Posts Tagged UL

[Abstract] Exploration of barriers and enablers for evidence-based interventions for upper limb rehabilitation following a stroke: Use of Constraint Induced Movement Therapy and Robot Assisted Therapy in NHS Scotland

The routine use of evidence-based upper limb rehabilitation interventions after stroke has the potential to improve function and increase independence. Two such interventions are Constraint Induced Movement Therapy and Robot Assisted Therapy. Despite evidence to support both interventions, their use within the National Health Service appears, anecdotally, to be low. We sought to understand user perceptions in order to explain low uptake in clinical practice.

A combination of a cross-sectional online survey with therapists and semi-structured interviews with stroke patients was used to explore uptake and user opinions on the benefits, enablers and barriers to each intervention.

The therapists surveyed reported low use of Constraint Induced Movement Therapy and Robot Assisted Therapy in clinical practice within the Scottish National Health Service. Barriers identified by therapists were inadequate staffing, and a lack of training and resources. Interviews with stroke patients identified themes that may help us to understand the acceptability of each intervention, such as the impact of motivation.

Barriers to the uptake of Constraint Induced Movement Therapy and Robot Assisted Therapy within the clinical setting were found to be similar. Further qualitative research should be completed in order to help us understand the role patient motivation plays in uptake.

via Exploration of barriers and enablers for evidence-based interventions for upper limb rehabilitation following a stroke: Use of Constraint Induced Movement Therapy and Robot Assisted Therapy in NHS Scotland – Gillian Sweeney, Mark Barber, Andrew Kerr,

, , , , , , , , , , ,

Leave a comment

[Abstract] Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke

Background. Accurate prediction of clinical impairment in upper-extremity motor function following therapy in chronic stroke patients is a difficult task for clinicians but is key in prescribing appropriate therapeutic strategies. Machine learning is a highly promising avenue with which to improve prediction accuracy in clinical practice.

Objectives. The objective was to evaluate the performance of 5 machine learning methods in predicting postintervention upper-extremity motor impairment in chronic stroke patients using demographic, clinical, neurophysiological, and imaging input variables.

Methods. A total of 102 patients (female: 31%, age 61 ± 11 years) were included. The upper-extremity Fugl-Meyer Assessment (UE-FMA) was used to assess motor impairment of the upper limb before and after intervention. Elastic net (EN), support vector machines, artificial neural networks, classification and regression trees, and random forest were used to predict postintervention UE-FMA. The performances of methods were compared using cross-validated R2Results. EN performed significantly better than other methods in predicting postintervention UE-FMA using demographic and baseline clinical data (median R2EN=0.91,R2RF=0.88,R2ANN=0.83,R2SVM=0.79,R2CART=0.70;REN2=0.91,RRF2=0.88,RANN2=0.83,RSVM2=0.79,RCART2=0.70; P < .05). Preintervention UE-FMA and the difference in motor threshold (MT) between the affected and unaffected hemispheres were the strongest predictors. The difference in MT had greater importance than the absence or presence of a motor-evoked potential (MEP) in the affected hemisphere.

Conclusion. Machine learning methods may enable clinicians to accurately predict a chronic stroke patient’s postintervention UE-FMA. Interhemispheric difference in the MT is an important predictor of chronic stroke patients’ response to therapy and, therefore, could be included in prospective studies.

1. Coupar, F, Pollock, A, Rowe, P, Weir, C, Langhorne, P. Predictors of upper limb recovery after stroke: a systematic review and meta-analysis. Clin Rehabil. 2012;26:291313. doi:10.1177/0269215511420305
Google Scholar | SAGE Journals | ISI

2. Karahan, AY, Kucuksen, S, Yilmaz, H, Salli, A, Gungor, T, Sahin, M. Effects of rehabilitation services on anxiety, depression, care-giving burden and perceived social support of stroke caregivers. Acta Medica (Hradec Kralove). 2014;57:6872. doi:10.14712/18059694.2014.42
Google Scholar | Crossref | Medline

3. Dobkin, BH. Clinical practice: rehabilitation after stroke. N Engl J Med. 2005;352:16771684.
Google Scholar | Crossref | Medline | ISI

4. Kim, B, Winstein, C. Can neurological biomarkers of brain impairment be used to predict poststroke motor recovery? A systematic review. Neurorehabil Neural Repair. 2017;31:324. doi:10.1177/1545968316662708
Google Scholar | SAGE Journals | ISI

5. Burke, E, Cramer, SC. Biomarkers and predictors of restorative therapy effects after stroke. Curr Neurol Neurosci Rep. 2013;13:329. doi:10.1007/s11910-012-0329-9
Google Scholar | Crossref | Medline | ISI

6. Asadi, H, Dowling, R, Yan, B, Mitchell, P. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS One. 2014;9:e88225. doi:10.1371/journal.pone.0088225
Google Scholar | Crossref | Medline

7. Stinear, CM, Barber, PA, Smale, PR, Coxon, JP, Fleming, MK, Byblow, WD. Functional potential in chronic stroke patients depends on corticospinal tract integrity. Brain. 2007;130:170180. doi:10.1093/brain/awl333
Google Scholar | Crossref | Medline | ISI

8. Escudero, JV, Sancho, J, Bautista, D, Escudero, M, Lopez-Trigo, J. Prognostic value of motor evoked potential obtained by transcranial magnetic brain stimulation in motor function recovery in patients with acute ischemic stroke. Stroke. 1998;29:18541859. doi:10.1161/01.STR.29.9.1854
Google Scholar | Crossref | Medline | ISI

9. Hendricks, HT, Pasman, JW, van Limbeek, J, Zwarts, MJ. Motor evoked potentials in predicting recovery from upper extremity paralysis after acute stroke. Cerebrovasc Dis. 2003;16:265271. doi:10.1159/000071126
Google Scholar | Crossref | Medline | ISI

10. Stinear, CM, Byblow, WD, Ackerley, SJ, Smith, MC, Borges, VM, Barber, PA. PREP2: a biomarker-based algorithm for predicting upper limb function after stroke. Ann Clin Transl Neurol. 2017;4:811820. doi:10.1002/acn3.488
Google Scholar | Crossref | Medline

11. Kemlin, C, Moulton, E, Lamy, JC, Rosso, C. Resting motor threshold is a biomarker for motor stroke recovery. Ann Phys Rehabil Med. 2018;61(suppl):e26. doi:10.1016/
Google Scholar | Crossref

12. Grefkes, C, Fink, GR. Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain. 2011;134(pt 5):12641276. doi:10.1093/brain/awr033
Google Scholar | Crossref | Medline

13. Westlake, KP, Nagarajan, SS. Functional connectivity in relation to motor performance and recovery after stroke. Front Syst Neurosci. 2011;5:112. doi:10.3389/fnsys.2011.00008
Google Scholar | Crossref | Medline

14. Carter, AR, Astafiev, SV, Lang, CE, et alResting inter-hemispheric fMRI connectivity predicts performance after stroke. Ann Neurol. 2010;67:365375. doi:10.1002/ana.21905
Google Scholar | Crossref | Medline | ISI

15. Siegel, JS, Ramsey, LE, Snyder, AZ, et alDisruptions of network connectivity predict impairment in multiple behavioral domains after stroke. Proc Natl Acad Sci U S A. 2016;113:E4367E4376. doi:10.1073/pnas.1521083113
Google Scholar | Crossref | Medline | ISI

16. Kuceyeski, A, Kamel, H, Navi, BB, Raj, A, Iadecola, C. Predicting future brain tissue loss from white matter connectivity disruption in ischemic stroke. Stroke. 2014;45:717722.
Google Scholar | Crossref | Medline

17. Kuceyeski, A, Navi, BB, Kamel, H, et alExploring the brain’s structural connectome: a quantitative stroke lesion-dysfunction mapping study. Hum Brain Mapp. 2015;36:21472160. doi:10.1158/1541-7786.MCR-15-0224
Google Scholar | Crossref | Medline

18. Kuceyeski, A, Navi, BB, Kamel, H, et alStructural connectome disruption at baseline predicts 6-months post-stroke outcome. Hum Brain Mapp. 2016;37:25872601. doi:10.1002/hbm.23198
Google Scholar | Crossref | Medline

19. Kuceyeski, A, Maruta, J, Relkin, N, Raj, A. The Network Modification (NeMo) tool: elucidating the effect of white matter integrity changes on cortical and subcortical structural connectivity. Brain Connect. 2013;3:451463. doi:10.1089/brain.2013.0147
Google Scholar | Crossref | Medline

20. Wang, Y, Fan, Y, Bhatt, P, Davatzikos, C. High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. Neuroimage. 2010;50:15191535. doi:10.1016/j.neuroimage.2009.12.092
Google Scholar | Crossref | Medline | ISI

21. Cohen, JR, Asarnow, RF, Sabb, FW, et alDecoding continuous variables from neuroimaging data: basic and clinical applications. Front Neurosci. 2011;5:75. doi:10.3389/fnins.2011.00075
Google Scholar | Crossref | Medline

22. Wang, J, Yu, L, Wang, J, Guo, L, Gu, X, Fang, Q. Automated Fugl-Meyer assessment using SVR modelPaper presented at: 2014 IEEE International Symposium on Bioelectronics and Bioinformatics (IEEE ISBB 2014)April 11-14, 2014Chung Li, Taiwan. doi:10.1109/ISBB.2014.6820907
Google Scholar | Crossref

23. Kim, WS, Cho, S, Baek, D, Bang, H, Paik, NJ. Upper extremity functional evaluation by Fugl-Meyer assessment scoring using depth-sensing camera in hemiplegic stroke patients. PLoS One. 2016;11:e0158640. doi:10.1371/journal.pone.0158640
Google Scholar | Crossref | Medline | ISI

24. Rondina, JM, Filippone, M, Girolami, M, Ward, NS. Decoding post-stroke motor function from structural brain imaging. Neuroimage Clin. 2016;12:372380. doi:10.1016/j.nicl.2016.07.014
Google Scholar | Crossref | Medline

25. van Os, HJA, Ramos, LA, Hilbert, A, et alPredicting outcome of endovascular treatment for acute ischemic stroke: potential value of machine learning algorithms. Front Neurol. 2018;9:784. doi:10.3389/fneur.2018.00784
Google Scholar | Crossref | Medline

26. Zou, H, Hastie, T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67:301320. doi:10.1111/j.1467-9868.2005.00503.x
Google Scholar | Crossref | ISI

27. Gladstone, DJ, Danells, CJ, Black, SE. The Fugl-Meyer assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabil Neural Repair. 2002;16:232240.
Google Scholar | SAGE Journals | ISI

28. Duncan, PW, Propst, M, Nelson, SG. Reliability of the Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident. Phys Ther. 1983;63:16061610. doi:10.1093/ptj/63.10.1606
Google Scholar | Crossref | Medline | ISI

29. Harvey, RL, Edwards, D, Dunning, K, et alRandomized sham-controlled trial of navigated repetitive transcranial magnetic stimulation for motor recovery in stroke. Stroke. 2018;49:21382146. doi:10.1161/STROKEAHA.117.020607
Google Scholar | Crossref | Medline

30. Hannula, H, Ilmoniemi, RJ. Basic principles of navigated TMS. In: Krieg, SM , ed. Navigated Transcranial Magnetic Stimulation in Neurosurgery. Cham, SwitzerlandSpringer2017:329. doi:10.1007/978-3-319-54918-7_1
Google Scholar | Crossref

31. Rossini, PM, Burke, D, Chen, R, et alNon-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: basic principles and procedures for routine clinical and research application. An updated report from an I.F.C.N. Committee. Clin Neurophysiol. 2015;126:10711107. doi:10.1016/j.clinph.2015.02.001
Google Scholar | Crossref | Medline | ISI

32. Manganotti, P, Acler, M, Masiero, S, Del Felice, A. TMS-evoked N100 responses as a prognostic factor in acute stroke. Funct Neurol. 2015;30:125130. doi:10.11138/FNeur/2015.30.2.125
Google Scholar | Crossref | Medline

33. Jain, AK, Mao, J, Mohiuddin, KM. Artificial neural networks: a tutorial. Computer (Long Beach Calif). 1996;29:3144. doi:10.1109/2.485891
Google Scholar | Crossref | ISI

34. Hsu, CW, Chang, CC, Lin, CJ. A practical guide to support vector classification Accessed February 13, 2020.
Google Scholar

35. Breiman, L, Friedman, JH, Stone, CJ, Olshen, RA. Classification algorithms and regression trees Accessed February 13, 2020.
Google Scholar

36. Breiman, L. Random forests. Mach Learn. 2001;45:532.
Google Scholar | Crossref | ISI

37. Page, SJ, Fulk, GD, Boyne, P. Clinically important differences for the upper-extremity Fugl-Meyer scale in people with minimal to moderate impairment due to chronic stroke. Phys Ther. 2012;92:791798. doi:10.2522/ptj.20110009
Google Scholar | Crossref | Medline | ISI

38. Chawla, NV, Bowyer, KW, Hall, LO, Kegelmeyer, WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321357. doi:10.1613/jair.953
Google Scholar | Crossref | ISI

39. Santos, MS, Soares, JP, Abreu, PH, Araujo, H, Santos, J. Cross-validation for imbalanced datasets: avoiding overoptimistic and overfitting approaches. IEEE Comput Intell Mag. 2018;13:5976. doi:10.1109/MCI.2018.2866730
Google Scholar | Crossref

40. Kuhn, M. Building predictive models in R using the caret package. J Stat Softw. 2008;28:126. doi:10.1053/j.sodo.2009.03.002
Google Scholar | Crossref | Medline | ISI

41. Gevrey, M, Dimopoulos, I, Lek, S. Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol Model. 2003;160:249264.
Google Scholar | Crossref | ISI

42. Cortez, P, Embrechts, MJ. Using sensitivity analysis and visualization techniques to open black box data mining models. Inf Sci. 2013;225:117. doi:10.1016/J.INS.2012.10.039
Google Scholar | Crossref

43. Burnham, KP, Anderson, DR. Multimodel inference: understanding AIC and BIC in model selection. Sociol Methods Res. 2004;33:261304. doi:10.1177/0049124104268644
Google Scholar | SAGE Journals | ISI

44. Hallett, M. Transcranial magnetic stimulation and the human brain. Nature. 2000;406:147150. doi:10.1038/35018000
Google Scholar | Crossref | Medline | ISI

45. Talelli, P, Greenwood, RJ, Rothwell, JC. Arm function after stroke: neurophysiological correlates and recovery mechanisms assessed by transcranial magnetic stimulation. Clin Neurophysiol. 2006;117:16411659. doi:10.1016/j.clinph.2006.01.016
Google Scholar | Crossref | Medline | ISI

46. Thickbroom, GW, Byrnes, ML, Archer, SA, Mastaglia, FL. Motor outcome after subcortical stroke correlates with the degree of cortical reorganization. Clin Neurophysiol. 2004;115:21442150. doi:10.1016/j.clinph.2004.04.001
Google Scholar | Crossref | Medline

47. Jo, JY, Lee, A, Kim, MS, et alPrediction of motor recovery using quantitative parameters of motor evoked potential in patients with stroke. Ann Rehabil Med. 2016;40:806815. doi:10.5535/arm.2016.40.5.806
Google Scholar | Crossref | Medline

48. Hope, TM, Seghier, ML, Leff, AP, Price, CJ. Predicting outcome and recovery after stroke with lesions extracted from MRI images. Neuroimage Clin. 2013;2:424433. doi:10.1016/j.nicl.2013.03.005
Google Scholar | Crossref | Medline

49. Varoquaux, G, Baronnet, F, Kleinschmidt, A, Fillard, P, Thirion, B. Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling. In: Jiang, T, Navab, N, Pluim, JPW, Viergever, MA, eds. Medical Image Computing and Computer-Assisted Intervention—MICCAI 2010. Berlin, GermanySpringer2010:200208Lecture Notes in Computer Science; vol 6361. doi:10.1007/978-3-642-15705-9_25
Google Scholar | Crossref

50. Prabhakaran, S, Zarahn, E, Riley, C, et alInter-individual variability in the capacity for motor recovery after ischemic stroke. Neurorehabil Neural Repair. 2008;22:6471. doi:10.1177/1545968307305302
Google Scholar | SAGE Journals | ISI

51. Stinear, CM. Prediction of motor recovery after stroke: advances in biomarkers. Lancet Neurol. 2017;16:826836. doi:10.1016/S1474-4422(17)30283-1
Google Scholar | Crossref | Medline

52. Rehme, AK, Volz, LJ, Feis, DL, et alIdentifying neuroimaging markers of motor disability in acute stroke by machine learning techniques. Cereb Cortex. 2015;25:30463056. doi:10.1093/cercor/bhu100
Google Scholar | Crossref | Medline

53. Stinear, CM, Byblow, WD, Ackerley, SJ, Barber, PA, Smith, MC. Predicting recovery potential for individual stroke patients increases rehabilitation efficiency. Stroke. 2017;48:10111019. doi:10.1161/STROKEAHA.116.015790
Google Scholar | Crossref | Medline | ISI

54. Stinear, CM, Barber, PA, Petoe, M, Anwar, S, Byblow, WD. The PREP algorithm predicts potential for upper limb recovery after stroke. Brain. 2012;135(pt 8):25272535. doi:10.1093/brain/aws146
Google Scholar | Crossref | Medline | ISI

55. Smith, MC, Ackerley, SJ, Barber, PA, Byblow, WD, Stinear, CM. PREP2 algorithm predictions are correct at 2 years poststroke for most patients. Neurorehabil Neural Repair. 2019;33:635642. doi:10.1177/1545968319860481
Google Scholar | SAGE Journals | ISI

56. 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 Scholar | Crossref | Medline | ISI

57. Ramos-Murguialday, A, Broetz, D, Rea, M, et alBrain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol. 2013;74:100108. doi:10.1002/ana.23879
Google Scholar | Crossref | Medline | ISI

58. Abdelnour, F, Voss, HU, Raj, A. Network diffusion accurately models the relationship between structural and functional brain connectivity networks. Neuroimage. 2014;90:335347. doi:10.1016/j.neuroimage.2013.12.039
Google Scholar | Crossref | Medline

59. Edwards, DJ, Cortes, M, Rykman-Peltz, A, et alClinical improvement with intensive robot-assisted arm training in chronic stroke is unchanged by supplementary tDCS. Restor Neurol Neurosci. 2019;37:167180. doi:10.3233/RNN-180869
Google Scholar | Crossref | Medline

60. Puig, J, Blasco, G, Alberich-Bayarri, A, et alResting-state functional connectivity magnetic resonance imaging and outcome after acute stroke. Stroke. 2018;49:23532360. doi:10.1161/STROKEAHA.118.021319
Google Scholar | Crossref | Medline

61. Lindenberg, R, Zhu, LL, Rüber, T, Schlaug, G. Predicting functional motor potential in chronic stroke patients using diffusion tensor imaging. Hum Brain Mapp. 2012;33:10401051. doi:10.1002/hbm.21266
Google Scholar | Crossref | Medline | ISI

62. Rüber, T, Schlaug, G, Lindenberg, R. Compensatory role of the cortico-rubro-spinal tract in motor recovery after stroke. Neurology. 2012;79:515522. doi:10.1212/WNL.0b013e31826356e8
Google Scholar | Crossref | Medline

via Machine Learning Methods Predict Individual Upper-Limb Motor Impairment Following Therapy in Chronic Stroke – Ceren Tozlu, Dylan Edwards, Aaron Boes, Douglas Labar, K. Zoe Tsagaris, Joshua Silverstein, Heather Pepper Lane, Mert R. Sabuncu, Charles Liu, Amy Kuceyeski,

, , , , , , , ,

Leave a comment

[ARTICLE] Design and Analysis of a Wearable Upper Limb Rehabilitation Robot with Characteristics of Tension Mechanism – Full Text HTML


Nowadays, patients with mild and moderate upper limb paralysis caused by cerebral apoplexy are uncomfortable with autonomous rehabilitation. In this paper, according to the “rope + toothed belt” generalized rope drive design scheme, we design a utility model for a wearable upper limb rehabilitation robot with a tension mechanism. Owing to study of the human upper extremity anatomy, movement mechanisms, and the ranges of motion, it can determine the range of motion angles of the human arm joints, and design the shoulder joint, elbow joint, and wrist joint separately under the principle of ensuring the minimum driving torque. Then, the kinematics, workspace and dynamics analysis of each structure are performed. Finally, the control system of the rehabilitation robot is designed. The experimental results show that the structure is convenient to wear on the human body, and the robot’s freedom of movement matches well with the freedom of movement of the human body. It can effectively support and traction the front and rear arms of the affected limb, and accurately transmit the applied traction force to the upper limb of the joints. The rationality of the wearable upper limb rehabilitation robot design is verified, which can help patients achieve rehabilitation training and provide an effective rehabilitation equipment for patients with hemiplegia caused by stroke.

1. Introduction

The number of young patients with functional impairment of the upper limbs caused by stroke has increased rapidly, as influenced by accelerated pace of life, poor lifestyles and environmental factors [1,2]. Limb movement disorder, which is caused by hemiplegia after stroke, not only reduces the quality of life of patients, but also brings great pain to their physiology and psychology. Effective rehabilitation training can improve the defect of patients’ nerve function and maintain the degree of joint activity; it also prevents joint spasms and enhances the final rehabilitation degree of patients’ motor functions significantly [3]. The traditional rehabilitation training is one-to-one auxiliary exercise for patients by therapists. This method is difficult to develop an effective treatment plan, and it is tough to control accurately [4]. With the development of rehabilitation robot technology and rehabilitation medicine, the rehabilitation robot has become a novel motor nerve rehabilitation treatment technology. It is of great significance to take advantage of rehabilitation robot technology for rehabilitation training to the recovery of limb function of stroke patients [5]. The traditional methods of treatment, which are based on the therapist’s clinical experience, have the problems of large staff consumption, long rehabilitation cycles, limited rehabilitation effects, and so on. The research and application of rehabilitation robot system is expected to alleviate the contradiction between supply and demand of rehabilitation medical resources effectively, and improve the quality of life of stroke patients [6,7].[…]

Continue —-> Applied Sciences | Free Full-Text | Design and Analysis of a Wearable Upper Limb Rehabilitation Robot with Characteristics of Tension Mechanism | HTML

Applsci 10 02101 g001

Figure 1. Shoulder joint freedom of motion. (a) Flexion/extension; (b) abduction/adduction; (c) internal rotation/external rotation.

, , , , , , , ,

Leave a comment

[Abstract+ References] Upper Limb Rehabilitation Electromechanical System for Stroke Patients – Conference paper


The mechanical and electrical system of upper limb rehabilitation is a kind of medical equipment which relies on the aid of machine to help stroke patients to carry out upper limb activity training. Many stroke patients can not move independently, which greatly limits their lives. In this paper, we have learned the etiology and symptoms of stroke patients, scientifically formulated their training methods and movements, and fully considered the safety and practicability of the equipment, and used relatively light materials as far as possible. For stroke patients to provide a safe, comfortable, effective upper limb wearable exoskeleton machine, can be anytime and anywhere rehabilitation training, simple appearance, low cost, suitable for stroke patients to use, to help them recover. Realize the independence of movement.


  1. 1.
    Gentile, M., Iualè, M., Mengoni, M., Germani, M.: Design of a system for upper-limb rehabilitation based on an electromechanical orthosis and sEMG wireless sensors. In: ASME International Design Engineering Technical Conferences & Computers & Information in Engineering Conference (2013)Google Scholar
  2. 2.
    Cifuentes, C., Braidot, A., Rodriguez, L., et al.: Development of a wearable ZigBee sensor system for upper limb rehabilitation robotics. In: 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE (2012)Google Scholar
  3. 3.
    Tang, B.: Application of piezoelectric patches for chatter suppression in machining processes. Measurement 138, 225–231 (2019)CrossRefGoogle Scholar
  4. 4.
    Zeiaee, A., Soltani-Zarrin, R., Langari, R., Tafreshi, R.: Design and kinematic analysis of a novel upper limb exoskeleton for rehabilitation of stroke patients. In: 2017 International Conference on Rehabilitation Robotics (ICORR) (2017)Google Scholar
  5. 5.
    Liu, H., Du, T., Wang, T., Fan, J., Qu, Y.: Design and trial operation of tele-rehabilitation gradient motor function self-evaluating system for stroke patients. Zhongguo yi liao qi xie za zhi = Chin. J. Med. Instrum. 42(2), 88–91 (2018)Google Scholar
  6. 6.
    Meng, F., Tong, K.Y., Chan, S.T., Wong, W.W., Lui, K.H., Tang, K.W., et al.: BCI-FES training system design and implementation for rehabilitation of stroke patients (2008)Google Scholar
  7. 7.
    Hesse, S., Kuhlmann, H., Wilk, J., Tomelleri, C., Kirker, S.G.B.: A new electromechanical trainer for sensorimotor rehabilitation of paralysed fingers: a case series in chronic and acute stroke patients. J. Neuroeng. Rehabil. 5(1), 21 (2008)CrossRefGoogle Scholar
  8. 8.
    Shahine, E.M., Shafshak, T.S.: Central neuroplasticity and lower limbs functional outcome following repetitive locomotor training in stroke patients. PM&R 4(10), S297–S298 (2014)Google Scholar

via Upper Limb Rehabilitation Electromechanical System for Stroke Patients | SpringerLink

, , , , , , , ,

Leave a comment

[Abstract] An interactive and innovative application for hand rehabilitation through virtual reality

Physiotherapy has been very monotonous for patients and they tend to lose interest and motivation in exercising. Introducing games with short term goals in the field of rehabilitation is the best alternative, to maintain patients’ motivation. Our research focuses on gamification of hand rehabilitation exercises to engage patients’ wholly in rehab and to maintain their compliance to repeated exercising, for a speedy recovery from hand injuries (wrist, elbow and fingers). This is achieved by integrating leap motion sensor with unity game development engine. Exercises (as gestures) are recognised and validated by leap motion sensor. Game application for exercises is developed using unity. Gamification alternative has been implemented by very few in the globe and it has been taken as a challenge in our research. We could successfully design and build an engine which would be interactive and real-time, providing platform for rehabilitation. We have tested the same with patients and received positive feedbacks. We have enabled the user to know the score through GUI.


via An interactive and innovative application for hand rehabilitation through virtual reality: International Journal of Advanced Intelligence Paradigms: Vol 15, No 3

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

Leave a comment


Recently, the functional near-infrared spectroscopy (600–900nm electromagnetic wave) (ff-NIRS)-based rehabilitation researches have been studied for understanding the human brain. Although ff-NIRS can successfully measure the relative blood concentration changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) as an assessment tool to identify significant clinical intervention during pre- and post-rehabilitation therapy for stroke survivors, there is insufficient information particularly on the use of ff-NIRS as a clinical translation in upper extremity function rehabilitation. In order to widely utilize the ff-NIRS for upper extremity rehabilitation, device information, experiment design, measurement procedure, and analyzing method are described for clinician aspect in this study. In addition, further research trend was introduced from previous studies for stroke survivor rehabilitation. The authors believed that the information provided in this study can be a useful guideline to encourage future researchers to focus on upper extremity function rehabilitation of stroke survivors.




, , , , , , , ,

Leave a comment

[REVIEW ARTICLE ] Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach – Full Text

Robot-mediated therapy is an innovative form of rehabilitation that enables highly repetitive, intensive, adaptive, and quantifiable physical training. It has been increasingly used to restore loss of motor function, mainly in stroke survivors suffering from an upper limb paresis. Multiple studies collated in a growing number of review articles showed the positive effects on motor impairment, less clearly on functional limitations. After describing the current status of robotic therapy after upper limb paresis due to stroke, this overview addresses basic principles related to robotic therapy applied to upper limb paresis. We demonstrate how this innovation is an evidence-based approach in that it meets both the improved clinical and more fundamental knowledge-base about regaining effective motor function after stroke and the need of more objective, flexible and controlled therapeutic paradigms.


Robot-mediated rehabilitation is an innovative exercise-based therapy using robotic devices that enable the implementation of highly repetitive, intensive, adaptive, and quantifiable physical training. Since the first clinical studies with the MIT-Manus robot (1), robotic applications have been increasingly used to restore loss of motor function, mainly in stroke survivors suffering from an upper limb paresis but also in cerebral palsy (2), multiple sclerosis (3), spinal cord injury (4), and other disease types. Thus, multiple studies suggested that robot-assisted training, integrated into a multidisciplinary program, resulted in an additional reduction of motor impairments in comparison to usual care alone in different stages of stroke recovery: namely, acute (57), subacute (18), and chronic phases after the stroke onset (911). Typically, patients engaged in the robotic therapy showed an impairment reduction of 5 points or more in the Fugl-Meyer assessment as compared to usual care. Of notice, rehabilitation studies conducted during the chronic stroke phase suggest that a 5-point differential represents the minimum clinically important difference (MCID), i.e., the magnitude of change that is necessary to produce real-world benefits for patients (12). These results were collated in multiple review articles and meta-analyses (1317). In contrast, the advantage of robotic training over usual care in terms of functional benefit is less clear, but there are recent results that suggest how best to organize training to achieve superior results in terms of both impairment and function (18). Indeed, the use of the robotic tool has allowed us the parse and study the ingredients that should form an efficacious and efficient rehabilitation program. The aim of this paper is to provide a general overview of the current state of robotic training in upper limb rehabilitation after stroke, to analyze the rationale behind its use, and to discuss our working model on how to more effectively employ robotics to promote motor recovery after stroke.

Upper Extremity Robotic Therapy: Current Status

Robotic systems used in the field of neurorehabilitation can be organized under two basic categories: exoskeleton and end-effector type robots. Exoskeleton robotic systems allow us to accurately determine the kinematic configuration of human joints, while end-effector type robots exert forces only in the most distal part of the affected limb. A growing number of commercial robotic devices have been developed employing either configuration. Examples of exoskeleton type include the Armeo®Spring, Armeo®Power, and Myomo® and of end-effector type include the InMotion™, Burt®, Kinarm™ and REAplan®. Both categories enable the implementation of intensive training and there are many other devices in different stages of development or commercialization (1920).

The last decade has seen an exponential growth in both the number of devices as well as clinical trials. The results coalesced in a set of systematic reviews, meta-analyses (1317) and guidelines such as those published by the American Heart Association and the Veterans Administration (AHA and VA) (21). There is a clear consensus that upper limb therapy using robotic devices over 30–60-min sessions, is safe despite the larger number of movement repetitions (14).

This technic is feasible and showed a high rate of eligibility; in the VA ROBOTICS (911) study, nearly two thirds of interviewed stroke survivors were enrolled in the study. As a comparison the EXCITE cohort of constraint-induced movement therapy enrolled only 6% of the screened patients participated (22). On that issue, it is relevant to notice the admission criteria of both chronic stroke studies. ROBOTICS enrolled subjects with Fugl-Meyer assessment (FMA) of 38 or lower (out of 66) while EXCITE typically enrolled subjects with an FMA of 42 or higher. Duret and colleagues demonstrated that the target population, based on motor impairments, seems to be broader in the robotic intervention which includes patients with severe motor impairments, a group that typically has not seen much benefit from usual care (23). Indeed, Duret found that more severely impaired patients benefited more from robot-assisted training and that co-factors such as age, aphasia, and neglect had no impact on the amount of repetitive movements performed and were not contraindicated. Furthermore, all patients enrolled in robotic training were satisfied with the intervention. This result is consistent with the literature (24).

The main outcome result is that robotic therapy led to significantly more improvement in impairment as compared to conventional usual care, but only slightly more on motor function of the limb segments targeted by the robotic device (16). For example, Bertani et al. (15) and Zhang et al. (17) found that robotic training was more effective in reducing motor impairment than conventional usual care therapy in patients with chronic stroke, and further meta-analyses suggested that using robotic therapy as an adjunct to conventional usual care treatment is more effective than robotic training alone (1317). Other examples of disproven beliefs: many rehabilitation professionals mistakenly expected significant increase of muscle hyperactivity and shoulder pain due to the intensive training. Most studies showed just the opposite, i.e., that intensive robotic training was associated with tone reduction as compared to the usual care groups (92526). These results are shattering the resistance to the widespread adoption of robotic therapy as a therapeutic modality post-stroke.

That said, not all is rosy. Superior changes in functional outcomes were more controversial until the very last years as most studies and reviews concluded that robotic therapy did not improve activities of daily living beyond traditional care. One first step was reached in 2015 with Mehrholz et al. (14), who found that robotic therapy can provide more functional benefits when compared to other interventions however with a quality of evidence low to very low. 2018 may have seen a decisive step in favor of robotic as the latest meta-analysis conducted by Mehrholz et al. (27) concluded that robot-assisted arm training may improve activities of daily living in the acute phase after stroke with a high quality of evidence However, the results must be interpreted with caution because of the high variability in trial designs as evidenced by the multicenter study (28) in which robotic rehabilitation using the Armeo®Spring, a non-motorized device, was compared to self-management with negative results on motor impairments and potential functional benefits in the robotic group.

The Robot Assisted Training for the Upper Limb after Stroke (RATULS) study (29) might clarify things and put everyone in agreement on the topic. Of notice, RATULS goes beyond the Veterans Administration ROBOTICS with chronic stroke or the French REM_AVC study with subacute stroke. RATULS included 770 stroke patients and covered all stroke phases, from acute to chronic, and it included a positive meaningful control in addition to usual care.[…]


Continue —-> Frontiers | Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach | Neurology

, , , , , , , , , ,

Leave a comment

[Conference Paper] HoVRS: Home-based Virtual Rehabilitation System – Full Text


After stroke, sustained hand rehabilitation training is required for continuous improvement and maintenance of distal function. In this paper, we present a system designed and implemented in our lab:  the Home-based Virtual Rehabilitation System (HoVRS). Eleven subjects with chronic stroke were recruited to test the feasibility of the system and refine its design and the training protocol to prepare for a future efficacy study. HoVRS was placed at subjects’ homes, and subjects were asked to use the system at least 15 minutes every weekday for 3 months (12 weeks) with limited technical support and remote clinical monitoring.  All subjects completed the study without any adverse events.  Subjects on average spent 12 hours using the system. Nine out of the eleven subjects improved on the Box and Blocks Test (BBT), and ten improved on the Upper Extremity Fugl-Meyer Assessment (FM) and the Action Research Arm Test (ARAT). The outcomes of this pilot study warrant further investigation of the system’s ability to promote recovery of hand function in subacute and chronic stroke.


Stroke is a leading cause of serious long-term disability in the United States.  The incidence of new or recurrent stroke in the US is 795,000 per year and the prevalence of chronic stroke is approximately 7 million (Go et al., 2014). Projections show that by 2030, an additional 3.4 million people or 3.88% of U.S. adults 18 and older will have had a stroke, a 20.5% increase from 2012 (American Stroke Association 2018). At six months post-stroke, about 65% of affected persons continue to have hand deficits that profoundly affect their ability to perform their usual activities and their independence (Dobkin, 2005; Lang, et al. 2006). This leaves a potential market segment of approximately 640,000 persons that may need long-term arm and hand rehabilitation. Restoration of hand function is of utmost importance since it is the loss of hand function that profoundly decreases quality of life by limiting the ability to perform feeding, dressing, and grooming, and further may limit the use of assistive as well as telecommunications technology (Brown et al. 1987, Grimby et al. 1998, Andren et al. 2004, Kwakkel et al. 2008).
Therapy in an inpatient rehabilitation center only lasts about 2-3 weeks. As outpatients, stroke survivors are typically only seen two to three times a week for short time periods. This volume of intervention falls far short of the hundreds of hours needed to re-establish normal hand function.  Recently published results of innovative lab-based interventions appear to have a similar problem (Lang et al., 2015, Winstein et al., 2016). It is therefore imperative to develop an intervention that can be delivered at home over a sufficient period of time to elicit improvements.
Innovative telerehabilitation systems have been developed using information and communication technologies to provide rehabilitation services at a distance. Many studies have developed video-game driven systems from commercially available gaming consoles such as Wii and Microsoft Kinect (Metcalf et. al, 2013), however, these systems do not address hand rehabilitation. Other groups, including members of our own team, have examined the use of custom-made telerehabilitation systems (Adamovich et. al, 2005, Turolla et. al., 2013) but they are not commercially available.  An ideal home-based telerehabilitation system has to be low cost, easy to setup, able to motivate the user for everyday use, generate progress reports for the user for self-tracking, and provide daily monitoring to remote clinicians. Exciting new technologies have now made this approach possible and hold promise for long-term benefit. These technological advances – for the first time – allow for virtual reality simulations interfaced with discrete finger and hand tracking that are affordable and easy to use.
Our product, the Home Virtual Rehabilitation System (HoVRS), provides intense upper extremity rehabilitation at home. It will allow patients to access hand/arm rehabilitation without the cost and transportation challenges associated with outpatient rehabilitation. HoVRS will consist of five elements:  1) an infrared camera specifically designed to capture finger and arm movements – a substantial improvement over rehabilitation activities provided by commercial game consoles like Kinect or Wii, 2) multiple engaging games that train the hand and arm using commercial gaming mechanics designed to optimize players’ motivation to perform these activities for long periods of time, 3) an optional exoskeleton designed to assist the patient’s arm as it moves against gravity (use of this support can be weaned and eliminated as patients get stronger),  4) monitoring and archiving software that will allow clinicians to design custom rehabilitation interventions, track a patient’s progress, and modify a patient’s rehabilitation program, in-person or remotely, and 5) a secure wireless data connector to collect detailed information on patient movement in real time. The secure communication channel will allow for remote monitoring by clinicians, remote technical support, and remote patient and clinician interaction face to face, while the patient uses HoVRS.
This study describes the experiences of the first eleven persons with stroke that participated in pilot testing of HoVRS in their homes.[…]


Continue —->  HoVRS: Home-based Virtual Rehabilitation System

Figure 1. HoVRS architecture design

, , , , , , , , ,

Leave a comment

[Conference Paper] Development of a Soft Inflatable Structure with Variable Stiffness for Hand Rehabilitation – Full Text PDF


Stroke is one of the main causes of death worldwide,
more precisely, in the UK, stroke is classified as the
fourth cause of death; around 7% (40,000 people per
year) of all deaths are caused by stroke [1-2]. Survivors,
estimated between 55% and 75%, suffer from a number
of disabilities, including those affecting the upper limbs.
In order to recover the lost ability or parts of it, a
rehabilitation programme is usually recommended. In
order to improve their mobility, patients affected by
neurological disorders caused by stroke need assisted
rehabilitation therapy; indeed, physiotherapists plan a
rehabilitation programmes based on intensive daily
training of repetitive movements.
In the last decade, a large number of ‘roboticists’ focused
their research on the development of devices that could
be used for post-stroke rehabilitation.
Different approaches have been explored, the three main
concepts are based on robotic assisted devices, pneumatic
devices and Virtual Reality (VR) based devices or a
combination of them.
Hadi et al. [3] developed a wearable glove for hand
rehabilitation and assistance using shape memory alloys,
able to exert more than 40N at the finger’s tip. While,
Connelly et al. [4] used a different approach creating a
pneumatic glove that works with pressurized air up to
10psi, paired with a virtual environment representation
(using a VR headset) allowing patients to perform
different exercises. Along similar lines, Alamri et al. [5]
proposed 5 different VR based exercises that could be
performed employing the CyberGrasp System [6]
composed of three different hardware pieces: the
CyberGlove, which allows to read the spatial hand
coordinates and recreates a realistic virtual avatar; the
CyberGrasp providing force feedback at the fingers tips
of the patient, and the CyberForce armature that
simulates inertia in order to give a more realistic
experience to the patient.
A simpler solution has been presented by Sebastian et al.
[7] where a soft robotic haptic interface with variable
stiffness, made of silicon material combined with a
Kevlar threading is used; this system is grabbed by the
patient and can work in two different modalities (1)
isometric and (2) constant pressure.
In our paper, a similar approach to the last presented will
be introduced – a low-cost and user-friendly design of a
soft inflatable structure with adjustable stiffness has been
chosen that can be used in clinical or home settings. […]

Image result for Development of a Soft Inflatable Structure with Variable Stiffness for Hand Rehabilitation

Figure 1 Soft Inflatable Device; Folded state (left), inflated
state (right)

Download Full Text PDF


, , , , ,

Leave a comment

[ARTICLE] Comparing Home Upper Extremity Activity with Clinical Evaluations of Arm Function in Chronic Stroke – Full Text PDF



To determine if clinical evaluations of post-stroke arm function correspond to everyday motor performance indexed by arm accelerometers.


Cross-sectional study analyzing baseline data from a larger trial (NCT02665052). Setting: Outpatient research center.


Twenty community-dwelling adults with chronic arm motor deficits (stroke≥6mo). Intervention: 72-hours of home wrist-worn accelerometry during normal routine.

Main Outcome Measures

Clinical evaluations included the Fugl-Meyer (FM), Action Research Arm Test (ARAT), Wolf Motor Function Test (WMFT), and two self-assessments: the Motor Activity Log (MAL) and hand motor subscale of the Stroke Impact Scale (SIS). Accelerometer-derived variables included quantifications of movement intensity (magnitude) and duration of arm use.


Participants had moderate arm impairment (FM 36.1 ± 9.4). The accelerometer-derived mean magnitude ratio correlated significantly with the FM (ρ = 0.60, p < 0.01), WMFT functional score (ρ = 0.59, p < 0.01), and ARAT (ρ = 0.50, p < 0.05). The hours of use ratio correlated with the MAL amount of use (ρ = 0.58, p < 0.01) and quality of movement (ρ = 0.61, p < 0.01). Total paretic hours did not correlate with the FM, WMFT or ARAT, and intensity variables did not correlate with the MAL or SIS.


Participants with higher baseline function had greater intensity of paretic arm movement at home; similarly, those who perceived they had less disability used their paretic arm more relative to their non-paretic arm. However, some participants with higher clinical scores did not exhibit greater arm use in everyday life, possibly due to neglect and learned non-use. Therefore, individualized home accelerometry profiles could provide valuable insight to better tailor post-stroke rehabilitation.

via Comparing Home Upper Extremity Activity with Clinical Evaluations of Arm Function in Chronic Stroke – ScienceDirect

, , , , , , , , ,

Leave a comment

%d bloggers like this: