Posts Tagged Training

[White Book] White Book on Physical and Rehabilitation Medicine (PRM) in Europe. Chapter 9. Education and continuous professional development: shaping the future of PRM – Full Text PDF

In the context of the White Book of Physical and Rehabilitation Medicine (PRM), this paper deals with the education of PRM physicians in Europe. To acquire the wide field of competence needed, specialists in Physical and Rehabilitation Medicine have to undergo a well organised and appropriately structured training of adequate duration. In fact they are required to develop not only medical knowledge, but also competence in patient care, specific procedural skills, and attitudes towards interpersonal relationship and communication, profound understanding of the main principles of medical ethics and public health, ability to apply policies of care and prevention for disabled people, capacity to master strategies for reintegration of disabled people into society, apply principles of quality assurance and promote a practice-based continuous professional development. This paper provides updated detailed information about the education and training of specialists, delivers recommendations concerning the standards required at a European level, in agreement with the UEMS rules of creating a Common Training Framework, that consists of a common set of knowledge, skills and competencies for postgraduate training. The role of the European PRM Board is highlighted as a body aimed at ensuring the highest standards of medical training and health care across Europe and the harmonization of PRM physicians’ qualifications. To this scope, the theoretical knowledge necessary for the practice of PRM specialty and the core competencies (training outcomes) to be achieved at the end of training have been established and the postgraduate PRM core curriculum has been added. Undergraduate training of medical students is also focused, being considered a mandatory element for the growth of both PRM specialty and the medical community as a whole, mainly in front of the future challenges of the ageing population and the increase of disability in our continent. Finally, the problems of continuing professional development and medical education are faced in a PRM European perspective, and the role of the European Accreditation Council of Continuous Medical Education (EACCME) of UEMS is outlined.

Download Full Text PDF

via White Book on Physical and Rehabilitation Medicine (PRM) in Europe. Chapter 9. Education and continuous professional development: shaping the future of PRM – European Journal of Physical and Rehabilitation Medicine 2018 April;54(2):279-86 – Minerva Medica – Journals


, , , , , , , ,

Leave a comment

[Abstract] Modelling and control of a novel walker robot for post-stroke gait rehabilitation


In this paper, a novel walker robot is proposed for post-stroke gait rehabilitation. It consists of an omni-directional mobile platform which provides high mobility in horizontal motion, a linear motor that moves in vertical direction to support the body weight of a patient and a 6-axis force/torque sensor to measure interaction force/torque between the robot and patient. The proposed novel walker robot improves the mobility of pelvis so it can provide more natural gait patterns in rehabilitation. This paper analytically derives the kinematic and dynamic models of the novel walker robot. Simulation results are given to validate the proposed kinematic and dynamic models.

I. Introduction

Stroke is one of the leading causes of death overall the world [1]. According to a report from the American Heart Association, around 8 million population experience stroke onset every year worldwide [2]. It remains many sequalae including a pathological walking pattern. Impaired walking function refrains stroke survivors from not only activities of daily living but also social participation, which causes poststroke depression in stroke survivors [3]. Unfortunately, the depressed mood also negatively influences on the recovery of daily functions [4]–[6]. Moreover, decreased mobility is associated with other diseases such as obesity which leads to comorbidity then raise the possibility to get recurrent strokes [7], [8]. This might become a vicious circle and form a huge economic burden for governments [9].

via Modelling and control of a novel walker robot for post-stroke gait rehabilitation – IEEE Conference Publication

, , , , , , , , , , , , ,

Leave a comment

[Abstract+References] Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review

Acquired brain injury (ABI) is associated with a range of cognitive and motor deficits, and poses a significant personal, societal, and economic burden. Rehabilitation programs are available that target motor skills or cognitive functioning. In this review, we summarize the existing evidence that training may enhance structural neuroplasticity in patients with ABI, as assessed using structural magnetic resonance imaging (MRI)–based techniques that probe microstructure or morphology. Twenty-five research articles met key inclusion criteria. Most trials measured relevant outcomes and had treatment benefits that would justify the risk of potential harm. The rehabilitation program included a variety of task-oriented movement exercises (such as facilitation therapy, postural control training), neurorehabilitation techniques (such as constraint-induced movement therapy) or computer-assisted training programs (eg, Cogmed program). The reviewed studies describe regional alterations in white matter architecture and/or gray matter volume with training. Only weak-to-moderate correlations were observed between improved behavioral function and structural changes. While structural MRI is a powerful tool for detection of longitudinal structural changes, specific measures about the underlying biological mechanisms are lacking. Continued work in this field may potentially see structural MRI metrics used as biomarkers to help guide treatment at the individual patient level.

1. O’Rance, L, Fortune, N. Disability in Australia: Acquired Brain Injury. Canberra, AustraliaAustralian Institute of Health and Welfare2007:124Google Scholar
2. Rabinowitz, A, Levin, HS. Cognitive sequale of traumatic brain injury. Psychiatr Clin North Am. 2014;37:111Google ScholarCrossrefMedline
3. Kuhtz-Buschbeck, JP, Hoppe, B, Gölge, M, Dreesmann, M, Damm-Stünitz, U, Ritz, A. Sensorimotor recovery in children after traumatic brain injury: analyses of gait, gross motor, and fine motor skills. Dev Med Child Neurol. 2003;45:821828Google ScholarCrossrefMedline
4. Hayes, JP, Bigler, ED, Verfaellie, M. Traumatic brain injury as a disorder of brain connectivity. J Int Neuropsychol Soc. 2016;22:120137. doi:10.1017/S1355617715000740. Google ScholarCrossrefMedline
5. Drijkoningen, D, Caeyenberghs, K, Vander Linden, C, Van Herpe, K, Duysens, J, Swinnen, SP. Associations between muscle strength asymmetry and impairments in gait and posture in young brain-injured patients. J Neurotrauma. 2015;32:13241332. doi:10.1089/neu.2014.3787. Google ScholarCrossrefMedline
6. Nocentini, U, Bozzali, M, Spanò, B. Exploration of the relationships between regional grey matter atrophy and cognition in multiple sclerosis. Brain Imaging Behav. 2014;8:378386. doi:10.1007/s11682-012-9170-7. Google ScholarCrossrefMedline
7. Hulkower, MB, Poliak, DB, Rosenbaum, SB, Zimmerman, ME, Lipton, ML. A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol. 2013;34:20642074. doi:10.3174/ajnr.A3395. Google ScholarCrossrefMedline
8. Caeyenberghs, K, Wenderoth, N, Smits-Engelsman, BC, Sunaert, S, Swinnen, SP. Neural correlates of motor dysfunction in children with traumatic brain injury: exploration of compensatory recruitment patterns. Brain. 2009;132(pt 3):684694Google ScholarCrossrefMedline
9. Chen, H, Epstein, J, Stern, E. Neural plasticity after acquired brain injury: evidence from functional neuroimaging. PM R. 2010;2(12 suppl 2):S306S312. doi:10.1016/j.pmrj.2010.10.006. Google ScholarCrossrefMedline
10. Choo, PL, Gallagher, HL, Morris, J, Pomeroy, VM, van Wijck, F. Correlations between arm motor behavior and brain function following bilateral arm training after stroke: a systematic review. Brain Behav. 2015;5:e00411. doi: 10.1002/brb3.411. Google ScholarCrossrefMedline
11. Matthews, PM, Johansen-Berg, H, Reddy, H. Non-invasive mapping of brain functions and brain recovery: applying lessons from cognitive neuroscience to neurorehabilitation. Restor Neurol Neurosci. 2004;22:245260Google ScholarMedline
12. Prosperini, L, Piattella, MC, Giannì, C, Pantano, P. Functional and structural brain plasticity enhanced by motor and cognitive rehabilitation in multiple sclerosis. Neural Plast. 2015;2015:481574. doi:10.1155/2015/481574. Google ScholarCrossrefMedline
13. Reid, LB, Boyd, RN, Cunnington, R, Rose, SE. Interpreting intervention induced neuroplasticity with fMRI: the case for multimodal imaging strategies. Neural Plast. 2016;2016:2643491. doi:10.1155/2016/2643491.Google ScholarCrossrefMedline
14. Richards, LG, Stewart, KC, Woodbury, ML, Senesac, C, Cauraugh, JH. Movement-dependent stroke recovery: a systematic review and meta-analysis of TMS and fMRI evidence. Neuropsychologia. 2008;46:311Google ScholarCrossrefMedline
15. Mechelli, A, Crinion, JT, Noppeney, U. Neurolinguistics: structural plasticity in the bilingual brain. Nature. 2004;431:757. doi:10.1038/431757a. Google ScholarCrossrefMedline
16. Takeuchi, H, Sekiguchi, A, Taki, Y. Training of working memory impacts structural connectivity. J Neurosci. 2010;30:32973303. doi:10.1523/JNEUROSCI.4611-09.2010. Google ScholarCrossrefMedline
17. van Tulder, M, Furlan, A, Bombardier, C, Bouter, L; Editorial Board of the Cochrane Collaboration Back Review Group. Updated method guidelines for systematic reviews in the cochrane collaboration back review group. Spine (Phila Pa 1976). 2003;28:12901299Google ScholarCrossrefMedline
18. Fritz, NE, Cheek, FM, Nichols-Larsen, DS. Motor-cognitive dual-task training in persons with neurologic disorders: a systematic review. J Neurol Phys Ther. 2015;39:142153. doi:10.1097/NPT.0000000000000090. Google ScholarCrossrefMedline
19. Guzmán, J, Esmail, R, Karjalainen, K, Malmivaara, A, Irvin, E, Bombardier, C. Multidisciplinary rehabilitation for chronic low back pain: systematic review. BMJ. 2001;322:15111516Google ScholarCrossrefMedline
20. Karjalainen, K, Malmivaara, A, van Tulder, M. Multidisciplinary biopsychosocial rehabilitation for subacute low back pain in working-age adults: a systematic review within the framework of the Cochrane Collaboration Back Review Group. Spine (Phila Pa 1976). 2001;26:262269Google ScholarCrossrefMedline
21. Gauthier, LV, Taub, E, Perkins, C, Ortmann, M, Mark, VW, Uswatte, G. Remodeling the brain: plastic structural brain changes produced by different motor therapies after stroke. Stroke. 2008;39:15201525. doi:10.1161/STROKEAHA.107.502229. Google ScholarCrossrefMedline
22. Schlaug, G, Marchina, S, Norton, A. Evidence for plasticity in white-matter tracts of patients with chronic Broca’s aphasia undergoing intense intonation-based speech therapy. Ann N Y Acad Sci. 2009;1169:385394. doi:10.1111/j.1749-6632.2009.04587.x. Google ScholarCrossrefMedline
23. Breier, J, Juranek, J, Papanicolaou, A. Changes in maps of language function and the integrity of the arcuate fasciculus after therapy for chronic aphasia. Neurocase. 2011;17:506517. doi:10.1080/13554794.2010.547505. Google ScholarCrossrefMedline
24. Caria, A, Weber, C, Brötz, D. Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology. 2011;48:578582. doi:10.1111/j.1469-8986.2010.01117.x. Google ScholarCrossrefMedline
25. Nordvik, JE, Schanke, AK, Walhovd, K, Fjell, A, Grydeland, H, Landrø, NI. Exploring the relationship between white matter microstructure and working memory functioning following stroke: a single case study of computerized cognitive training. Neurocase. 2012;18:139151. doi:10.1080/13554794.2011.568501.Google ScholarCrossrefMedline
26. Borstad, AL, Bird, T, Choi, S, Goodman, L, Schmalbrock, P, Nichols-Larsen, DS. Sensorimotor training induced neural reorganization after stroke: a case series. J Neurol Phys Ther. 2013;37:2736. doi:10.1097/NPT.0b013e318283de0d. Google ScholarCrossrefMedline
27. Lazaridou, A, Astrakas, L, Mintzopoulos, D. Diffusion tensor and volumetric magnetic resonance Imaging using an MR-compatible hand-induced robotic device suggests training-induced neuroplasticity in patients with chronic stroke. Int J Mol Med. 2013;32:9951000. doi:10.3892/ijmm.2013.1476. Google ScholarCrossrefMedline
28. Särkämö, T, Ripollés, P, Vepsäläinen, H. Structural changes induced by daily music listening in the recovering brain after middle cerebral artery stroke: a voxel-based morphometry study. Front Hum Neurosci. 2014;8:245. doi:10.3389/fnhum.2014.00245. Google ScholarCrossrefMedline
29. Wan, CY, Zheng, X, Marchina, S, Norton, A, Schlaug, G. Intensive therapy induces contralateral white matter changes in chronic stroke patients with Broca’s aphasia. Brain Lang. 2014;136:17. doi:10.1016/j.bandl.2014.03.011. Google ScholarCrossrefMedline
30. Fan, YT, Lin, KC, Liu, HL, Chen, YL, Wu, CY. Changes in structural integrity are correlated with motor and functional recovery after post-stroke rehabilitation. Restor Neurol Neurosci. 2015;33:835844. doi:10.3233/RNN-150523. Google ScholarCrossrefMedline
31. Young, BM, Stamm, JM, Song, J. Brain-computer interface training and stroke affects patterns of brain-behavior relationships in corticospinal motor fibers. Front Hum Neurosci. 2016;10:457Google ScholarCrossrefMedline
32. Wilkins, KB, Owen, M, Ingo, C, Carmona, C, Dewald, J, Yao, J. Neural plasticity in moderate to severe chronic stroke following a device-assisted task-specific arm/hand intervention Front Neurol. 2017;8:284. doi:10.33389/fneur.2017.00284. Google ScholarCrossrefMedline
33. Yang, HE, Kyeong, S, Lee, SH. Structural and functional improvements due to robot-assisted gait training in the stroke-injured brain. Neurosci Lett. 2017;637:114119Google ScholarCrossrefMedline
34. Ibrahim, I, Tintera, J, Skoch, A. Fractional anisotropy and mean diffusivity in the corpus callosum of patients with multiple sclerosis: the effect of physiotherapy. Neuroradiology. 2011;53:917926. doi:10.1007/s00234-011-0879-6. Google ScholarCrossrefMedline
35. Filippi, M, Riccitelli, G, Mattioli, F. Multiple sclerosis: effects of cognitive rehabilitation on structural and functional MR imaging measures—an explorative study. Radiology. 2012;262:932940. doi:10.1148/radiol.11111299. Google ScholarCrossrefMedline
36. Bonzano, L, Tacchino, A, Brichetto, G. Upper limb motor rehabilitation impacts white matter microstructure in multiple sclerosis. Neuroimage. 2014;90:107116. doi:10.1016/j.neuroimage.2013.12.025. Google ScholarCrossrefMedline
37. Prosperini, L, Fanelli, F, Petsas, N. Multiple sclerosis: changes in microarchitecture of white matter tracts after training with a video game balance board. Radiology. 2014;273:529538. doi:10.1148/radiol.14140168. Google ScholarCrossrefMedline
38. Rasova, K, Prochazkova, M, Tintera, J, Ibrahim, I, Zimova, D, Stetkarova, I. Motor programme activating therapy influences adaptive brain functions in multiple sclerosis: clinical and MRI study. Int J Rehabil Res. 2015;38:4954. doi:10.1097/MRR.0000000000000090. Google ScholarCrossrefMedline
39. Ernst, A, Sourty, M, Roquet, D. Functional and structural cerebral changes in key brain regions after facilitation programme for episodic future thought in relapsing-remitting multiple sclerosis patients. Brain Cogn. 2016;105:3445Google ScholarCrossrefMedline
40. Cruickshank, TM, Thompson, JA, Domínguez, D. The effect of multidisciplinary rehabilitation on brain structure and cognition in Huntington’s disease: an exploratory study. Brain Behav. 2015;5:e00312. doi:10.1002/brb3.312. Google ScholarCrossrefMedline
41. Metzler-Baddeley, C, Cantera, J, Coulthard, E, Rosser, A, Jones, DK, Baddeley, RJ. Improved executive function and callosal white matter microstructure after rhythm exercise in Huntington’s disease. J Huntingtons Dis. 2014;3:273283. doi:10.3233/JHD-140113. Google ScholarCrossrefMedline
42. Sehm, B, Taubert, M, Conde, V. Structural brain plasticity in Parkinson’s disease induced by balance training. Neurobiol Aging. 2014;35:232239. doi:10.1016/j.neurobiolaging.2013.06.021. Google ScholarCrossrefMedline
43. Díez-Cirarda, M, Ojeda, N, Peña, J. Increased brain connectivity and activation after cognitive rehabilitation in Parkinson’s disease: a randomized controlled trial. Brain Imaging Behav. 2017;11:16401651Google ScholarCrossrefMedline
44. Burciu, RG, Fritsche, N, Granert, O. Brain changes associated with postural training in patients with cerebellar degeneration: a voxel-based morphometry study. J Neurosci. 2013;33:45944604. doi:10.1523/JNEUROSCI.3381-12.2013. Google ScholarCrossrefMedline
45. Han, K, Davis, RA, Chapman, SB, Krawczyk, DC. Strategy-based reasoning training modulates cortical thickness and resting-state functional connectivity in adults with chronic traumatic brain injury. Brain Behav. 2017;7:e00687. doi:10.1002/brb3.687. Google ScholarCrossrefMedline
46. Cohen, J. Statistical Power Analysis for the Behavioral Sciences. Hilsdale, NJLawrence Earlbaum1988Google Scholar
47. Borenstein, M, Hedges, LV, Higgins, JPT, Rothstein, HR. Multiple outcomes or time-points within a study. In: Borenstein, M, ed. Introduction to Meta-Analysis. Chichester, EnglandWiley2009:225238Google ScholarCrossref
48. Thomas, C, Baker, CI. Teaching an adult brain new tricks: a critical review of evidence for training-dependent structural plasticity in humans. Neuroimage. 2013;73:225236. doi:10.1016/j.neuroimage.2012.03.069. Google ScholarCrossrefMedline
49. Smith, S, Rao, A, De Stefano, N. Longitudinal and cross-sectional analysis of atrophy in Alzheimer’s disease: cross-validation of BSI, SIENA and SIENAX. Neuroimage. 2007;36:12001206. doi:10.1016/j.neuroimage.2007.04.035. Google ScholarCrossrefMedline
50. Heiervang, E, Behrens, TE, Mackay, CE, Robson, MD, Johansen-Berg, H. Between session reproducibility and between subject variability of diffusion MR and tractography measures. Neuroimage. 2006;33:867877. doi:10.1016/j.neuroimage.2006.07.037. Google ScholarCrossrefMedline
51. Wakana, S, Caprihan, A, Panzenboeck, MM. Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage. 2007;36:630644. doi:10.1016/j.neuroimage.2007.02.049. Google ScholarCrossrefMedline
52. Scholz, J, Klein, MC, Behrens, TE, Johansen-Berg, H. Training induces changes in white-matter architecture. Nat Neurosci. 2009;12:13701371. doi:10.1038/nn.2412. Google ScholarCrossrefMedline
53. Taubert, M, Draganski, B, Anwander, A. Dynamic properties of human brain structure: learning-related changes in cortical areas and associated fiber connections. J Neurosci. 2010;30:1167011677. doi:10.1523/JNEUROSCI.2567-10.2010. Google ScholarCrossrefMedline
54. Hofstetter, S, Tavor, I, Tzur Moryosef, S, Assaf, Y. Short-term learning induces white matter plasticity in the fornix. J Neurosci. 2013;33:1284411280. doi:10.1523/JNEUROSCI.4520-12.2013. Google ScholarCrossrefMedline
55. Cercignani, M, Bammer, R, Sormani, MP, Fazekas, F, Filippi, M. Inter-sequence and inter-imaging unit variability of diffusion tensor MR imaging histogram-derived metrics of the brain in healthy volunteers. AJNR Am J Neuroradiol. 2003;24:638643Google ScholarMedline
56. Price, R, Axel, L, Morgan, T. Quality assurance methods and phantoms for magnetic resonance imaging: report of AAPM Nuclear Magnetic Resonance Task Group No. 1. Med Phys. 1990;17:287295. doi:10.1118/1.596566. Google ScholarCrossrefMedline
57. Bookstein, FL. “Voxel-based morphometry” should not be used with imperfectly registered images. Neuroimage. 2001;14:14541462Google ScholarCrossrefMedline
58. Thompson, WK, Holland, D; Alzheimer’s Disease Neuroimaging Initiative. Bias in tensor based morphometry Stat-ROI measures may result in unrealistic power estimates. Neuroimage. 2011;57:14. doi:10.1016/j.neuroimage.2010.11.092. Google ScholarCrossrefMedline
59. Zatorre, RJ, Fields, RD, Johansen-Berg, H. Plasticity in gray and white: neuroimaging changes in brain structure during learning. Nat Neurosci. 2012;15:528536. doi:10.1038/nn.3045. Google ScholarCrossrefMedline
60. Eriksson, S, Free, S, Thom, M. Quantitative grey matter histological measures do not correlate with grey matter probability values from in vivo MRI in the temporal lobe. J Neurosci Methods. 2009;181:111118. doi:10.1016/j.jneumeth.2009.05.001. Google ScholarCrossrefMedline
61. Jones, DK, Knösche, TR, Turner, R. White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage. 2011;73:239254. doi:10.1016/j.neuroimage.2012.06.081. Google ScholarCrossref
62. Jeurissen, B, Leemans, A, Jones, DK, Tournier, JD, Sijbers, J. Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Hum Brain Mapp. 2011;32:461479. doi:10.1002/hbm.21032. Google ScholarCrossrefMedline
63. Caeyenberghs, K, Metzler-Baddeley, C, Foley, S, Jones, DK. Dynamics of the human structural connectome underlying working memory training. J Neurosci. 2016;36:40564066. doi:10.1523/jneurosci.1973-15.2016. Google ScholarCrossrefMedline
64. Metzler-Baddeley, C, Foley, S, de Santis, S. Dynamics of white matter plasticity underlying working memory training: multimodal evidence from diffusion MRI and relaxometry. J Cogn Neurosci. 2017;29:15091520. doi:10.1162/jocn_a_01127. Google ScholarCrossrefMedline
65. Assaf, Y, Basser, PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage. 2005;27:4858. doi:10.1016/j.neuroimage.2005.03.042. Google ScholarCrossrefMedline
66. Deoni, SC, Rutt, BK, Jones, DK. Investigating exchange and multicomponent relaxation in fully-balanced steady-state free precession imaging. J Magn Reson Imaging. 2008;27:14211429. doi:10.1002/jmri.21079. Google ScholarCrossrefMedline

via Evidence for Training-Dependent Structural Neuroplasticity in Brain-Injured Patients: A Critical Review – Karen Caeyenberghs, Adam Clemente, Phoebe Imms, Gary Egan, Darren R. Hocking, Alexander Leemans, Claudia Metzler-Baddeley, Derek K. Jones, Peter H. Wilson, 2018

, , , ,

Leave a comment

[Abstract+References] SMART Arm Training With Outcome-Triggered Electrical Stimulation in Subacute Stroke Survivors With Severe Arm Disability: A Randomized Controlled Trial.

Background. Stroke survivors with severe upper limb disability need opportunities to engage in task-oriented practice to achieve meaningful recovery. Objective. To compare the effect of SMART Arm training, with or without outcome-triggered electrical stimulation to usual therapy, on arm function for stroke survivors with severe upper limb disability undergoing inpatient rehabilitation. Methods. A prospective, multicenter, randomized controlled trial was conducted with 3 parallel groups, concealed allocation, assessor blinding and intention-to-treat analysis. Fifty inpatients within 4 months of stroke with severe upper limb disability were randomly allocated to 60 min/d, 5 days a week for 4 weeks of (1) SMART Arm with outcome-triggered electrical stimulation and usual therapy, (2) SMART Arm alone and usual therapy, or (3) usual therapy. Assessment occurred at baseline (0 weeks), posttraining (4 weeks), and follow-up (26 and 52 weeks). The primary outcome measure was Motor Assessment Scale item 6 (MAS6) at posttraining. Results. All groups demonstrated a statistically (P < .001) and clinically significant improvement in arm function at posttraining (MAS6 change ≥1 point) and at 52 weeks (MAS6 change ≥2 points). There were no differences in improvement in arm function between groups (P= .367). There were greater odds of a higher MAS6 score in SMART Arm groups as compared with usual therapy alone posttraining (SMART Arm stimulation generalized odds ratio [GenOR] = 1.47, 95%CI = 1.23-1.71) and at 26 weeks (SMART Arm alone GenOR = 1.31, 95% CI = 1.05-1.57). Conclusion. SMART Arm training supported a clinically significant improvement in arm function, which was similar to usual therapy. All groups maintained gains at 12 months.

1. Houwink, A, Nijland, RH, Geurts, AC, Kwakkel, G. Functional recovery of the paretic upper limb after stroke: who regains hand capacity? Arch Phys Med Rehabil. 2013;94:839844Google ScholarCrossrefMedline
2. Broeks, JG, Lankhorst, GJ, Rumping, K, Prevo, AJ. The long-term outcome of arm function after stroke: results of a follow-up study. Disabil Rehabil. 1999;21:357364Google ScholarCrossrefMedline
3. Wolf, SL, Kwakkel, G, Bayley, M, McDonnell, MN. Upper Extremity Stroke Algorithm Working Group. Best practice for arm recovery post stroke: an international application. Physiotherapy. 2016;102:14Google ScholarCrossrefMedline
4. Kleim, JA. Neural plasticity and neurorehabilitation: teaching the new brain old tricks. J Commun Disord. 2011;44:521528Google ScholarCrossrefMedline
5. Pollock, A, Farmer, SE, Brady, MC. Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev. 2014;(11):CD010820Google ScholarMedline
6. Zondervan, DK, Augsburger, R, Bodenhoefer, B, Friedman, N, Reinkensmeyer, DJ, Cramer, SC. Machine-based, self-guided home therapy for individuals with severe arm impairment after stroke:a randomized controlled trial. Neurorehabil Neural Repair. 2015;29:395406Google ScholarLink
7. Howlett, OA, Lannin, NA, Ada, L, McKinstry, C. Functional electrical stimulation improves activity after stroke: a systematic review with meta-analysis. Arch Phys Med Rehabil. 2015;96:934943Google ScholarCrossrefMedline
8. McCabe, J, Monkiewicz, M, Holcomb, J, Pundik, S, Daly, JJ. Comparison of robotics, functional electrical stimulation, and motor learning methods for treatment of persistent upper extremity dysfunction after stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2015;96:981990Google ScholarCrossrefMedline
9. Barker, RN, Brauer, SG, Carson, RG. Training of reaching in stroke survivors with severe and chronic upper limb paresis using a novel nonrobotic device: a randomized clinical trial. Stroke. 2008;39:18001807Google ScholarCrossrefMedline
10. Hayward, KS, Barker, RN, Brauer, SG, Lloyd, D, Horsley, SA, Carson, RG. SMART Arm with outcome-triggered electrical stimulation: a pilot randomized clinical trial. Top Stroke Rehabil. 2013;20:289298Google ScholarCrossrefMedline
11. Barker, RN, Brauer, S, Carson, R. Training-induced changes in the pattern of triceps to biceps activation during reaching tasks after chronic and severe stroke. Exp Brain Res. 2009;196:483496Google ScholarCrossrefMedline
12. Barker, RN, Brauer, SG, Barry, BK, Gill, TJ, Carson, RG. Training-induced modifications of corticospinal reactivity in severely affected stroke survivors. Exp Brain Res. 2012;221:211221Google ScholarCrossrefMedline
13. Zeiler, SR, Krakauer, JW. The interaction between training and plasticity in the post-stroke brain. Curr Opin Neurol. 2013;26:609616Google ScholarCrossrefMedline
14. Hayward, KS, Neibling, BA, Barker, RN. Self-administered, home-based SMART (Sensorimotor Active Rehabilitation Training) arm training: a single-case report. Am J Occup Ther. 2015;69:6904210020p1-8. Google ScholarCrossrefMedline
15. Wahl, AS, Schwab, ME. Finding an optimal rehabilitation paradigm after stroke: enhancing fiber growth and training of the brain at the right moment. Front Hum Neurosci. 2014;8:381Google ScholarCrossrefMedline
16. Hatam, SM, Saussez, G, Della Faille, M. Rehabilitation of motor function after stroke: a multiple systematic review focused on techniques to stimulate upper extremity recovery. Front Hum Neurosci. 2016;10:442Google ScholarMedline
17. Murphy, TH, Corbett, D. Plasticity during stroke recovery: from synapse to behaviour. Nat Rev Neurosci. 2009;10:861872Google ScholarCrossrefMedline
18. Kwah, LK, Harvey, LA, Diong, JH, Herbert, RD. Half of the adults who present to hospital with stroke develop at least one contracture within six months: an observational study. J Physiother. 2012;58:4147Google ScholarCrossrefMedline
19. Adey-Wakeling, Z, Arima, H, Crotty, M. Incidence and associations of hemiplegic shoulder pain poststroke: prospective population-based study. Arch Phys Med Rehabil. 2015;96:241.e1247.e1Google ScholarCrossref
20. Kleim, JA. Neural Plasticity: Foundation for Neurorehabilita-tion. Scottsdale, AZTanas2012Google Scholar
21. Brauer, SG, Hayward, KS, Carson, RG, Cresswell, AG, Barker, RN. The efficacy of SMART arm training early after stroke for stroke survivors with severe upper limb disability: a protocol for a randomised controlled trial. BMC Neurol. 2013;13:71Google ScholarCrossrefMedline
22. Malouin, F, Pichard, L, Bonneau, C, Durand, A, Corriveau, D. Evaluating motor recovery early after stroke: comparison of the Fugl-Meyer Assessment and the Motor Assessment Scale. Arch Phys Med Rehabil. 1994;75:12061212Google ScholarCrossrefMedline
23. Hayward, KS, Kuys, SS, Barker, RN, Brauer, SG. Can stroke survivors with severe upper arm disability achieve clinically important change in arm function during inpatient rehabilitation? A multicentre, prospective, observational study. NeuroRehabilitation. 2014;35:1723Google ScholarMedline
24. Bohannon, RW, Smith, MB. Inter-rater reliability of a modified Ashworth scale of muscle spasticity. Phys Ther. 1987;67:206207Google ScholarCrossrefMedline
25. Ritchie, DM, Boyle, JA, McInnes, JM. Clinical studies with an articular index for the assessment of joint tenderness in patients with rheumatoid arthritis. Q J Med. 1968;37:393406Google ScholarMedline
26. Khan, A, Chien, CW, Brauer, SG. Rasch-based scoring offered more precision in differentiating patient groups in measuring upper limb function. J Clin Epidemiol. 2013;66:681687Google ScholarCrossrefMedline
27. Duncan, PW, Wallace, D, Lai, SM, Johnson, D, Emberston, S, Laster, LJ. The Stroke Impact Scale Version 2.0. Evaluation of the reliability, validity and sensitivity to change. Stroke. 1999;30:21312140Google ScholarCrossrefMedline
28. Uswatte, G, Taub, E, Morris, D, Light, K, Thompson, PA. The Motor Activity Log-28: assessing daily use of the hemiparetic arm after stroke. Neurology. 2006;67:11891194Google ScholarCrossrefMedline
29. Helm-Estabrooks, N. Cognitive Linguistic Quick Test. Examiner’s Manual. San Antonio, TXPsychological Corporation, Harcourt Assessment Company2001Google Scholar
30. Lincoln, NB, Jackson, JM, Adams, SA. Reliability and revision of the Nottingham Sensory Assessment for stroke patients. Physiotherapy. 1998:358365Google Scholar
31. Winters, C, Heymans, MW, van Wegen, EE, Kwakkel, G. How to design clinical rehabilitation trials for the upper paretic limb early post stroke? Trials. 2016;17:468Google ScholarCrossrefMedline
32. van der Lee, JH, Wagenaar, RC, Lankhorst, GJ, Vogelaar, TW, Deville, WL, Bouter, LM. Forced use of the upper extremity in chronic stroke patients—results from a single-blind randomized clinical trial. Stroke. 1999;30:23692375Google ScholarCrossrefMedline
33. Hayward, KS, Kuys, SS, Barker, RN, Brauer, SG. Clinically important improvements in motor function are achievable during inpatient rehabilitation by stroke patients with severe motor disability: a prospective observational study. NeuroRehabilitation. 2014;34:773779Google ScholarMedline
34. Churilov, L, Arnup, S, Johns, H. An improved method for simple, assumption-free ordinal analysis of the modified Rankin Scale using generalized odds ratios. Int J Stroke. 2014;9:9991005Google ScholarLink
35. Hayward, KS, Barker, RN, Wiseman, AH, Brauer, SG. Dose and content of training provided to stroke survivors with severe upper limb disability undertaking inpatient rehabilitation: an observational study. Brain Impairment. 2013;14:392405Google ScholarCrossref
36. Shirzad, N, Van der Loos, HFM. Evaluating the user experience of exercising reaching motions with a robot that predicts desired movement difficulty. J Mot Behav. 2016;48:3146Google ScholarCrossrefMedline
37. Bernstein, NA. The Co-ordination and Regulation of Movement. Oxford, EnglandPergamon Press1967Google Scholar
38. Krakauer, JW. Rethinking motor rehabilitation after strokePaper presented at: International Conference on Virtual RehabilitationJune 9-12, 2015Valencia, SpainGoogle Scholar
39. Guadagnoli, MA, Lee, TD. Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. J Mot Behav. 2004;36:212224Google ScholarCrossrefMedline
40. Winters, C, van Wegen, EE, Daffertshofer, A, Kwakkel, G. Generalizability of the proportional recovery model for the upper extremity after an ischemic stroke. Neurorehabil Neural Repair. 2015;29:614622Google ScholarLinkISI
41. Schneider, EJ, Lannin, NA, Ada, L, Schmidt, J. Increasing the amount of usual rehabilitation improves activity after stroke: a systematic review. J Physiother. 2016;62:182187Google ScholarCrossrefMedline
42. Krakauer, JW, Marshall, RS. The proportional recovery rule for stroke revisited. Ann Neurol. 2015;78:845847Google ScholarCrossrefMedline
43. Duncan, PW, Goldstein, LB, Matchar, D, Divine, GW, Feussner, J. Measurement of motor recovery after stroke—outcome assessment and sample-size requirements. Stroke. 1992;23:10841089Google ScholarCrossrefMedline
44. Birkenmeier, RL, Prager, EM, Lang, CE. Translating animal doses of task-specific training to people with chronic stroke in 1-hour therapy sessions: a proof-of-concept study. Neurorehabil Neural Repair. 2010;24:620635Google ScholarLink
45. Lang, CE, Lohse, KR, Birkenmeier, RL. Dose and timing in neurorehabilitation: prescribing motor therapy after stroke. Curr Opin Neurol. 2015;28:549555Google ScholarCrossrefMedline
46. Magill, RA, Anderson, D. Motor Learning and Control: Concepts and Applications. 10th ed. New York, NYMcGraw-Hill2014Google Scholar
47. Lee, JY, Ready, EA, Davis, EN, Doyle, PC. Purposefulness as a critical factor in functioning, disability and health. Clin Rehabil. 2017;31:10051018Google ScholarLink
48. Bernhardt, J, Borschmann, K, Boyd, L. Moving rehabilitation research forward: developing consensus statements for rehabilitation and recovery research. Int J Stroke. 2016;11:454458Google ScholarLinkISI
49. Hayward, KS. It is time to redefine recovery for individuals with severe upper limb impairment after stroke. IJTR Int J Ther Rehabil. 2016;23:256257Google ScholarCrossref
50. Krebs, HI, Hogan, N. Therapeutic robotics: a technology push. Stroke rehabilitation is being aided by robots that guide movement of shoulders and elbows, wrists, hands, arms and ankles to significantly improve recovery of patients. Proc IEEE Inst Electr Electron Eng. 2006;94:17271738Google ScholarCrossrefMedline
51. de Kroon, JR, van der Lee, JH, Ijzerman, MJ, Lankhorst, GJ. Therapeutic electrical stimulation to improve motor control and functional abilities of the upper extremity after stroke: a systematic review. Clin Rehabil. 2002;16:350360Google ScholarLink
52. Cauraugh, J, Light, K, Kim, S, Thigpen, M, Behrman, A. Chronic motor dysfunction after stroke: recovering wrist and finger extension by electromyography-triggered neuromuscular stimulation. Stroke. 2000;31:13601364Google ScholarCrossrefMedline
53. Veerbeek, JM, Langbroek-Amersfoort, AC, van Wegen, EE, Meskers, CG, Kwakkel, G. Effects of robot-assisted therapy for the upper limb after stroke. Neurorehabil Neural Repair. 2017;31:107121Google ScholarLink
54. Nascimento, LR, Michaelsen, SM, Ada, L, Polese, JC, Teixeira-Salmela, LF. Cyclical electrical stimulation increases strength and improves activity after stroke: a systematic review. J Physiother. 2014;60:2230Google ScholarCrossrefMedline
55. Kwakkel, G, Kollen, BJ, van der Grond, J, Prevo, AJ. Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. Stroke. 2003;34:21812186Google ScholarCrossrefMedline
56. Dromerick, AW, Edwardson, MA, Edwards, DF. Critical periods after stroke study: translating animal stroke recovery experiments into a clinical trial. Front Hum Neurosci. 2015;9:231Google ScholarCrossrefMedline

via SMART Arm Training With Outcome-Triggered Electrical Stimulation in Subacute Stroke Survivors With Severe Arm Disability: A Randomized Controlled TrialNeurorehabilitation and Neural Repair – Ruth N. Barker, Kathryn S. Hayward, Richard G. Carson, David Lloyd, Sandra G. Brauer, 2017

, , , , , , , , , ,

Leave a comment

[Abstract] Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton


The applications of robotics to the rehabilitation training of neuromuscular impairments have received increasing attention due to their promising prospects. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy program. This paper presents an upper extremity exoskeleton for the functional recovery training of disabled patients. A minimal-intervention-based admittance control strategy is developed to induce the active participation of patients and maximize the use of recovered motor functions during training. The proposed control strategy can transit among three control modes, including human-conduct mode, robot-assist mode, and motion-restricted mode, based on the real-time position tracking errors of the end-effector. The human-robot interaction in different working areas can be modulated according to the motion intention of patient. Graphical guidance developed in Unity-3-D environment is introduced to provide visual training instructions. Furthermore, to improve training performance, the controller parameters should be adjusted in accordance with the hemiplegia degree of patients. For the patients with severe paralysis, robotic assistance should be increased to guarantee the accomplishment of training. For the patients recovering parts of motor functions, robotic assistance should be reduced to enhance the training intensity of effected limb and improve therapeutic effectiveness. The feasibility and effectiveness of the proposed control scheme are validated via training experiments with two healthy subjects and six stroke patients with different degrees of hemiplegia.

via Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton – IEEE Journals & Magazine

, , , , , , , , ,

Leave a comment

[Abstract] EEG-guided robotic mirror therapy system for lower limb rehabilitation – IEEE Conference Publication


Lower extremity function recovery is one of the most important goals in stroke rehabilitation. Many paradigms and technologies have been introduced for the lower limb rehabilitation over the past decades, but their outcomes indicate a need to develop a complementary approach. One attempt to accomplish a better functional recovery is to combine bottom-up and top-down approaches by means of brain-computer interfaces (BCIs). In this study, a BCI-controlled robotic mirror therapy system is proposed for lower limb recovery following stroke. An experimental paradigm including four states is introduced to combine robotic training (bottom-up) and mirror therapy (top-down) approaches. A BCI system is presented to classify the electroencephalography (EEG) evidence. In addition, a probabilistic model is presented to assist patients in transition across the experiment states based on their intent. To demonstrate the feasibility of the system, both offline and online analyses are performed for five healthy subjects. The experiment results show a promising performance for the system, with average accuracy of 94% in offline and 75% in online sessions.

Source: EEG-guided robotic mirror therapy system for lower limb rehabilitation – IEEE Conference Publication

, , , , , , ,

Leave a comment

[BLOG POST] Strength training improves the nervous system’s ability to drive muscles

Imagine that the New Year has just begun. You’ve made a resolution to improve your physical fitness. In particular, you want to improve your muscle strength. You’ve heard that people with stronger muscles live longer and have less difficulty standing, walking, and using the toilet when they get older (Rantanen et al. 1999; Ruiz et al. 2008). So, you join a fitness centre and hire a personal trainer. The trainer assesses your maximal strength, and then guides you through a 4-week program that involves lifting weights which are about 80% of your maximum.

Sure enough, after the program, you become stronger (probably around 20% stronger) (Carroll et al. 2011). You think this is great – and it is! You are so excited, you decide to stand in front of your mirror, flex your biceps, and take a selfie (your plan is to post the picture to Facebook to show your friends how much bigger your muscles got). However, after examining the picture, you realise your muscles did not get bigger. Or perhaps they did get a little bigger, but not enough to explain your substantial improvement in strength. You are somewhat disappointed in this, but then you remember your goal was to get stronger, not necessarily bigger, so you post the picture, anyway.

Magnetic stimulation of the brain can be used to test how well a person can voluntarily drive their muscles.

Interestingly, the observations you made are completely consistent with the scientific literature. Within the first weeks of strength training, muscle strength can improve without a change in the size or architecture of the muscle (e.g., Blazevich et al. 2007). Consequently, researchers have speculated that initial improvements in muscle strength from strength training are due primarily to changes in the central nervous system. One hypothesis has been that strength training helps the nervous system learn how to better “drive” or communicate with muscles. This ability is termed voluntary activation, and it can be tested by stimulating the motor area of an individual’s brain while they perform a maximal contraction (Todd et al. 2003). If the stimulation produces extra muscle force, it means that the individual’s nervous system was not maximally activating their muscles. Currently, there is no consensus as to whether voluntary activation can actually be improved by strength training.

Therefore, we conducted a randomised, controlled trial in which one group of participants completed four weeks of strength training, while a control group did not complete the training (Nuzzo et al. in press). For the group who performed the training, each exercise session consisted of four sets of strong contractions of the elbow flexor muscles (i.e., the muscles that bend the elbow, such as the biceps). Before and after the four week intervention, both groups were tested for muscle strength, voluntary activation, and several other measures. The participants were healthy, university-aged, and they had limited or no experience with strength training.


Prior to the intervention, the strength training and control groups had similar levels of muscle strength and activation of the elbow flexor muscles. After the intervention, the group who performed the strength training improved their strength by 13%. They also improved their voluntary activation from 88.7% to 93.4%. The control group did not improve muscle strength or voluntary activation.


The results from our study show that four weeks of strength training improves the brain’s ability to “drive” the elbow flexor muscles to produce their maximal force. This helps to explain how muscles can become stronger, without a change in muscle size or architecture. Moreover, the results suggest that clinicians should consider strength training as a treatment for patients with motor impairments (e.g., stroke), as these individuals are likely to have poor voluntary activation (Bowden et al. 2014).


Nuzzo JL, Barry BK, Jones MD, Gandevia SC, Taylor JL. Effects of four weeks of strength training on the corticomotoneuronal pathway. Med Sci Sports Exerc,  doi: 10.1249/MSS.0000000000001367.


Blazevich AJ, Gill ND, Deans N, Zhou S. Lack of human muscle architectural adaptation after short-term strength training. Muscle Nerve 35: 78-86.

Bowden JL, Taylor JL, McNulty PA. Voluntary activation is reduced in both the more- and less-affected upper limbs after unilateral stroke.Front Neurol 5: 239, 2014.

Carroll TJ, Selvanayagam VS, Riek S, Semmler RG. Neural adaptations to strength training: moving beyond transcranial magnetic stimulation and reflex studies. Acta Physiol 202: 119-140, 2011.

Rantanen T, Guralnik JM, Foley D, Masaki K, Leveille S, Curb JD, White L. Midline hand grip strength as a predictor of old age disability.JAMA 281: 558-560, 1999.

Ruiz JR, Sui X, Lobelo F, Morrow Jr. JR, Jackson AW, Sjöström M, Blair SN. Association between muscular strength and mortality in men: prospective cohort study. BMJ 337: a439, 2008.

Todd G, Taylor JL, Gandevia SC. Measurement of voluntary activation of fresh and fatigued human muscles using transcranial magnetic stimulation. J Physiol 555: 661-671, 2003.


Jim Nuzzo is a Postdoctoral Fellow at Neuroscience Research Australia (NeuRA). His research investigates how strength training alters the neural connections between the brain and muscles. Click here to read Jim’s other blogs.

Source: Strength training improves the nervous system’s ability to drive muscles – Motor Impairment

, , , , , , , , ,

Leave a comment

[Abstract] Robot-assisted arm training in physical and virtual environments: A case study of long-term chronic stroke


Robot-assisted training (RT) is a novel technique with promising results for stroke rehabilitation. However, benefits of RT on individuals with long-term chronic stroke have not been well studied. For this case study, we developed an arm-based RT protocol for reaching practice in physical and virtual environments and tracked the outcomes in an individual with a long-term chronic stroke (20+ years) over 10 half-hour sessions. We analyzed the performance of the reaching movement with kinematic measures and the arm motor function using the Fugl-Meyer Assessment-Upper Extremity scale (FMA-UE). The results showed significant improvements in the subject’s reaching performance accompanied by a small increase in FMA-UE score from 18 to 21. The improvements were also transferred into real life activities, as reported by the subject. This case study shows that even in long-term chronic stroke, improvements in motor function are still possible with RT, while the underlying mechanisms of motor learning capacity or neuroplastic changes need to be further investigated.

Source: Robot-assisted arm training in physical and virtual environments: A case study of long-term chronic stroke – IEEE Xplore Document

, , , , , , , ,

Leave a comment

[Abstract] Virtual reality and non-invasive brain stimulation in stroke: How effective is their combination for upper limb motor improvement?


Upper limb (UL) hemiparesis is frequently a disabling consequence of stroke. The ability to improve UL functioning is associated with motor relearning and experience dependent neuroplasticity. Interventions such as non-invasive brain stimulation (NIBS) and task-practice in virtual environments (VEs) can influence motor relearning as well as adaptive plasticity. However, the effectiveness of a combination of NIBS and task-practice in VEs on UL motor improvement has not been systematically examined. The objective of this review was to examine the evidence regarding the effectiveness of combining NIBS with task-practice in VEs on UL motor impairment and activity levels. A systematic review of the published literature was conducted using standard methodology. Study quality was assessed using the PEDro scale and Down’s and Black checklist. Four studies examining the effects of a combination of NIBS (involving transcranial direct current stimulation; tDCS and repetitive transcranial magnetic stimulation; rTMS) were retrieved. Of these, three studies were randomized controlled trials (RCTs) and one was a cross-sectional study. There was 1a level evidence that the combination of NIBS and task-practice in a VE was beneficial in the sub-acute stage. A combination of training in a VE with rTMS as well as tDCS was beneficial for motor improvements in the UL in sub-acute stage of stroke (1b level). The combination was not found to be superior compared to task practice in VEs alone in the chronic stage (1b level). The results suggest that people with stroke may be capable of improving levels of motor impairment and activity in the sub-acute stage if their rehabilitation program involves a combination on NIBS and VE training. Emergent questions regarding the use of more sensitive outcomes, different types of stimulation parameters, locations and training environments still need to be addressed.

Source: Virtual reality and non-invasive brain stimulation in stroke: How effective is their combination for upper limb motor improvement? – IEEE Xplore Document

, , , , , , , , , , , ,

Leave a comment

[Abstract] The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation.


Chronic wrist impairment is frequent following stroke and negatively impacts everyday life. Rehabilitation of the dysfunctional limb is possible but requires extensive training and motivation. Wearable training devices might offer new opportunities for rehabilitation. However, few devices are available to train wrist extension even though this movement is highly relevant for many upper limb activities of daily living. As a proof of concept, we developed the eWrist, a wearable one degree-of-freedom powered exoskeleton which supports wrist extension training. Conceptually one might think of an electric bike which provides mechanical support only when the rider moves the pedals, i.e. it enhances motor activity but does not replace it. Stroke patients may not have the ability to produce overt movements, but they might still be able to produce weak muscle activation that can be measured via surface electromyography (sEMG). By combining force and sEMG-based control in an assist-as-needed support strategy, we aim at providing a training device which enhances activity of the wrist extensor muscles in the context of daily life activities, thereby, driving cortical reorganization and recovery. Preliminary results show that the integration of sEMG signals in the control strategy allow for adjustable assistance with respect to a proxy measurement of corticomotor drive.

Source: The eWrist — A wearable wrist exoskeleton with sEMG-based force control for stroke rehabilitation – IEEE Xplore Document

, , , , , , , , , , , ,

Leave a comment

%d bloggers like this: