ORIGINAL RESEARCH

Effect of robot-assisted gait training on biomechanics of ankle joint in patients with post-stroke hemiparesis

About authors

Research Center of Neurology, Moscow, Russia

Correspondence should be addressed: Anton S. Klochkov
Volokolamskoe shosse, 80, Moscow, 125367; ur.ygoloruen@vokhcolk

About paper

Funding: this study was state-funded.

Compliance with ethical standards: the study was approved by the Ethics Committee of the Research Center of Neurology (Protocol № 14/09 dated December 23, 2009). Informed consent was obtained from all study participants.

Author contribution: Klochkov AS — study planning, patient recruitment, literature analysis, data interpretation, manuscript preparation; Zimin AA — statistical analysis, data interpretation, manuscript preparation; Khizhnikova AE — literature analysis, data interpretation, manuscript preparation; Suponeva NA, Piradov MA — manuscript preparation.

Received: 2020-09-28 Accepted: 2020-10-14 Published online: 2020-10-30
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  1. Balaban B, Tok F. Gait Disturbances in Patients With Stroke. J PM&R. 2014; 6 (7): 635–42.
  2. Beyaert C, Vasa R, Frykberg GE. Gait post-stroke: Pathophysiology and rehabilitation strategies. J Neurophysiol Clin. 2015; 45 (4–5): 335–55.
  3. Jørgensen HS, Nakayama H, Raaschou H, et al. Recovery of walking function in stroke patients: The copenhagen stroke study. J Arch Phys Med Rehabil. 1995; 76 (1): 27–32.
  4. Mehrholz J, Thomas S, Werner C, et al. Electromechanical-assisted training for walking after stroke. Cochrane Database Syst Rev. 2017: 5.
  5. Skvortsov DV. Klinicheskiy analiz dvizheniy. Analiz pokhodki. Ivanovo: Stimul, 1996; 344 s. Russian.
  6. Kim CM, Eng JJ. Magnitude and pattern of 3D kinematic and kinetic gait profiles in persons with stroke: relationship to walking speed. Gait Posture. 2004; 20 (2): 140–6.
  7. Milot M-H, Nadeau S, Gravel D. Muscular utilization of the plantarflexors, hip flexors and extensors in persons with hemiparesis walking at self-selected and maximal speeds. J Electromyogr Kinesiol. 2007; 17 (2): 184–193.
  8. Sadeghi H, Allard P, Duhaime M. Muscle power compensatory mechanisms in below-knee amputee gait. Am J Phys Med Rehabil. 2001; 80 (1): 25–32.
  9. Brunnstrom S. Movement Therapy in Hemiplegia: A Neurophysiological Approach. Harper & Row. 1970; 192.
  10. Bruni MF, Corrado M, De Cola MC, et al. What does best evidence tell us about robotic gait rehabilitation in stroke patients: A systematic review and meta-analysis. J Clin Neurosci. 2018; 48: 11–17.
  11. Tan CH, Kadone H, Watanabe H, Marushima A, et al. Lateral Symmetry of Synergies in Lower Limb Muscles of Acute Post-stroke Patients After Robotic Intervention. Frontiers in Neuroscience. 2018; 12: 276.
  12. Pismennaya EV, Petrushanskaya KA, Kotov SV, et al. Clinical and biomechanical foundation of application of the exoskeleton exoatlet at walking of patients with poststroke disturbances. Russian Journal of biomechanics. 2019; 23 (2): 204–30. Russian.
  13. Vukobratovíc M, Borovac B. Zero-moment point-thirty five years of its life. International Journal of Humanoid Robotics. 2004; 1 (1): 157–73.
  14. Mokhtari M, Taghizadeh M, Mazare M. Hybrid Adaptive Robust Control Based on CPG and ZMP for a Lower Limb Exoskeleton. Robotica. 2020: 1–19.
  15. Al-Shuka H, Corves B, Vanderborght B, et al. Zero-Moment Point- Based Biped Robot with Different Walking Patterns. International Journal of Intelligent Systems and Applications (IJISA). 2015; 7: 31–41.
  16. Erbatur K, Kurt O. Natural ZMP Trajectories for Biped Robot Reference Generation. IEEE Transactions on Industrial Electronics. 2009; 56 (3): 835–45.
  17. Schwartz I, Meiner Z. Robotic-Assisted Gait Training in Neurological Patients: Who May Benefit? Ann Biomed Eng. 2015; 43 (5): 1260–9.
  18. Moucheboeuf G, Griffier R, Gasq D. Effects of robotic gait training after stroke: a meta-analysis. Ann Phys Rehabil Med. 2020; S.1877–0657(20)30065-8. DOI: 10.1016/j.rehab.2020.02.008.
  19. Aprile I, Iacovelli C, Goffredo M, et al. Efficacy of end-effector Robot- Assisted Gait Training in subacute stroke patients: Clinical and gait outcomes from a pilot bi-centre study. NeuroRehabilitation. 2019; 45 (2): 201–12.
  20. De Luca A, Vernetti H, Capra C, et al. Recovery and compensation after robotic assisted gait training in chronic stroke survivors. Disabil Rehabil Assist Technol. 2019; 14 (8): 826–38.
  21. Klochkov AS, Telenkov AA, Chernikova LA. Effect of Lokomat trainings on the severity of gait disorders in patients after stroke. Annals of Clinical and Experimental Neurology. 2011; 5 (3): 20–25.
  22. Neckel ND, Blonien N, Nichols D, et al. Abnormal joint torque patterns exhibited by chronic stroke subjects while walking with a prescribed physiological gait pattern. J Neuroeng Rehabil. 2008; 5 (1): 1–13.
  23. Suponeva NA, Yusupova DG, Zhirova ES, at al. Validation of the modified Rankin Scale in Russia. J Neurology, Neuropsychiatry, Psychosomatics. 2018; 10 (4): 36–39.
  24. Suponeva NA, Yusupova DG, Ilyina KA, et al. Validation of the Modified Ashworth scale in Russia. J Annals of clinical and experimental neurology. 2020; 14 (1): 89–96.
  25. Shpakov AV, Artamonov AA, Orlov DO, i dr. Novye podhody v obrabotke biomehanicheskih harakteristik lokomocij cheloveka, poluchennyh s ispol'zovaniem videoanaliza dvizhenij. Upravlenie dvizheniem Motor Control 2020 materialy VIII Rossijskoj s mezhdunarodnym uchastiem konferencii po upravleniju dvizheniem. 2020; 65–66.
  26. Docenko VI, Voronov AV, Titarenko NYu, i dr. Komp'juternyj videoanaliz dvizhenij v sportivnoj medicine i nejroreabilitacii. Medicinskij alfavit. 2005; 3: 12–14.
  27. Ferrarin M, Bovi G, Rabuffetti M, et al. Gait pattern classification in children with Charcot-Marie-Tooth disease type 1A. Gait and Posture. 2012; 35: 131–7.
  28. Kaczmarczyk K, Wit A, Krawczyk M, et al. Gait classification in poststroke patients using artificial neural networks. Gait and Posture. 2009; 30 (2): 207–10.
  29. Toro B, Nester CJ, Farren PC. Cluster analysis for the extraction of sagittal gait patterns in children with cerebral palsy. Gait and Posture. 2007; 25: 157–65.
  30. Giacomozzi C, Martelli F, Nagel A, et al. Cluster analysis to classify gait alterations in rheumatoid arthritis using peak pressure curves. Gait and Posture. 2009; 29: 220–4.
  31. Fong-Chin S, Wen-Lan W, Yuh-Min C, et al. Fuzzy clustering of gait patterns of patients after ankle arthrodesis based on kinematic parameters. Med Eng Phys. 2001; 23: 83–90.
  32. Rozumalski A, Schwartz M. Crouch gait patterns defined using k-means cluster analysis are related to underlying clinical pathology. Gait and Posture. 2009; 30: 155–60.
  33. Mulroy S, Gronley J, Weiss W, et al. Use of cluster analysis for gait pattern classification of patients in the early and late recovery phases following stroke. Gait and Posture. 2003; 18: 114–25.
  34. Phinyomark A, Osis S, Hettinga BA, Ferber R. Kinematic gait patterns in healthy runners: A hierarchical cluster analysis. J Biomech. 2015; 48 (14): 3897–904.
  35. Watari R, Osis ST, Phinyomark A, Ferber R. Runners with patellofemoral pain demonstrate sub-groups of pelvic acceleration profiles using hierarchical cluster analysis: an exploratory cross-sectional study. BMC Musculoskeletal Disorders. 2018; 19: 120.
  36. Trompetto C, Marinelli L, Mori L, et al. Postactivation depression changes after robotic-assisted gait training in hemiplegic stroke patients. Gait Posture. 2013; 38 (4): 729–33.
  37. Skvortsov DV. Diagnostika dvigatel'noy patologii instrumental'nymi metodami: analiz pokhodki stabilometriya. М., 2007; 640 s.
  38. Vitenson AS, Petrushanskaya KA. Physiological foundations of a method of artificial correction of movements by means of programmable electrical stimulation of muscles during walking. Russian Journal of biomechanics. 2005; 9 (1): 7–26.
  39. Vorontcova OI, Lozovskaya MV. Structure of gait cycle based on kinetic and kinematical parameters. Journal of new medical technologies. 2017; 3: 9–15.
  40. Bonnyaud C, Zory R, Boudarham J, et al. Effect of a robotic restraint gait training versus robotic conventional gait training on gait parameters in stroke patients. Exp Brain Res. 2014; 232 (1): 31–42.