experience.


Six case studies in the subject Neurological Physiotherapy were transformed from paper-based cases to multi-media cases built by iSpring suite 8.1. Simulated, real or animated clients were used. Gamification mechanics such as leaderboards, scoring and prioritisation were embedded in the case studies. These gamified case studies were used in classes with Year-3 students enrolled in this subject. After taking these classes, 10 students participated in two focus groups and 32 students responded to a survey to share their experiences and perceptions on this pedagogy.


Results showed that students perceived gamified education as motivating since this satisfied their competence and social needs and enhanced their self-efficacy. In addition, authentic patient videos, class activities that allowed conflict resolution and reflection, and the use of leaderboards were enablers in this gamified experience.


Future gamified education in physiotherapy can provide authentic experience through class designs and gamification mechanics to foster learning motivation. A suggested mapping of gamified lessons for physiotherapy education is provided based on the results of this study.


Learning is an inherently human activity that involves many complex active and interactive processes. Motivation appears to be a key driver to both initial and ongoing learning, as well as improved learning outcomes [1234]. Gamification, or the use of game elements in non-game contexts [5], promotes achievement, challenge, goal, competition and collaboration to learning [6], which in turn motivates learners [7]. Gamification is thought to enhance motivation and engagement through three levels of processes: cognitive, psychological/emotional and social [89]. At the cognitive level, learners experience processes such as problem-solving and decision-making [10]. At the psychological/emotional level, learners’ positive emotions (e.g. feeling competent) with certain experiences would wire into their memories to enhance further learning of similar experiences [1112]. At the social level, interactions with other learners facilitate knowledge constructions [13]. Gamified education should be structured to promote these processes.

To promote the aforementioned processes, better conceptualisations of gamification are needed. Gamification mechanics are often classified by reward or process-tracking types; namely leaderboards, badges, points (or scores), feedback and prizes [79]. Some educational gamification systems use one type of mechanics while others use a mix-and-match approach. Pedersen and Poulsen [9] found that feedback and points showed an increase in positive outcomes in terms of learning motivations, while other mechanics warrant further investigations. In addition, it is important to differentiate between game-based learning, gamification and serious game. Game-based learning is the use of games (digital or non-digital) as learning tools [14], while gamification does not necessarily include a game but embed game elements (such as competitions) in learning tasks [5]. The term serious game is sometimes used interchangeably with game-based learning as it applies to any game with a purpose other than pleasure; here learning fits into this rationale [81516]. The focus of this study is on gamification rather than game-based learning and serious game.

Gamification has been applied across different disciplines in higher education, such as computer science, mathematics, language and health education [171819]. Currently, there is a lack of literature describing or studying gamification in physiotherapy education. In a recent systematic review on gamification in health care education by Wang, DeMaria [20], only two out of 48 reviewed studies included physiotherapy students as participants. This paucity warrants investigation in the use of gamification in physiotherapy education given its reported benefits on learning.[…]


Continue —>  Benefits and challenges with gamified multi-media physiotherapy case studies: a mixed method study | Archives of Physiotherapy | Full Text

Fig. 1 Title pages of the three gamified virtual patient cases


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[Abstract] Cognitive rehabilitation post traumatic brain injury: A systematic review for emerging use of virtual reality technology


  • Virtual reality technology improves cognitive function post-traumatic brain injury.
  • Optimal treatment protocol is; 10–12 sessions, 20–40 min in duration with 2–4 sessions per week.
  • There was weak evidence for positive effect of virtual reality on attention.



Traumatic brain injury (TBI) can causes numerous cognitive impairments usually in the aspects of problem-solving, executive function, memory, and attention. Several studies has suggested that rehabilitation treatment interventions can be effective in treating cognitive symptoms of brain injury. Virtual reality (VR) technology potential as a useful tool for the assessment and rehabilitation of cognitive processes.


The aims of present systematic review are to examine effects of VR training intervention on cognitive function, and to identify effective VR treatment protocol in patients with TBI.


PubMed, Scopus, PEDro, REHABDATA, EMBASE, web of science, and MEDLINE were searched for studies investigated effect of VR on cognitive functions post TBI. The methodological quality were evaluated using PEDro scale. The results of selected studies were summarized.


Nine studies were included in present study. Four were randomized clinical trials, case studies (n = 3), prospective study (n = 1), and pilot study (n = 1). The scores on the PEDro ranged from 0 to 7 with a mean score of 3. The results showed improvement in various cognitive function aspects such as; memory, executive function, and attention in patients with TBI after VR training.


Using different VR tools with following treatment protocol; 10–12 sessions, 20–40 min in duration with 2–4 sessions per week may improves cognitive function in patients with TBI. There was weak evidence for effects of VR training on attention post TBI.

via Cognitive rehabilitation post traumatic brain injury: A systematic review for emerging use of virtual reality technology – Journal of Clinical Neuroscience

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