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http://dx.doi.org/10.20540/JIAPTR.2020.11.2.2021

Effects of Robot-Mediated Gait Training Combined with Virtual Reality System on Muscle Activity: A Case Series Research  

Heo, Seoyoon (Department of Occupational Therapy, School of Medical and Health Care, Kyungbok University)
Kim, Mooki (Department of Occupational Therapy, Pohang University)
Choi, Wansuk (Department of Physical Therapy, International University of Korea)
Publication Information
Journal of International Academy of Physical Therapy Research / v.11, no.2, 2020 , pp. 2021-2027 More about this Journal
Abstract
Background: Previous robot-mediated gait training has been proven several limitations such as pointless repeated motion training, decreased presence, etc. In this research, adult stroke patients were participated in robot-mediated gait training accompanied with or without virtual reality program. Objectives: Exploring whether the results indicated virtual reality system has contribution to muscle strength and balance ability. Design: A case series research, cross-over trial. Methods: Eleven participants (male 4, female 7) with adults diagnosed as stroke from medical doctor ware engaged. The participants received 2 treatment sessions of identical duration, robot-assisted gait training with virtual reality and robot-assisted gait training with screen-off randomly crossed over include 1-day for each person of wash-out period. The parameter was muscle activity, the researchers assessed sEMG (surface electromyography). Results: The result showed less muscle activities during training in robot-assisted gait training with virtual reality circumstances, and these indicated muscles were gluteus medius muscle, vastus medialis muscle, vastus intermedius and vastus lateralis muscle, semimembranosus muscle, gastrocnemius-lateral head, and soleus muscle (P<.05). Conclusion: In this study, we analyzed the outcome of muscle activity for clinical inference of robot-assisted gait training with virtual reality (VR). Less muscle activity was measured in the treatment accompanied by VR, therefore, a more systematic, in-depth and well-founded level of follow-up research is needed.
Keywords
Virtual reality; Stroke; End-effector; Robot-mediated ambulation training;
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1 Munari D, Fonte C, Varalta V, et al. Effects of robot-assisted gait training combined with virtual reality on motor and cognitive functions in patients with multiple sclerosis: A pilot, single-blind, randomized controlled trial. Restor Neurol Neurosci. 2020;38(2):151-164.   DOI
2 Shahab M, Raisi M, Hejrati M, Taheri AR, Meghdari A. Virtual Reality Robot for Rehabilitation of Children with Cerebral Palsy (CP). In: IEEE conference on Robotics and Mechatronics (ICRoM), 2019.
3 Patel J. Virtual Reality and Robotic Based Training for the Upper Limb in the Acute and Early Sub-acute Periods Post-stroke [PhD thesis]. New Jersey, USA: Rutgers The State University of New Jersey, Rutgers School of Health Professions; 2020.
4 Sorrento GU, Archambault PS, Fung J. The Effects of a Virtual Environment and Robot Generated Haptic Forces on the Coordination of the Lower Limb During Gait in Chronic Stroke Using Planar and 3D Phase Diagrams. In: IEEE conference on Virtual Rehabilitation (ICVR), 2019.
5 Cram JR. Cram's Introduction to Surface Electromyography. 2nd ed. Burlington: Jones & Bartlett Learning; 2010.
6 Kwakkel G, Kollen BJ, Krebs HI. Effects of Robot-Assisted Therapy on Upper Limb Recovery After Stroke: A Systematic Review. Neurorehabil Neural Repair. 2008;22(2):111-121.   DOI
7 Heo SY, Lee HJ, Ham AJ, Kim YN, Jeong SN, Kim KM. The Effects of Virtual Reality Therapy on Executive Function and Balance for Stroke Patients: A Randomized Controlled Clinical Trial. J Korean Soc Occup Ther. 2016;24(4):1-14.   DOI
8 Lemmens RJ, Timmermans AA, Janssen-Potten YJ, Smeets RJ, Seelen HA. Valid and reliable instruments for arm-hand assessment at ICF activity level in persons with hemiplegia: a systematic review. BMC Neurol. 2012;12(1):21.   DOI
9 Ali M, Atula S, Bath PM, et al. Stroke outcome in clinical trial patients deriving from different countries. Stroke. 2009;40(1):35-40.   DOI
10 Van Delden AEQ, Peper CE, Beek PJ, Kwakkel G. Unilateral versus bilateral upper limb exercise therapy after stroke: a systematic review. J Rehabil Med. 2012;44(2):106-117.   DOI
11 Hoeger WWK, Hopkins DR, Barette SL, Hale DF. Relationship between Repetitions and Selected percentages of One Repetition Maximum: A Comparison between Untrained and Trained Males and Females. J Strength Cond Res. 1990;4(2):47-54.   DOI
12 Heller J, Peric T, Dlouha R, Kohlikova E, Melichna J, Novakova H. Physiological profiles of male and female taekwon-do (ITF) black belts. J Sports Sci. 1998;16(3):243-249.   DOI
13 Schick EE, Coburn JW, Brown LE, et al. A Comparison of Muscle Activation Between a Smith Machine and Free Weight Bench Press. J Strength Cond Res. 2010;24(3):779-784.   DOI
14 Yoo JH, Hwang D, Moon KY, Mark SN. Automated Human Recognition by Gait using Neural Network. In: IEEE conference on Robotics and Mechatronics (ICRoM), 2019.
15 Zhang M, Davies TC, Xie S. Effectiveness of robot-assisted therapy on ankle rehabilitation - a systematic review. J Neuroeng Rehabil. 2013;10:30.   DOI
16 American College of Sports Medicine. American College of Sports Medicine position stand. Progression models in resistance training for healthy adults. Med Sci Sports Exerc. 2009;41(3):687-708.   DOI
17 Ancillao A. Modern Functional Evaluation Methods for Muscle Strength and Gait Analysis. Springer; 2018.
18 Criswell E, Cram JR, eds. Cram's Introduction to Surface Electromyography. 2nd ed. Jones and Bartlett; 2011.
19 Adams JM, Cerny K. Observational Gait Analysis. Slack Incorporated; 2018
20 Williams J. The Declaration of Helsinki and public health. Bull World Health Organ. 2008;86(8):650-651.   DOI
21 Nishimura RA, Trusty JM, Hayes DL, et al. Dual-Chamber Pacing for Hypertrophic Cardiomyopathy: A Randomized, Double-Blind, Crossover Trial. J Am Coll Cardiol. 1997;29(2):435-441.   DOI
22 Green AJ, Gelfand JM, Cree BA, et al. Clemastine fumarate as a remyelinating therapy for multiple sclerosis (ReBUILD): a randomised, controlled, double-blind, crossover trial. The Lancet. 2017;390(10111):2481-2489.   DOI
23 Friede T, Posch M, Zohar S, et al. Recent advances in methodology for clinical trials in small populations: the InSPiRe project. OrphanetJ Rare Dis. 2018;13(1):186.   DOI
24 Nishimura RA, Trusty JM, Hayes DL, et al. Dual-Chamber Pacing for Hypertrophic Cardiomyopathy: A Randomized, Double-Blind, Crossover Trial. J Am Coll Cardiol. 1997;29(2):435-441.   DOI
25 Kempen TGH, Bertilsson M, Lindner K-J, et al. Medication Reviews Bridging Healthcare (MedBridge): Study protocol for a pragmatic cluster-randomised crossover trial. Contemp Clin Trials. 2017;61:126-132.   DOI
26 Tim Joda, Urs Bragger. Patient-centered outcomes comparing digital and conventional implant impression procedures: a randomized crossover trial. Clin Oral Implants Res. 2016;27(12):e185-e189.   DOI
27 Kashi S, Feingold-Polak R, Lerner B, Rokach L, Levy-Tzedek S. A machine-learning model for automatic detection of movement compensations in stroke patients. In: IEEE Transactions on Emerging Topics in Computing, 2020.
28 Tepe Victoria, Charles M. Peterson. Full stride: advancing the state of the art in lower extremity Gait systems. NY: Springer; 2017.
29 Stergiou N. Biomechanics and Gait Analysis. Academic Press; 2020.
30 Baker RW. Measuring Walking: A Handbook of Clinical Gait Analysis. London: Mac Keith Press; 2013.
31 Williams JR. The Declaration of Helsinki and public health. Bull World Health Organ. 2008;86:650-652.   DOI
32 Keramas JG. Robot Technology Fundamentals. Delmar Learning; 1998:398.
33 Timmermans AA, Spooren AI, Kingma H, Seelen HA. Influence of task-oriented training content on skilled arm-hand performance in stroke: a systematic review. Neurorehabil Neural Repair. 2010;24(9):858-870.   DOI
34 Francesco T, Antonino C, Maria FP, et al. A Case Report on Intensive, Robot-Assisted Rehabilitation Program for Brainstem Radionecrosis. Medicine (Baltimore). 2020;99(10):e19517.   DOI
35 Zhao Y, Liang C, Gu Z, Zheng Y, Wu Q. A New Design Scheme for Intelligent Upper Limb Rehabilitation Training Robot. Int J Environ Res Public Health. 2020;17(8):2948.   DOI