Posts Tagged Upper Extremity

[ARTICLE] Combining Upper Limb Robotic Rehabilitation with Other Therapeutic Approaches after Stroke: Current Status, Rationale, and Challenges – Full Text


A better understanding of the neural substrates that underlie motor recovery after stroke has led to the development of innovative rehabilitation strategies and tools that incorporate key elements of motor skill relearning, that is, intensive motor training involving goal-oriented repeated movements. Robotic devices for the upper limb are increasingly used in rehabilitation. Studies have demonstrated the effectiveness of these devices in reducing motor impairments, but less so for the improvement of upper limb function. Other studies have begun to investigate the benefits of combined approaches that target muscle function (functional electrical stimulation and botulinum toxin injections), modulate neural activity (noninvasive brain stimulation), and enhance motivation (virtual reality) in an attempt to potentialize the benefits of robot-mediated training. The aim of this paper is to overview the current status of such combined treatments and to analyze the rationale behind them.

1. Introduction

Significant advances have been made in the management of stroke (including prevention, acute management, and rehabilitation); however cerebrovascular diseases remain the third most common cause of death and the first cause of disability worldwide [16]. Stroke causes brain damage, leading to loss of motor function. Upper limb (UL) function is particularly reduced, resulting in disability. Many rehabilitation techniques have been developed over the last decades to facilitate motor recovery of the UL in order to improve functional ability and quality of life [710]. They are commonly based on principles of motor skill learning to promote plasticity of motor neural networks. These principles include intensive, repetitive, task-oriented movement-based training [1119]. A better understanding of the neural substrates of motor relearning has led to the development of innovative strategies and tools to deliver exercise that meets these requirements. Treatments mostly target the neurological impairment (paresis, spasticity, etc.) through the activation of neural circuits or by acting on peripheral effectors. Robotic devices provide exercises that incorporate key elements of motor learning. Advanced robotic systems can offer highly repetitive, reproducible, interactive forms of training for the paretic limb, which are quantifiable. Robotic devices also enable easy and objective assessment of motor performance in standardized conditions by the recording of biomechanical data (i.e., speed, forces) [2022]. This data can be used to analyze and assess motor recovery in stroke patients [2326]. Since the 1990s, many other technology-based approaches and innovative pharmaceutical treatments have also been developed for rehabilitation, including virtual reality- (VR-) based systems, botulinum neurotoxin (BoNT) injections, and noninvasive brain stimulation (NIBS) (Direct Current Stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS)). There is currently no high-quality evidence to support any of these innovative interventions, despite the fact that some are used in routine practice [27]. By their respective mechanisms of action, each of these treatments could potentiate the effects of robotic therapy, leading to greater improvements in motor capacity. The aim of this paper is to review studies of combined treatments based on robotic rehabilitation and to analyze the rationale behind such approaches.[…]


Continue —> Combining Upper Limb Robotic Rehabilitation with Other Therapeutic Approaches after Stroke: Current Status, Rationale, and Challenges


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

Leave a comment

[WEB SITE] Myoelectric Arm Orthosis Designed for Adolescents

Published on 



Myomo Inc announces that its MyoPro myoelectric arm orthosis is now available to adolescents to help restore upper limb functionality in paralyzed or weakened arms.

In order to facilitate MyoPro fittings and delivery to adolescent patients, Myomo has partnered with Easterseals DuPage & Fox Valley (Chicago area), and is exploring partnerships with additional youth institutions and children’s hospitals, according to a media release from Cambridge, Mass-based Myomo Inc.

Paul R. Gudonis, chairman and CEO of Myomo, says in the release that, “For adolescents who suffer from a neuromuscular condition like cerebral palsy or BPI, and whose options for treatment and care have been limited, MyoPro represents new hope. We can now provide these teens with a chance to help restore function in their arms and, as a result, improve their quality of life.”

Kathy Schrock, vice president of clinical services, Easterseals DuPage & Fox Valley, Illinois, adds that, “Our partnership provides Easterseals DuPage & Fox Valley with cutting-edge technology for our therapists and clients. MyoPro will help develop arm control for adolescent clients with neurological disorders, giving them greater independence.”

Based on patented technology developed at MIT, MyoPro is designed to sense a patient’s own EMG signals through noninvasive sensors and restore function to the paralyzed or weakened arm. This allows MyoPro users to perform activities of daily living including feeding themselves, carrying objects, and doing household tasks.

[Source(s): Myomo Inc, Business Wire]


via Myoelectric Arm Orthosis Designed for Adolescents – Rehab Managment

, , , , , , ,

Leave a comment

[Abstract+References] Greater Cortical Thickness Is Associated With Enhanced Sensory Function After Arm Rehabilitation in Chronic Stroke

Objective. Somatosensory function is critical to normal motor control. After stroke, dysfunction of the sensory systems prevents normal motor function and degrades quality of life. Structural neuroplasticity underpinnings of sensory recovery after stroke are not fully understood. The objective of this study was to identify changes in bilateral cortical thickness (CT) that may drive recovery of sensory acuity. Methods. Chronic stroke survivors (n = 20) were treated with 12 weeks of rehabilitation. Measures were sensory acuity (monofilament), Fugl-Meyer upper limb and CT change. Permutation-based general linear regression modeling identified cortical regions in which change in CT was associated with change in sensory acuity. Results. For the ipsilesional hemisphere in response to treatment, CT increase was significantly associated with sensory improvement in the area encompassing the occipital pole, lateral occipital cortex (inferior and superior divisions), intracalcarine cortex, cuneal cortex, precuneus cortex, inferior temporal gyrus, occipital fusiform gyrus, supracalcarine cortex, and temporal occipital fusiform cortex. For the contralesional hemisphere, increased CT was associated with improved sensory acuity within the posterior parietal cortex that included supramarginal and angular gyri. Following upper limb therapy, monofilament test score changed from 45.0 ± 13.3 to 42.6 ± 12.9 mm (P = .063) and Fugl-Meyer score changed from 22.1 ± 7.8 to 32.3 ± 10.1 (P < .001). Conclusions. Rehabilitation in the chronic stage after stroke produced structural brain changes that were strongly associated with enhanced sensory acuity. Improved sensory perception was associated with increased CT in bilateral high-order association sensory cortices reflecting the complex nature of sensory function and recovery in response to rehabilitation.


1. Wolf, SL, Winstein, CJ, Miller, JP; EXCITE Investigators. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA. 2006;296:20952104. doi:10.1001/jama.296.17.2095. Google ScholarCrossrefMedlineISI
2. Lo, AC, Guarino, PD, Richards, LG. Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med. 2010;362:17721783. doi:10.1056/NEJMoa0911341.Google ScholarCrossrefMedlineISI
3. 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;96:981990. doi:10.1016/j.apmr.2014.10.022. Google ScholarCrossrefMedlineISI
4. Johansen-Berg, H, Dawes, H, Guy, C, Smith, SM, Wade, DT, Matthews, PM. Correlation between motor improvements and altered fMRI activity after rehabilitative therapy. Brain. 2002;125(pt 12):27312742Google ScholarCrossrefMedline
5. Luft, AR, McCombe-Waller, S, Whitall, J. Repetitive bilateral arm training and motor cortex activation in chronic stroke: a randomized controlled trial. JAMA. 2004;292:18531861. doi:10.1001/jama.292.15.1853. Google ScholarCrossrefMedlineISI
6. Pundik, S, McCabe, JP, Hrovat, K. Recovery of post stroke proximal arm function, driven by complex neuroplastic bilateral brain activation patterns and predicted by baseline motor dysfunction severity. Front Hum Neurosci. 2015;9:394. doi:10.3389/fnhum.2015.00394. Google ScholarCrossrefMedline
7. Desrosiers, J, Noreau, L, Rochette, A, Bourbonnais, D, Bravo, G, Bourget, A. Predictors of long-term participation after stroke. Disabil Rehabil. 2006;28:221230. doi:10.1080/09638280500158372. Google ScholarCrossrefMedlineISI
8. Carey, L, Macdonell, R, Matyas, TA. SENSe: Study of the Effectiveness of Neurorehabilitation on Sensation: a randomized controlled trial. Neurorehabil Neural Repair. 2011;25:304313. doi:10.1177/1545968310397705. Google ScholarSAGE JournalsISI
9. Cramer, SC, Nelles, G, Benson, RR. A functional MRI study of subjects recovered from hemiparetic stroke. Stroke. 1997;28:25182527Google ScholarCrossrefMedlineISI
10. Carey, JR, Kimberley, TJ, Lewis, SM. Analysis of fMRI and finger tracking training in subjects with chronic stroke. Brain. 2002;125(pt 4):773788Google ScholarCrossrefMedline
11. Carey, LM, Abbott, DF, Lamp, G, Puce, A, Seitz, RJ, Donnan, GA. Same intervention-different reorganization: the impact of lesion location on training-facilitated somatosensory recovery after stroke. Neurorehabil Neural Repair. 2016;30:9881000. doi:10.1177/1545968316653836.Google ScholarSAGE JournalsISI
12. Carey, LM, Matyas, TA. Frequency of discriminative sensory loss in the hand after stroke in a rehabilitation setting. J Rehabil Med. 2011;43:257263. doi:10.2340/16501977-0662. Google ScholarCrossrefMedlineISI
13. Borstad, AL, Bird, T, Choi, S, Goodman, L, Schmalbrock, P, Nichols-Larsen, DS. Sensorimotor training and neural reorganization after stroke: a case series. J Neurol Phys Ther. 2013;37:2736. doi:10.1097/NPT.0b013e318283de0d. Google ScholarCrossrefMedlineISI
14. 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 ScholarCrossrefMedlineISI
15. Zheng, X, Schlaug, G. Structural white matter changes in descending motor tracts correlate with improvements in motor impairment after undergoing a treatment course of tDCS and physical therapy. Front Hum Neurosci. 2015;9:229. doi:10.3389/fnhum.2015.00229. Google ScholarCrossrefMedlineISI
16. Bailey, CH, Kandel, ER. Structural changes accompanying memory storage. Annu Rev Physiol. 1993;55:397426. doi:10.1146/ Google ScholarCrossrefMedlineISI
17. Jones, TA, Chu, CJ, Grande, LA, Gregory, AD. Motor skills training enhances lesion-induced structural plasticity in the motor cortex of adult rats. J Neurosci. 1999;19:1015310163Google ScholarCrossrefMedlineISI
18. Wang, L, Conner, JM, Rickert, J, Tuszynski, MH. Structural plasticity within highly specific neuronal populations identifies a unique parcellation of motor learning in the adult brain. Proc Natl Acad Sci U S A. 2011;108:25452550. doi:10.1073/pnas.1014335108. Google ScholarCrossrefMedline
19. Maguire, EA, Gadian, DG, Johnsrude, IS. Navigation-related structural change in the hippocampi of taxi drivers. Proc Natl Acad Sci U S A. 2000;97:43984403. doi:10.1073/pnas.070039597. Google ScholarCrossrefMedlineISI
20. Draganski, B, Gaser, C, Busch, V, Schuierer, G, Bogdahn, U, May, A. Neuroplasticity: changes in grey matter induced by training. Nature. 2004;427:311312. doi:10.1038/427311a. Google ScholarCrossrefMedlineISI
21. Sampaio-Baptista, C, Scholz, J, Jenkinson, M. Gray matter volume is associated with rate of subsequent skill learning after a long term training intervention. Neuroimage. 2014;96:158166. doi:10.1016/j.neuroimage.2014.03.056. Google ScholarCrossrefMedline
22. Nouri, S, Cramer, SC. Anatomy and physiology predict response to motor cortex stimulation after stroke. Neurology. 2011;77:10761083. doi:10.1212/WNL.0b013e31822e1482. Google ScholarCrossrefMedlineISI
23. Gauthier, LV, Taub, E, Mark, VW, Barghi, A, Uswatte, G. Atrophy of spared gray matter tissue predicts poorer motor recovery and rehabilitation response in chronic stroke. Stroke. 2012;43:453457. doi:10.1161/STROKEAHA.111.633255. Google ScholarCrossrefMedlineISI
24. Sterr, A, Dean, PJ, Vieira, G, Conforto, AB, Shen, S, Sato, JR. Cortical thickness changes in the non-lesioned hemisphere associated with non-paretic arm immobilization in modified CI therapy. Neuroimage Clin. 2013;2:797803. doi:10.1016/j.nicl.2013.05.005. Google ScholarCrossrefMedline
25. Schaechter, JD, Moore, CI, Connell, BD, Rosen, BR, Dijkhuizen, RM. Structural and functional plasticity in the somatosensory cortex of chronic stroke patients. Brain. 2006;129(pt 10):27222733. doi:10.1093/brain/awl214. Google ScholarCrossrefMedline
26. Kopp, B, Kunkel, A, Flor, H. The Arm Motor Ability Test: reliability, validity, and sensitivity to change of an instrument for assessing disabilities in activities of daily living. Arch Phys Med Rehabil. 1997;78:615620Google ScholarCrossrefMedlineISI
27. Duncan, PW, Lai, SM, Keighley, J. Defining post-stroke recovery: implications for design and interpretation of drug trials. Neuropharmacology. 2000;39:835841Google ScholarCrossrefMedlineISI
28. Fischl, B. FreeSurfer. Neuroimage. 2012;62:774781. doi:10.1016/j.neuroimage.2012.01.021.Google ScholarCrossrefMedlineISI
29. Reuter, M, Rosas, HD, Fischl, B. Highly accurate inverse consistent registration: a robust approach. Neuroimage. 2010;53:11811196. doi:10.1016/j.neuroimage.2010.07.020. Google ScholarCrossrefMedlineISI
30. Greve, DN, Van der Haegen, L, Cai, Q. A surface-based analysis of language lateralization and cortical asymmetry. J Cogn Neurosci. 2013;25:14771492. doi:10.1162/jocn_a_00405. Google ScholarCrossrefMedlineISI
31. Winkler, AM, Ridgway, GR, Webster, MA, Smith, SM, Nichols, TE. Permutation inference for the general linear model. Neuro-image. 2014;92:381397. doi:10.1016/j.neuroimage.2014.01.060. Google ScholarCrossrefMedlineISI
32. Smith, SM, Nichols, TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage. 2009;44:8398. doi:10.1016/j.neuroimage.2008.03.061. Google ScholarCrossrefMedlineISI
33. Nichols, T, Holmes, A. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:125. doi:10.1016/B978-012264841-0/50048-2.Google ScholarCrossrefMedlineISI
34. Winkler, AM, Ridgway, GR, Douaud, G, Nichols, TE, Smith, SM. Faster permutation inference in brain imaging. Neuroimage. 2016;141:502516. doi:10.1016/j.neuroimage.2016.05.068.Google ScholarCrossrefMedline
35. Desikan, RS, Ségonne, F, Fischl, B. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968980. doi:10.1016/j.neuroimage.2006.01.021. Google ScholarCrossrefMedlineISI
36. Thut, G, Théoret, H, Pfennig, A. Differential effects of low-frequency rTMS at the occipital pole on visual-induced alpha desynchronization and visual-evoked potentials. Neuroimage. 2003;18:334347Google ScholarCrossrefMedline
37. Amedi, A, Floel, A, Knecht, S, Zohary, E, Cohen, LG. Transcranial magnetic stimulation of the occipital pole interferes with verbal processing in blind subjects. Nat Neurosci. 2004;7:12661270. doi:10.1038/nn1328. Google ScholarCrossrefMedlineISI
38. Merabet, LB, Swisher, JD, McMains, SA. Combined activation and deactivation of visual cortex during tactile sensory processing. J Neurophysiol. 2006;97:16331641. doi:10.1152/jn.00806.2006. Google ScholarCrossrefMedline
39. Amedi, A, Malach, R, Hendler, T, Peled, S, Zohary, E. Visuo-haptic object-related activation in the ventral visual pathway. Nat Neurosci. 2001;4:324330. doi:10.1038/85201. Google ScholarCrossrefMedlineISI
40. Stoesz, MR, Zhang, M, Weisser, VD, Prather, SC, Mao, H, Sathian, K. Neural networks active during tactile form perception: common and differential activity during macrospatial and microspatial tasks. Int J Psychophysiol. 2003;50:4149Google ScholarCrossrefMedline
41. Lacey, S, Tal, N, Amedi, A, Sathian, K. A putative model of multisensory object representation. Brain Topogr. 2009;21:269274. doi:10.1007/s10548-009-0087-4. Google ScholarCrossrefMedline
42. Kim, JK, Zatorre, RJ. Tactile-auditory shape learning engages the lateral occipital complex. J Neurosci. 2011;31:78487856. doi:10.1523/JNEUROSCI.3399-10.2011. Google ScholarCrossrefMedline
43. Botvinick, M, Cohen, J. Rubber hands “feel” touch that eyes see. Nature. 1998;391:756. doi:10.1038/35784. Google ScholarCrossrefMedlineISI
44. Zangaladze, A, Epstein, CM, Grafton, ST, Sathian, K. Involvement of visual cortex in tactile discrimination of orientation. Nature. 1999;401:587590. doi:10.1038/44139. Google ScholarCrossrefMedlineISI
45. Sathian, K. Analysis of haptic information in the cerebral cortex. J Neurophysiol. 2016;116:17951806. doi:10.1152/jn.00546.2015. Google ScholarCrossrefMedline
46. Hsiao, S, Gomez-Ramirez, M. Touch. In: Gottfried, JA ed. Neurobiology of Sensation and Reward. Frontiers in Neuroscience. Boca Raton, FLCRC Press/Taylor & Francis2011Google ScholarCrossref
47. Vincent, JL, Snyder, AZ, Fox, MD. Coherent spontaneous activity identifies a hippocampal-parietal memory network. J Neurophysiol. 2006;96:35173531. doi:10.1152/jn.00048.2006.Google ScholarCrossrefMedlineISI
48. Roland, PE, O’Sullivan, B, Kawashima, R. Shape and roughness activate different somatosensory areas in the human brain. Proc Natl Acad Sci U S A. 1998;95:32953300Google ScholarCrossrefMedlineISI
49. Bodegård, A, Geyer, S, Grefkes, C, Zilles, K, Roland, PE. Hierarchical processing of tactile shape in the human brain. Neuron. 2001;31:317328Google ScholarCrossrefMedline
50. Karhu, J, Tesche, CD. Simultaneous early processing of sensory input in human primary (SI) and secondary (SII) somatosensory cortices. J Neurophysiol. 1999;81:20172025Google ScholarCrossrefMedline
51. Fabri, M, Polonara, G, Pesce, MD, Quattrini, A, Salvolini, U, Manzoni, T. Posterior corpus callosum and interhemispheric transfer of somatosensory information: an fMRI and neuropsychological study of a partially callosotomized patient. J Cogn Neurosci. 2001;13:10711079. doi:10.1162/089892901753294365. Google ScholarCrossrefMedlineISI
52. Chung, Y, Han, S, Kim, HS. Intra- and inter-hemispheric effective connectivity in the human somatosensory cortex during pressure stimulation. BMC Neurosci. 2014;15:43. doi:10.1186/1471-2202-15-43. Google ScholarCrossrefMedline
53. Mohajerani, MH, Aminoltejari, K, Murphy, TH. Targeted mini-strokes produce changes in interhemispheric sensory signal processing that are indicative of disinhibition within minutes. Proc Natl Acad Sci U S A. 2011;108:E183E191. doi:10.1073/pnas.1101914108. Google ScholarCrossrefMedlineISI
54. Blankenburg, F, Ruff, CC, Bestmann, S. Interhemispheric effect of parietal TMS on somatosensory response confirmed directly with concurrent TMS-fMRI. J Neurosci. 2008;28:1320213208. doi:10.1523/JNEUROSCI.3043-08.2008. Google ScholarCrossrefMedline
55. Inoue, K, Kawashima, R, Sugiura, M. Activation in the ipsilateral posterior parietal cortex during tool use: a PET study. Neuroimage. 2001;14:14691475. doi:10.1006/nimg.2001.0942. Google ScholarCrossrefMedlineISI
56. Takatsuru, Y, Fukumoto, D, Yoshitomo, M, Nemoto, T, Tsukada, H, Nabekura, J. Neuronal circuit remodeling in the contralateral cortical hemisphere during functional recovery from cerebral infarction. J Neurosci. 2009;29:1008110086. doi:10.1523/JNEUROSCI.1638-09.2009.Google ScholarCrossrefMedlineISI
57. Nelles, G, Spiekermann, G, Jueptner, M. Reorganization of sensory and motor systems in hemiplegic stroke patients. A positron emission tomography study. Stroke. 1999;30:15101516Google ScholarCrossrefMedlineISI
58. Jang, SH, Lee, MY. Correlation between somatosensory function and cortical activation induced by touch stimulation in patients with intracerebral hemorrhage. Int J Neurosci. 2013;123:248252. doi:10.3109/00207454.2012.755968. Google ScholarCrossrefMedlineISI
59. Bannister, LC, Crewther, SG, Gavrilescu, M, Carey, LM. Improvement in touch sensation after stroke is associated with resting functional connectivity changes. Front Neurol. 2015;6:165. doi:10.3389/fneur.2015.00165. Google ScholarCrossrefMedline
60. Kang, X, Herron, TJ, Cate, AD, Yund, EW, Woods, DL. Hemispherically-unified surface maps of human cerebral cortex: reliability and hemispheric asymmetries. PloS One. 2012;7:e45582. doi:10.1371/journal.pone.0045582. Google ScholarCrossrefMedline
61. Maingault, S, Tzourio-Mazoyer, N, Mazoyer, B, Crivello, F. Regional correlations between cortical thickness and surface area asymmetries: a surface-based morphometry study of 250 adults. Neuropsychologia. 2016;93(pt B):350364. doi:10.1016/j.neuropsychologia.2016.03.025. Google ScholarCrossrefMedline
62. Van de Winckel, A, Wenderoth, N, De Weerdt, W. Frontoparietal involvement in passively guided shape and length discrimination: a comparison between subcortical stroke patients and healthy controls. Exp Brain Res. 2012;220:179189. doi:10.1007/s00221-012-3128-2. Google ScholarCrossrefMedline
63. Borstad, A, Schmalbrock, P, Choi, S, Nichols-Larsen, DS. Neural correlates supporting sensory discrimination after left hemisphere stroke. Brain Res. 2012;1460:7887. doi:10.1016/j.brainres.2012.03.060. Google ScholarCrossrefMedlineISI
64. Lindberg, PG, Schmitz, C, Engardt, M, Forssberg, H, Borg, J. Use-dependent up- and down-regulation of sensorimotor brain circuits in stroke patients. Neurorehabil Neural Repair. 2007;21:315326. doi:10.1177/1545968306296965. Google ScholarSAGE JournalsISI
65. Dechaumont-Palacin, S, Marque, P, De Boissezon, X. Neural correlates of proprioceptive integration in the contralesional hemisphere of very impaired patients shortly after a subcortical stroke: an FMRI study. Neurorehabil Neural Repair. 2008;22:154165. doi:10.1177/1545968307307118. Google ScholarSAGE JournalsISI
66. 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 ScholarCrossrefMedlineISI
67. Fields, RD. Changes in brain structure during learning: fact or artifact? Reply to Thomas and Baker. Neuroimage. 2013;73:260267. doi:10.1016/j.neuroimage.2012.08.085. Google ScholarCrossrefMedline
68. Xu, T, Yu, X, Perlik, AJ. Rapid formation and selective stabilization of synapses for enduring motor memories. Nature. 2009;462:915919. doi:10.1038/nature08389. Google ScholarCrossrefMedlineISI
69. Kleim, JA, Hogg, TM, VandenBerg, PM, Cooper, NR, Bruneau, R, Remple, M. Cortical synaptogenesis and motor map reorganization occur during late, but not early, phase of motor skill learning. J Neurosci. 2004;24:628633. doi:10.1523/JNEUROSCI.3440-03.2004. Google ScholarCrossrefMedlineISI
70. Sampaio-Baptista, C, Khrapitchev, AA, Foxley, S. Motor skill learning induces changes in white matter microstructure and myelination. J Neurosci. 2013;33:1949919503. doi:10.1523/JNEUROSCI.3048-13.2013. Google ScholarCrossrefMedlineISI
71. Erickson, KI. Evidence for structural plasticity in humans: comment on Thomas and Baker (2012). Neuroimage. 2013;73:237238. doi:10.1016/j.neuroimage.2012.07.003. Google ScholarCrossrefMedline
72. Feydy, A, Carlier, R, Roby-Brami, A. Longitudinal study of motor recovery after stroke: recruitment and focusing of brain activation. Stroke. 2002;33:16101617Google ScholarCrossrefMedlineISI
73. Hamzei, F, Liepert, J, Dettmers, C, Weiller, C, Rijntjes, M. Two different reorganization patterns after rehabilitative therapy: an exploratory study with fMRI and TMS. Neuroimage. 2006;31:710720. doi:10.1016/j.neuroimage.2005.12.035. Google ScholarCrossrefMedlineISI

via Greater Cortical Thickness Is Associated With Enhanced Sensory Function After Arm Rehabilitation in Chronic Stroke – Svetlana Pundik, Aleka Scoco, Margaret Skelly, Jessica P. McCabe, Janis J. Daly, 2018

, , , , , , , , , ,

Leave a comment

[ARTICLE] Measurements by A LEAP-Based Virtual Glove for the Hand Rehabilitation – Full Text


Hand rehabilitation is fundamental after stroke or surgery. Traditional rehabilitation requires a therapist and implies high costs, stress for the patient, and subjective evaluation of the therapy effectiveness. Alternative approaches, based on mechanical and tracking-based gloves, can be really effective when used in virtual reality (VR) environments. Mechanical devices are often expensive, cumbersome, patient specific and hand specific, while tracking-based devices are not affected by these limitations but, especially if based on a single tracking sensor, could suffer from occlusions. In this paper, the implementation of a multi-sensors approach, the Virtual Glove (VG), based on the simultaneous use of two orthogonal LEAP motion controllers, is described. The VG is calibrated and static positioning measurements are compared with those collected with an accurate spatial positioning system. The positioning error is lower than 6 mm in a cylindrical region of interest of radius 10 cm and height 21 cm. Real-time hand tracking measurements are also performed, analysed and reported. Hand tracking measurements show that VG operated in real-time (60 fps), reduced occlusions, and managed two LEAP sensors correctly, without any temporal and spatial discontinuity when skipping from one sensor to the other. A video demonstrating the good performance of VG is also collected and presented in the Supplementary Materials. Results are promising but further work must be done to allow the calculation of the forces exerted by each finger when constrained by mechanical tools (e.g., peg-boards) and for reducing occlusions when grasping these tools. Although the VG is proposed for rehabilitation purposes, it could also be used for tele-operation of tools and robots, and for other VR applications.

1. Introduction

Hand rehabilitation is extremely important for recovering from post-stroke or post-surgery residual impairments and its effectiveness depends on frequency, duration and quality of the rehabilitation sessions [1]. Traditional rehabilitation requires a therapist for driving and controlling patients during sessions. Procedure effectiveness is evaluated subjectively by the therapist, basing on experience. In the last years, several automated (tele)rehabilitation gloves, based on mechanical devices or tracking sensors, have been presented [2,3,4,5,6,7,8,9,10]. These gloves allow the execution of therapy at home and rehabilitation effectiveness can be analytically calculated and summarized in numerical parameters, controlled by therapists through Internet. Moreover, these equipment can be easily interfaced with virtual reality (VR) environments [11], which have been proven to increase rehabilitation efficacy [12]. Mechanical devices are equipped with pressure sensors and pneumatic actuators for assisting and monitoring the hand movements and for applying forces to which the patient has to oppose [13,14]. However, they are expensive, cumbersome, patient specific (different patients cannot reuse the same system) and hand specific (the patient cannot use the same system indifferently with both hands). Tracking-based gloves consist of computer vision algorithms for the analysis and interpretation of videos from depth sensing sensors to calculate hand kinematics in real time [10,15,16,17,18,19]. Besides depth sensors, LEAP [20] is a small and low-cost hand 3D tracking device characterized by high-resolution and high-reactivity [21,22,23], used in VR [24], and has been recently presented and tested with success in the hand rehabilitation, with exercises designed in VR environments [25]. Despite the advantages of using LEAP with VR, a single sensor does not allow accurate quantitative evaluation of hand and fingers tracking in case of occlusions. The system proposed in [10] consisted on two orthogonal LEAPs designed to reduce occlusions and to improve objective hand-tracking evaluation. The two sensors were fixed to a wood support that maintained them orthogonal each other. The previous prototype was useful to test the robustness of each sensor, in presence of the other, to the potential infra-red interferences, to evaluate the maintenance of the maximum operative range of each sensor and, finally, to demonstrate the hand tracking idea. However, it was imprecise, due to the usage of raw VG support and positioning system, the non-optimal reciprocal positioning of the sensors, and the impossibility of performing a reciprocal calibration independent of the sensors measurements. This fact did not allow the evaluation of the intrinsic precision of the VG and to perform accurate, real-time quantitative hand tracking measurements. In this paper, we present a method for constructing an engineered version of the LEAP based VG, a technique for its accurate calibration and for collecting accurate positioning measurements and high-quality evaluation of positioning errors, specific of VG. Moreover, real-time experimental hand tracking measurements were collected (a video demonstrating its real-time performance and precision was also provided in the Supplementary Materials), presented and discussed.[…]


Continue —>  Measurements by A LEAP-Based Virtual Glove for the Hand Rehabilitation

An external file that holds a picture, illustration, etc.Object name is sensors-18-00834-g001.jpg

Figure 1
VG mounted on its aluminium support.

, , , , , , , , ,

Leave a comment

[Abstract] Effects of Kinect-based virtual reality game training on upper extremity motor recovery in chronic stroke.



Therapeutic benefits of Kinect-based virtual reality (VR) game training in rehabilitation encourage its use to improve motor function.


To assess the effects of Kinect-based VR training on motor recovery of the upper extremity and functional outcomes in patients with chronic stroke.


In this randomized controlled trial, group A received 20 sessions of physical therapy (PT) + 20 sessions of Kinect-based VR training and group B received only 20 sessions of PT. Clinical outcome measures were assessed at baseline and at the end of the treatments. Primary outcome measures that assess stroke patients’ motor function included upper extremity (UE) Fugl-Meyer Assessment (FMA). Secondary outcome measures were Brunnstrom Recovery Stages (BRS), Modified Ashworth Scale (MAS), Box and Block test (BBT), Motricity index (MI), and active range of motion (AROM) measurement.


Statistically significant improvements in game scores (p < 0.05) were observed in group A. In within-group analysis, there were statistically significant improvements in all clinical outcome measures except for the BRS-hand, MAS-distal, and MAS-hand in group A; MAS-(proximal, distal, hand) and BRS-(UE, hand) in group B compared with baseline values. Differences from baseline of FMA, MI, and AROM (except adduction of shoulder and extension of elbow) were greater in group A (p < 0.05).


To conclude, our results suggest that the adjunct use of Kinect-based VR training may contribute to the improvement of UE motor function and AROM in chronic stroke patients. Further studies with a larger number of subjects with longer follow-up periods are needed to establish its effectiveness in neurorehabilitation.


via Effects of Kinect-based virtual reality game training on upper extremity motor recovery in chronic stroke. – PubMed – NCBI

, , , , , , , , , ,

Leave a comment

[ARTICLE] A multisession evaluation of an adaptive competitive arm rehabilitation game – Full Text


People with neurological injuries such as stroke should exercise frequently and intensely to regain their motor abilities, but are generally hindered by lack of motivation. One way to increase motivation in rehabilitation is through competitive exercises, but such exercises have only been tested in single brief sessions and usually did not adapt difficulty to the patient’s abilities.


We designed a competitive arm rehabilitation game for two players that dynamically adapts its difficulty to both players’ abilities. This game was evaluated by two participant groups: 15 participants with chronic arm impairment who exercised at home with an unimpaired friend or relative, and 20 participants in the acute or subacute phase of stroke who exercised in pairs (10 pairs) at a rehabilitation clinic. All participants first played the game against their human opponent for 3 sessions, then played alone (against a computer opponent) in the final, fourth session. In all sessions, participants’ subjective experiences were assessed with the Intrinsic Motivation Inventory questionnaire while exercise intensity was measured using inertial sensors built into the rehabilitation device. After the fourth session, a final brief questionnaire was used to compare competition and exercising alone.


Participants who played against an unimpaired friend or relative at home tended to prefer competition (only 1 preferred exercising alone), and exhibited higher enjoyment and exercise intensity when competing (first three sessions) than when exercising alone (last session).

Participants who played against each other in the clinic, however, did not exhibit significant differences between competition and exercising alone. For both groups, there was no difference in enjoyment or exercise intensity between the first three sessions, indicating no negative effects of habituation or novelty.


Competitive exercises have high potential for unsupervised home rehabilitation, as they improve enjoyment and exercise intensity compared to exercising alone. Such exercises could thus improve rehabilitation outcome, but this needs to be tested in long-term clinical trials. It is not clear why participants who competed against each other at the clinic did not exhibit any advantages of competition, and further studies are needed to determine how different factors (environment, nature of opponent etc.) influence patients’ experiences with competitive exercises.

Trial registration

The study is not a clinical trial. While human subjects are involved, they do not participate in a full rehabilitation intervention, and no health outcomes are examined.

Electronic supplementary material

The online version of this article (10.1186/s12984-017-0336-9) contains supplementary material, which is available to authorized users.


Rehabilitation games

Stroke is a leading cause of disability, with 795,000 new or recurrent strokes per year in the United States alone [1]. 88% of survivors experience motor function impairment and thus require rehabilitation to regain their movement abilities [2]. However, even top hospitals devote only an hour per day to motor rehabilitation [3], and exercise intensity is usually too low for optimal rehabilitation outcome [4]. Patients are thus expected to exercise independently at home after leaving the clinic to fully regain their abilities, but frequently do not exercise frequently or intensely enough. For example, one study found that only 30% of unsupervised patients comply with prescribed home rehabilitation regimens [5]. Another home rehabilitation study found that patients average around 1.5 h of exercise per week [6], while clinical studies involve at least 3 h of exercise per week [78]. To improve home rehabilitation, it is therefore critical to increase the frequency and intensity of exercise.

One key reason for poor compliance in home rehabilitation is lack of motivation, which is an important predictor of rehabilitation outcome [910]. While the definition of motivation in rehabilitation is blurry, it is generally agreed to involve a willingness to actively engage in exercise [1112]. To improve engagement, researchers have thus developed numerous rehabilitation games that try to both ensure high enjoyment (using, e.g., meaningful goals, in-game rewards and entertaining graphics [1215]) and provide an appropriate exercise intensity via automated difficulty adaptation [121416]. The games are controlled using motion tracking hardware such as the Microsoft Kinect or even with rehabilitation robots that provide limb support in addition to motion tracking. However, recent reviews have emphasized that such games are not yet sufficiently engaging for all patients [1718]. Therefore, additional rehabilitation game development and validation is necessary to improve patient engagement.[…]


Continue —> A multisession evaluation of an adaptive competitive arm rehabilitation game

An external file that holds a picture, illustration, etc.Object name is 12984_2017_336_Fig1_HTML.jpg

Fig. 1
The Bimeo arm rehabilitation system in the wrist and forearm training configuration. Inertial sensors are attached to the upper arm, attached to the forearm, and integrated in the sphere that supports the hand

, , , , , , , , , , ,

Leave a comment

[BOOK] New Vibratory Device for Wrist Rehabilitation – Innovation, Engineering and Entrepreneurship – Google Books

New Vibratory Device for Wrist Rehabilitation

H Puga – Innovation, Engineering and Entrepreneurship, 2018
Wrist injuries are very common in most of the population, specially bone fractures,
but also other pathologies such as tendinitis and neurological diseases. When the
wrist is injured, their flexion-extension and radial-ulnar deviation and pronation …


via Innovation, Engineering and Entrepreneurship – Google Books

, , , , , , , ,

Leave a comment

[ARTICLE] Let’s Improvise! iPad-based music therapy with functional electrical stimulation for upper limb stroke rehabilitation – Full Text

In plain language

In the western world, stroke has been identified as the leading cause of disability in adults. Impairment to the arm/hand and depressive symptoms seem to be among the most frequent resultants of stroke. This article describes a collaborative occupational therapy and music therapy intervention for post-stroke arm/hand recovery.  The intervention itself combines principles of music therapy with tablet technology and functional electrical stimulation. The implementation of this novel intervention, described in this clinical case report, has implications for benefits to physical and motivational aspects of rehabilitation. Recommendations for further research of this intervention are also discussed.


This retrospective clinical case report will examine the implementation of a novel intervention combining a Functional Electrical Stimulation (FES) protocol with an iPad application. A 74-year-old female retired pianist and Professor of Music was admitted to a rehabilitation hospital following a left pontine stroke. On assessment, she was unable to use her right upper limb functionally. Conventional occupational therapy commenced soon after admission and consisted of functional retraining, including FES to the wrist and finger extensors. At week 4, the Registered Music Therapist (RMT) and Occupational Therapist (OT) collaborated to commence a trial of forearm FES in combination with an iPad-based music making application; ThumbJam. This application was used to encourage the patient to participate in touch sensitive musical improvisation using the affected hand in an attempt to promote engagement in complex motor patterns and non-verbal expression. Within 3 weeks, the patient was able to use ThumbJam without the FES, progressed to the keyboard in 4 weeks and has since commenced independent scales on the piano at home (21 weeks), as well as successful use of the upper limb in Activities of Daily Living (ADLs). On follow up (7 months), the patient reflected on the motivating elements of the intervention that helped her to achieve a functional outcome in her upper limb. This retrospective clinical case report will review the evidence with regard to FES and music therapy, outline the treatment protocol used and make recommendations for future research of “FES+ThumbJam” in upper limb stroke rehabilitation.[…]

Continue —> Let’s Improvise! iPad-based music therapy with functional electrical stimulation for upper limb stroke rehabilitation | Australian Music Therapy Association


, , , , , , , , ,

Leave a comment

[ARTICLE] Upper limb robotic rehabilitation for chronic stroke survivors: a single-group preliminary study – Full Text PDF


[Purpose] This study aimed to assess whether robotic rehabilitation can improve upper limb function, activities of daily living performance, and kinematic performance of chronic stroke survivors.

[Subjects and Methods] Participants were 21 chronic stroke survivors (19 men; 60.8 years; Mini-Mental State Examination score: 28; onset duration: 10.2 years). Training exercises were performed with a Whole Arm Manipulator and a 120-inch projective display to provide visual and auditory feedback. Once the training began, red and grey balls appeared on the projective display, and participants performed reaching movements, in the assist-as-needed mode, toward 6 directional targets in a 3-dimensional space. All participants received training for 40 minutes per day, thrice per
week, for 6 weeks. Main outcome measures were upper limb function (Fugl-Meyer Assessment, Action Research Arm Test, and Box and Blocks Test scores), activities of daily living performance (Modified Barthel Index), and kinematic performance (movement velocity) in 6 directions.

[Results] After 6 weeks, significant improvement was observed in upper limb function, activities of daily living performance, and kinematic performance.

This study demonstrated the positive effects of robotic rehabilitation on upper limb function, activities of daily living performance, and kinematic performance in chronic stroke survivors.

Full Text PDF

, , , , , , ,

Leave a comment

[Abstract] The effect of robot therapy assisted by surface EMG on hand recovery in post-stroke patients. A pilot study


Background: Hemiparesis caused by a stroke negatively limits a patient’s motor function. Nowadays, innovative technologies such as robots are commonly used in upper limb rehabilitation. The main goal of robot-aided therapy is to provide a maximum number of stimuli in order to stimulate brain neuroplasticity. Treatment applied in this study via the AMADEO robot aimed to improve finger flexion and extension.
Aim: To assess the effect of rehabilitation assisted by a robot and enhanced by surface EMG.
Research project: Before-after study design.
Materials and methods: The study group consisted of 10 post-stroke patients enrolled for therapy with the AMADEO robot for at least 15 sessions. At the beginning and at the end of treatment, the following tests were used for clinical assessment: Fugl-Meyer scale, Box and Block test and Nine Hole Peg test. In the present study, we used surface electromyography (sEMG) to maintain optimal kinematics of hand motion. Whereas sensorial feedback, provided by the robot, was vital in obtaining closed-loop control. Thus, muscle contraction was transmitted to the amplifier through sEMG, activating the mechanism of the robot. Consequentially, sensorial feedback was provided to the patient.
Results: Statistically significant improvement of upper limb function was observed in: Fugl-Meyer (p = 0.38) and Box and Block (p = 0.27). The Nine Hole Peg Test did not show statistically significant changes in motor skills of the hand. However, the functional improvement was observed at the level of 6% in the Fugl-Meyer, 15% in the Box and Block, and 2% in the Nine Hole Peg test.
Conclusions: Results showed improvement in hand grasp and overall function of the upper limb. Due to sEMG, it was possible to implement robot therapy in the treatment of patients with severe hand impairment.

via The effect of robot therapy assisted by surface EMG on hand recovery in pos

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

Leave a comment

%d bloggers like this: