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neuroreha 2017; 09(04): 167-171
DOI: 10.1055/s-0043-120318
Schwerpunkt Robotik
Georg Thieme Verlag KG Stuttgart · New York

Roboterassistierte Gangrehabilitation bei Patienten mit Hirn- und Rückenmarkverletzungen

Eva Swinnen
,
Nina Lefeber
,
Emma De Keersmaecker
,
Eric Kerckhofs
Further Information

Publication History

Publication Date:
08 December 2017 (online)

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Zusammenfassung

Computergestütztes Gehtraining unterstützt Patienten dabei, die Gehfähigkeit wiederzuerlangen. Doch welche Feinheiten sich hinter dieser Aussage verbergen, beleuchtet der folgende Artikel.

 

  • Literatur

  • 1 Beer S, Aschbacher B, Manoglou D. et al. Robot-assisted gait training in multiple sclerosis: A pilot randomized trial. Mult Scler 2008; 2: 231-236
    Google Scholar
  • 2 Calabro RS, Cacciola A, Berte F. et al. Robotic gait rehabilitation and substitution devices in neurological disorders: Where are we now?. Neurol Sci 2016; 4: 503-514
    Google Scholar
  • 3 Darekar A, McFadyen BJ, Lamontagne A. et al. Efficacy of virtual reality-based intervention on balance and mobility disorders post-stroke: A scoping review. J Neuroeng Rehabil 2015; 46
    PubMedGoogle Scholar
  • 4 David D, Regnaux JP, Lejaille M. et al. Oxygen consumption during machine-assisted and unassisted walking: A pilot study in hemiplegic and healthy humans. Arch Phys Med Rehabil 2006; 4: 482-489
    Google Scholar
  • 5 Delussu AS, Morone G, Iosa M. et al. Physiological responses and energy cost of walking on the Gait Trainer with and without body weight support in subacute stroke patients. J Neuroeng Rehabil 2014; 54
    PubMedGoogle Scholar
  • 6 Feys P. Potential of robot-assisted therapy for disabled persons with MS. Mult Scler 2016; 3: 264-265
    Google Scholar
  • 7 Foundation NS. Clinical Guidelines for Stroke Management 2010. Melbourne, Australia:
    Google Scholar
  • 8 Galli M, Cimolin V, De Pandis MF. et al. Robot-assisted gait training versus treadmill training in patients with Parkinson’s disease: A kinematic evaluation with gait profile score. Funct Neurol 2016; 3: 163-170
    Google Scholar
  • 9 Gandolfi M, Geroin C, Picelli A. et al. Robot-assisted vs. sensory integration training in treating gait and balance dysfunctions in patients with multiple sclerosis: A randomized controlled trial. Front Hum Neurosci 2014; 318
    Google Scholar
  • 10 Hesse S. Treadmill training with partial body weight support after stroke: A review. NeuroRehabilitation 2008; 1: 55-65
    Google Scholar
  • 11 Hidler J, Wisman W, Neckel N. Kinematic trajectories while walking within the Lokomat robotic gait-orthosis. Clin Biomech (Bristol, Avon) 2008; 10: 1251-1259
    Google Scholar
  • 12 Israel JF, Campbell DD, Kahn JH. et al. Metabolic costs and muscle activity patterns during robotic- and therapist-assisted treadmill walking in individuals with incomplete spinal cord injury. Phys Ther 2006; 11: 1466-1478
    Google Scholar
  • 13 Krewer C, Muller F, Husemann B. et al. The influence of different Lokomat walking conditions on the energy expenditure of hemiparetic patients and healthy subjects. Gait Posture 2007; 3: 372-377
    Google Scholar
  • 14 Laver KE, George S, Thomas S. et al. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev 2015; 2: CD008349
    Google Scholar
  • 15 Lee SY, Han EY, Kim BR. et al. Can lowering the guidance force of robot-assisted gait training induce a sufficient metabolic demand in subacute dependent ambulatory patients with stroke?. Archives of Physical Medicine and Rehabilitation 2017; 4: 695-700
    Google Scholar
  • 16 Lefeber N, Swinnen E, Kerckhofs E. The immediate effects of robot-assistance on energy consumption and cardiorespiratory load during walking compared to walking without robot-assistance: A systematic review. Disabil Rehabil Assist Technol 2016; 1-15
    PubMedGoogle Scholar
  • 17 Lefeber N, Swinnen E. Michielsen. et al. Energy consumption and cardiorespiratory load during Lokomat walking compared to walking without robot-assistance in stroke patients: Preliminary results. In: Ibáñez J, González-Vargas J, Azorín JM. et al., eds Converging Clinical and Engineering Research on Neurorehabilitation II: Proceedings of the 3 rd International Conference on NeuroRehabilitation (ICNR2016), October 18–21, 2016, Segovia, Spain. Cham: Springer International Publishing; 2017: 1229-1233
    CrossrefGoogle Scholar
  • 18 Lindsay P, Bayley M, Hellings C. et al. Canadian best practice recommendations for stroke care (updated 2008). Canadian Medical Association Journal 2008; 12: S1-S25
    Google Scholar
  • 19 Lo AC, Triche EW. Improving gait in multiple sclerosis using robot-assisted, body weight supported treadmill training. Neurorehabil Neural Repair 2008; 6: 661-671
    Google Scholar
  • 20 Lo AC, Chang VC, Gianfrancesco MA. et al. Reduction of freezing of gait in Parkinson’s disease by repetitive robot-assisted treadmill training: A pilot study. J Neuroeng Rehabil 2010; 51
    PubMedGoogle Scholar
  • 21 Maclean N, Pound P, Wolfe C. et al. The concept of patient motivation: A qualitative analysis of stroke professionals’ attitudes. Stroke 2002; 2: 444-448
    Google Scholar
  • 22 Mehrholz J, Pohl M. Electromechanical-assisted gait training after stroke: A systematic review comparing end-effector and exoskeleton devices. J Rehabil Med 2012; 3: 193-199
    Google Scholar
  • 23 Mehrholz J, Pohl M, Elsner B. Treadmill training and body weight support for walking after stroke. Cochrane Database Syst Rev 2014; 1: CD002840
    Google Scholar
  • 24 Mehrholz J, Thomas S, Werner C. et al. Electromechanical-assisted training for walking after stroke. Cochrane Database Syst Rev 2017; CD006185
    PubMedGoogle Scholar
  • 25 Meuleman J, van Asseldonk E, van Oort G. et al. LOPES II – Design and evaluation of an admittance controlled gait training robot with shadow-leg approach. IEEE Trans Neural Syst Rehabil Eng 2016; 3: 352-363
    Google Scholar
  • 26 Motl RW, Goldman M. Physical inactivity, neurological disability, and cardiorespiratory fitness in multiple sclerosis. Acta Neurol Scand 2011; 2: 98-104
    Google Scholar
  • 27 Myers J, McAuley P, Lavie CJ. et al. Physical activity and cardiorespiratory fitness as major markers of cardiovascular risk: Their independent and interwoven importance to health status. Prog Cardiovasc Dis 2015; 4: 306-314
    Google Scholar
  • 28 Nam KY, Kim HJ, Kwon BS. et al. Robot-assisted gait training (Lokomat) improves walking function and activity in people with spinal cord injury: A systematic review. J Neuroeng Rehabil 2017; 1: 24
    Google Scholar
  • 29 Picelli A, Melotti C, Origano F. et al. Robot-assisted gait training in patients with Parkinson disease: A randomized controlled trial. Neurorehabil Neural Repair 2012; 4: 353-361
    Google Scholar
  • 30 Pompa A, Morone G, Iosa M. et al. Does robot-assisted gait training improve ambulation in highly disabled multiple sclerosis people? A pilot randomized control trial. Mult Scler 2017; 5: 696-703
    Google Scholar
  • 31 Ruiz J, Labas MP, Triche EW. et al. Combination of robot-assisted and conventional body-weight-supported treadmill training improves gait in persons with multiple sclerosis: A pilot study. J Neurol Phys Ther 2013; 4: 187-193
    Google Scholar
  • 32 Sale P, De Pandis MF, Le Pera D. et al. Robot-assisted walking training for individuals with Parkinson’s disease: A pilot randomized controlled trial. BMC Neurol 2013; 50
    PubMedGoogle Scholar
  • 33 Schwartz I, Sajin A, Moreh E. et al. Robot-assisted gait training in multiple sclerosis patients: A randomized trial. Mult Scler 2012; 6: 881-90
    Google Scholar
  • 34 Smith AC, Saunders DH, Mead G. Cardiorespiratory fitness after stroke: A systematic review. Int J Stroke 2012; 6: 499-510
    Google Scholar
  • 35 Straudi S, Fanciullacci C, Martinuzzi C. et al. The effects of robot-assisted gait training in progressive multiple sclerosis: A randomized controlled trial. Mult Scler 2016; 3: 373-384
    Google Scholar
  • 36 Swinnen E, Duerinck S, Baeyens JP. et al. Effectiveness of robot-assisted gait training in persons with spinal cord injury: A systematic review. J Rehabil Med 2010; 6: 520-526
    Google Scholar
  • 37 Swinnen E, Beckwee D, Pinte D. et al. Treadmill training in multiple sclerosis: Can body weight support or robot assistance provide added value? A systematic review. Mult Scler Int 2012; 240274
    PubMedGoogle Scholar
  • 38 Swinnen E, Baeyens JP, Pintens S. et al. Trunk kinematics during walking in persons with multiple sclerosis: The influence of body weight support. NeuroRehabilitation 2014; 4: 731-740
    Google Scholar
  • 39 Swinnen E, Baeyens JP, Pintens S. et al. Trunk muscle activity during walking in persons with multiple sclerosis: The influence of body weight support. NeuroRehabilitation 2014; 2: 323-35
    Google Scholar
  • 40 Swinnen E, Baeyens JP, Hens G. et al. Body weight support during robot-assisted walking: Influence on the trunk and pelvis kinematics. NeuroRehabilitation 2015; 1: 81-91
    Google Scholar
  • 41 Swinnen E, Baeyens JP, Knaepen K. et al. Walking with robot assistance: The influence of body weight support on the trunk and pelvis kinematics. Disabil Rehabil Assist Technol 2015; 3: 252-257
    Google Scholar
  • 42 Swinnen E, Lefeber N, Willaert W. et al. Motivation, expectations, and usability of a driven gait orthosis in stroke patients and their therapists. Top Stroke Rehabil 2017; 4: 299-308
    Google Scholar
  • 43 Vaney C, Gattler B, Lugon-Moulin V. et al. Robotic-assisted step training (lokomat) not superior to equal intensity of over-ground rehabilitation in patients with multiple sclerosis. Neurorehabil Neural Repair 2012; 3: 212-221
    Google Scholar
  • 44 van Kammen K, Boonstra AM, van der Woude LH. et al. The combined effects of guidance force, bodyweight support and gait speed on muscle activity during able-bodied walking in the Lokomat. Clin Biomech (Bristol, Avon) 2016; 65-73
    PubMedGoogle Scholar
  • 45 van Kammen K, Boonstra AM, van der Woude LHV. et al. Differences in muscle activity and temporal step parameters between Lokomat guided walking and treadmill walking in post-stroke hemiparetic patients and healthy walkers. J Neuroeng Rehabil 2017; 1: 32
    Google Scholar
  • 46 Vanmulken DA, Spooren AI, Bongers HM. et al. Robot-assisted task-oriented upper extremity skill training in cervical spinal cord injury: A feasibility study. Spinal Cord 2015; 7: 547-551
    Google Scholar
  • 47 Wier LM, Hatcher MS, Triche EW. et al. Effect of robot-assisted versus conventional body-weight-supported treadmill training on quality of life for people with multiple sclerosis. J Rehabil Res Dev 2011; 4: 483-492
    Google Scholar
  • 48 Zimmerli L, Jacky M, Lunenburger L. et al. Increasing patient engagement during virtual reality-based motor rehabilitation. Arch Phys Med Rehabil 2013; 9: 1737-1746
    Google Scholar