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Neural-gesteuerte Robotik für Assistenz und Rehabilitation im Alltag

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Book cover Mensch-Roboter-Kollaboration

Zusammenfassung

Die Entwicklung kollaborativer, robotischer Systeme, die direkt mit dem menschlichen Nervensystem interagieren, verspricht, die Autonomie, Lebensqualität und Leistungsfähigkeit von Menschen mit Behinderungen, beispielsweise nach einem Schlaganfall oder einer Rückenmarksverletzung, substanziell zu verbessern. Durch die direkte Übersetzung elektrischer, magnetischer oder metabolischer Hirnaktivität ermöglichen solche Systeme die aktive und intuitive Steuerung tragbarer Exoskelette, die beispielsweise bei Greif- oder Laufbewegungen assistieren. So wurde es Querschnittsgelähmten mit kompletter Fingerlähmung ermöglicht, erstmals wieder selbstständig zu essen und zu trinken. Zudem konnte gezeigt werden, dass der wiederholte Einsatz solch neural-gesteuerter Exoskelette unter bestimmten Voraussetzungen auch zu einer Wiederherstellung verlorengegangener motorischer Funktionen führen kann. Trainierten Schlaganfallüberlebende mit chronischer Fingerlähmung über mehrere Wochen hinweg täglich mit einem solchen Exoskelett, so wiesen sie eine deutliche Verbesserung ihrer Arm- und Handfunktion auf. Um neural-gesteuerte Robotik in die breite medizinische Versorgung zu integrieren, müssen jedoch noch eine Reihe wissenschaftlich-technischer sowie rechtlich-regulatorischer Herausforderungen gemeistert werden. Neben einer Übersicht über den Stand der Technik sowie die aktuellen Herausforderungen in der Weiterentwicklung neural-gesteuerter robotischer Systeme werden in diesem Kapitel mögliche Lösungsansätze und Anwendungsperspektiven skizziert.

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Literatur

  • Acqualagna, L., Botrel, L., Vidaurre, C., Kubler, A., & Blankertz, B. (2016). Large-scale assessment of a fully automatic co-adaptive motor imagery-based brain computer interface. PLoS One, 11(2), e0148886.

    Article  Google Scholar 

  • Ajiboye, A. B., et al. (2017). Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration (in English). Lancet, 389(10081), 1821–1830.

    Article  Google Scholar 

  • Borton, D., Micera, S., Millan Jdel, R., & Courtine, G. (2013). Personalized neuroprosthetics. Science Translational Medicine, 5(210), 210rv2.

    Article  Google Scholar 

  • Bouton, C. E., et al. (2016). Restoring cortical control of functional movement in a human with quadriplegia. Nature, 533(7602), 247–250.

    Article  Google Scholar 

  • Busch, M. A., Schienkiewitz, A., Nowossadeck, E., & Gosswald, A. (2013). Prevalence of stroke in adults aged 40 to 79 years in Germany: Results of the German Health Interview and Examination Survey for Adults (DEGS1) (in German). Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 56(5-6), 656–660. Pravalenz des Schlaganfalls bei Erwachsenen im Alter von 40 bis 79 Jahren in Deutschland: Ergebnisse der Studie zur Gesundheit Erwachsener in Deutschland (DEGS1).

    Article  Google Scholar 

  • Cervera, M. A., et al. (2018). Brain-computer interfaces for post-stroke motor rehabilitation: A meta-analysis. Annals of Clinical Translational Neurology, 5(5), 651–663.

    Article  Google Scholar 

  • Clausen, J., et al. (2017). Help, hope, and hype: Ethical dimensions of neuroprosthetics. Science, 356(6345), 1338–1339.

    Article  Google Scholar 

  • Collinger, J. L., et al. (2013). High-performance neuroprosthetic control by an individual with tetraplegia. Lancet, 381(9866), 557–564.

    Article  Google Scholar 

  • Feigin, V., et al. (2016). Global Burden of Diseases, Injuries and Risk Factors Study 2013 and Stroke Experts Writing Group. Global burden of stroke and risk factors in 188 countries, during 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet Neurology, 15(9), 913–924.

    Article  Google Scholar 

  • GBD 2016 Stroke Collaborators. (2019). Global, regional, and national burden of stroke, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurology, 18(5), 439–458. https://doi.org/10.1016/S1474-4422(19)30034-1.

  • Hochberg, L. R., et al. (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm (in English). Nature, 485(7398), 372–375.

    Article  Google Scholar 

  • Kim, J., et al. (2019). Reducing the metabolic rate of walking and running with a versatile, portable exosuit. Science, 365(6454), 668–672.

    Article  Google Scholar 

  • Kolominsky-Rabas, P. L., et al. (2006). Lifetime cost of ischemic stroke in Germany: Results and national projections from a population-based stroke registry: the Erlangen Stroke Project. Stroke, 37(5), 1179–1183.

    Article  Google Scholar 

  • Kwakkel, G., Kollen, B. J., van der Grond, J., & Prevo, A. J. (2003). Probability of regaining dexterity in the flaccid upper limb: Impact of severity of paresis and time since onset in acute stroke. Stroke, 34(9), 2181–2186.

    Article  Google Scholar 

  • Mohammadi, A., Lavranos, J., Choong, P., & Oetomo, D. (2018). Flexo-glove: A 3D printed soft exoskeleton robotic glove for impaired hand rehabilitation and assistance. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Honolulu, Hawaii 2018, 2120–2123.

    Google Scholar 

  • Muller, K. R., Tangermann, M., Dornhege, G., Krauledat, M., Curio, G., & Blankertz, B. (2008). Machine learning for real-time single-trial EEG-analysis: From brain-computer interfacing to mental state monitoring. Journal of Neuroscience Methods, 167(1), 82–90.

    Article  Google Scholar 

  • Nann, M., Cohen, L. G., Deecke, L., & Soekadar, S. R. (2019). To jump or not to jump – The Bereitschaftspotential required to jump into 192-meter abyss. Scientific Reports, 9(1), 2243.

    Article  Google Scholar 

  • Ramos-Murguialday, A., et al. (2013). Brain-machine interface in chronic stroke rehabilitation: A controlled study. Annals of Neurology, 74(1), 100–108.

    Article  Google Scholar 

  • Rosamond, W., et al. (2008). American Heart Association Statistics Committee And Stroke Statistics Subcommittee. Disease and stroke statistics – 2008 update: A report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation, 117(4), e25–e146.

    Google Scholar 

  • Singh, N., Saini, M., Anand, S., Kumar, N., Srivastava, M. V. P., & Mehndiratta, A. (2019). Robotic exoskeleton for wrist and fingers joint in post-stroke neuro-rehabilitation for low-resource settings. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27, 2369–2377.

    Article  Google Scholar 

  • Soekadar, S., & Birbaumer, N. (2015). Brain-machine interfaces for communication in complete paralysis: Ethical implications and challenges. In J. N. L. Clausen (Hrsg.), Handbook of neuroethics (S. 705–724). Dordrecht: Springer.

    Google Scholar 

  • Soekadar, S. R., Haagen, K., & Birbaumer, N. (2007). Brain-Computer Interfaces (BCI): Restoration of movement and thought from neuroelectric and metabolic brain activity. In A. Schuster (Hrsg.), Intelligent computing everywhere (S. 229–252). London: Springer.

    Google Scholar 

  • Soekadar, S. R., Witkowski, M., Vitiello, N., & Birbaumer, N. (2015a). An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand. Biomedizinische Technik. Biomedical Engineering, 60(3), 199–205.

    Article  Google Scholar 

  • Soekadar, S. R., Birbaumer, N., Slutzky, M. W., & Cohen, L. G. (2015b). Brain-machine interfaces in neurorehabilitation of stroke. Neurobiology of Disease, 83, 172–179.

    Article  Google Scholar 

  • Soekadar, S. R., et al. (2016). Hybrid EEG/EOG-based brain/neural hand exoskeleton restores fully independent daily living activities after quadriplegia. Science Robotics, 1(1). https://doi.org/10.1126/scirobotics.aag3296.

  • Taub, E., Uswatte, G., & Pidikiti, R. (1999). Constraint-induced movement therapy: A new family of techniques with broad application to physical rehabilitation – A clinical review. Journal of Rehabilitation Research and Development, 36(3), 237–251.

    Google Scholar 

  • Toyama, S., Takano, K., & Kansaku, K. (2012). A non-adhesive solid-gel electrode for a non-invasive brain-machine interface (in English). Frontiers in Neurology, 3(114), 114.

    Google Scholar 

  • WHO. (2012). World health report. Geneva: World Health Organization.

    Google Scholar 

  • Winter, Y., Wolfram, C., Schoffski, O., Dodel, R. C., & Back, T. (2008). Long-term disease-related costs 4 years after stroke or TIA in Germany. Nervenarzt, 79(8), 918–920. 922–924, 926. Langzeitkrankheitskosten 4 Jahre nach Schlaganfall oder TIA in Deutschland.

    Article  Google Scholar 

  • Witkowski, M., Cortese, M., Cempini, M., Mellinger, J., Vitiello, N., & Soekadar, S. R. (2014). Enhancing brain-machine interface (BMI) control of a hand exoskeleton using electrooculography (EOG). Journal of Neuroengineering and Rehabilitation, 11(1), 165.

    Article  Google Scholar 

  • Wolf, S. L., Lecraw, D. E., Barton, L. A., & Jann, B. B. (1989). Forced use of hemiplegic upper extremities to reverse the effect of learned nonuse among chronic stroke and head-injured patients (in English). Experimental Neurology, 104(2), 125–132.

    Article  Google Scholar 

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Correspondence to Surjo R. Soekadar .

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© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Soekadar, S.R., Nann, M. (2020). Neural-gesteuerte Robotik für Assistenz und Rehabilitation im Alltag. In: Buxbaum, HJ. (eds) Mensch-Roboter-Kollaboration. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-28307-0_8

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