Towards Bidirectional and Coadaptive Robotic Exoskeletons for Neuromotor Rehabilitation and Assisted Daily Living: a Review

Current Robotics Reports - Tập 3 - Trang 21-32 - 2022
Elsa Andrea Kirchner1,2, Judith Bütefür1
1Institute of Medical Technology Systems, University of Duisburg-Essen, Duisburg, Germany
2Robotics Innovation Center, German Research Center for Artificial Intelligence, Bremen, Germany

Tóm tắt

Starting with a technical categorization and an overview of current exoskeletons and orthoses and their applications, this review focuses on robotic exoskeletons and orthoses for neuromotor rehabilitation and relevant research needed to provide individualized adaptive support to people under complex environmental conditions, such as assisted daily living. Many different approaches from the field of autonomous robots have recently been applied to the control of exoskeletons. In addition, approaches from the field of brain-computer interfaces for intention recognition are being intensively researched to improve interaction. Finally, besides stimulation, bidirectional feedback and feedback-based learning are recognized as very important to enable individualized, flexible, and adaptive human assistance. AI-based methods for adaptation and online learning of robotic exoskeleton control, combined with intrinsic recognition of human intentions and consent, will in particular lead to improving the quality of human–robot interaction and thus user satisfaction with exoskeleton-based rehabilitation interventions.

Tài liệu tham khảo

N. Yagn, “Apparatus for facilitating walking”. Patent US 440684 A, 1890.

H. Alfven and H. Kleinwächter, “Syntelmann—und die möglichen Konsequenzen,” Bild der Wissenschaft, 1970.

G. Cobb, “Walking motion”. Patent US 2010482 A, 1934.

C.-J. Yang, J.-F. Zhang, Y. Chen, Y.-M. Dong and Y. Zhang, “A review of exoskeleton-type systems and their key technologies,” Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, pp. 1599–1612, 2008, doi: https://doi.org/10.1243/09544062JMES936.

Y. Sankai, “HAL: hybrid assistive limb based on cybernics,” Kaneko M., Nakamura Y. (eds) Robotic Research. Springer Tracts in Advanced Robotics, 2010, https://doi.org/10.1007/978-3-642-14743-2_3.

H. Kazerooni, W. Tung and M. Pillai, “Evaluation of Trunk-supporting exoskeleton,” Proceedings of the Human Factors and Ergonomics Scoiety, pp. 1080–1083, 2019, https://doi.org/10.1177/1071181319631261.

A. Zoss, H. Kazerooni and A. Chu, “On the mechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX),” IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3465–3472, 2005, doi: https://doi.org/10.1109/IROS.2005.1545453.

M. Folgheraiter, M. Jordan, S. Straube, A. Seeland, S.-K. Kim and E. A. Kirchner, “Measuring the improvement of the interaction comfort of a wearbale exoskeleton,” International Journal of Social Robotics, pp. 285–302, 2012, https://doi.org/10.1007/s12369-012-0147-x.

I. Jo, Y. Park and J. Bae, “A teleoperation system with an exoskeleton interface,” IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 1649–1654, 2013, doi: https://doi.org/10.1109/AIM.2013.6584333.

M. Mallwitz, N. Will, J. Teiwes and E. A. Kirchner, “The CAPIO active upper body exoskeleton and its application for teleoperation,” Proceedings of the 13th Symposium on Advanced Space Technologies in Robotics and Automation, 2015.

A. P. Irawan, D. W. Utama, E. Affandi, Michael and H. Suteja, “Product design of chairless chair based on local components to provide support for active workers,” IOP Conference Series: Materials Science and Engineering, 2019, doi:https://doi.org/10.1088/1757-899X/508/1/012054.

S. Spada, L. Ghibaudo, S. Gilotta, L. Gastaldi and M. P. Cavatorta, “Investigation into the applicability of a passive upper-limb exoskeleton in automotive industry,” Procedia Manufacturing, pp. 1255–1262, 2017, https://doi.org/10.1016/j.promfg.2017.07.252.

T. Platz and S. Roschka, “Rehabilitative Therapie bei Armparese nach Schlaganfall,” Neurol. Rehabil., pp. 81–106, 2009.

T. Platz, “Rehabilitative Therapie bei Armlähmungen nach einem Schlaganfall. S2-Leitlinie der Deutschen Gesellschaft für Neurorehabilitation,” NeuroGeriatrie, pp. 104–116, 2011.

J. Nitschke, D. Kuhn, K. Fischer and K. Röhl, “Comparison of the usability of the rewalk, Ekso and HAL,” Special edition from: OrthOpädietechnik, p. 22, 2014.

C. D. Takahashi, L. Der-Yeghiaian, V. Le, R. R. Motiwala and S. C. Cramer, “Robot-based hand motor therapy after stroke,” Brain, pp. 425–437, 2008, doi: https://doi.org/10.1093/brain/awm311.

T. Noda, N. Sugimoto, J. Furukawa, M.-A. Sato, S.-H. Hyon and J. and Morimoto, “Brain-controlled exoskeleton robot for BMI rehabilitation,” Proc. 12th IEEE-RAS Int. Conf. Humanoid Robots (Humanoids), pp. 21–27, 2012, doi: https://doi.org/10.1109/HUMANOIDS.2012.6651494.

E. Hortal, D. Planelles and F. e. a. Resquin, “Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions,” J NeuroEngineering Rehabil, 2015, https://doi.org/10.1186/s12984-015-0082-9.

N. Singh, M. Saini, N. Kumar, M. V. Padma Srivastava and A. Mehndiratta, “Evidence of neuroplasticy with robotic hand exoskeleton for post-stroke rehabilitation: a randomized controlled trial,” J NeuroEngineering Rehabil, 2021, https://doi.org/10.1186/s12984-021-00867-7.

E. A. Kirchner, J. Albiez, A. Seeland, M. Jordan and F. Kirchner, “Towards assistive robotics for home rehabilitation,” Proceedings of the 6th International Conference in Biomdeical Electronics and Devices (BIODEVICES-13), 2013.

E. A. Kirchner, S.-K. Kim, S. Straube, A. Seeland, H. Wöhrle, M. M. Krell, M. Tabie and M. Fahle, “On the applicability of brain reading for predictive human-machine interfaces in robotics,” PLoS ONE, Public Library of Science, p. e81732, 2013, https://doi.org/10.1371/journal.pone.0081732.

E. A. Kirchner, N. Will, M. Simnofske, L. M. Vaca Benitez, B. Bongardt, M. M. Krell, S. Kumar, M. Mallwitz, A. Seeland, M. Tabie, H. Wöhrle, M. Yüksel, A. Heß, R. Buschfort and F. Kirchner, “Recupera-reha: exoskeleton technology with integrated biosignal analysis for sensorimotor rehabilitation,” 2. Transdiziplinäre Konferenz “Technische Unterstüzungssysteme, die die Menschen wirklich wollen”, pp. 504–517, 2016.

J. Law and E. Martin, Concise Medical Dictionary, 10 ed., Oxford University Press, 2020.

H. Herr, “Exoskeleton and orthoses: classification, design challanges and future directions,” Journal of NeuroEngineering and Rehabilitation, vol. 6, no. 21, 2009, https://doi.org/10.1186/1743-0003-6-21.

Strickland, “Good-bye, wheelchair,” IEEE Spectrum, pp. 30–32, 2012, doi: https://doi.org/10.1109/MSPEC.2012.6117830.

Kirchner, E.A. et al., “Exoskelette der künstlichen Intelligenz in der klinischen Rehabilitation,” in Digitale Transformation von Dienstleistungen im Gesundheitswesen, Wiesbaden, Springer Gabler, 2019, pp. 413–435, https://doi.org/10.1007/978-3-658-23987-9_21.

Otto Bock HealthCare Deutschland GmbH, “Elektronisch gesteuertes Kniegelenksystem E-MAG Active,” 2021. [Online]. Available: https://www.ottobock.de/orthesen/produkte/bein-und-knieorthesen/e-mag-active/. [Accessed 11 February 2022].

Bauerfeind AG, “MalleoLoc,” Bauerfeind AG, 2022. [Online]. Available: https://www.bauerfeind.de/de/produkte/orthesen/fuss/details/product/malleoloc. [Accessed 11 February 2022].

BORT medical, “Produkte—BORT OsoTract Oberarm-Schulter-Orthese,” BORT GmbH, 2022. [Online]. Available: https://www.bort.com/de/produktdetail.html?product=121300. [Accessed 11 February 2022].

eksoBIONICS, “eksoNR,” Ekso Bionics, 2021. [Online]. Available: https://eksobionics.com/eksonr/. [Accessed 11 February 2022].

E. A. Kirchner, N. Will, M. Simnofske, L. M. Vaca Benitez, B. Bongardt, M. M. Krell, S. Kumar, M. Mallwitz, A. Seeland, M. Tabie, H. Wöhrle, M. Yüksel, A. Heß, R. Buschfort and F. Kirchner, “Recupera-reha: exoskeleton technology with integrated biosignal analysis for sensorimotor rehabilitation,” in Zweite transdiziplinäre Konferenz “Technische Unterstützungssysteme, die die Menschen wirklich wollen”, 2016.

Otto Bock HealthCare Deutschland GmbH, “Paexo Neck,” [Online]. Available: https://paexo.com/wp-content/uploads/2019/11/2019-10363-66-Beileger-PaexoNeck-DL-DE-OBE-20190926.pdf. [Accessed 11 February 2022].

Balser F, Desai R, Ekizoglou A, Bai S. A novel passive shoulder exoskeleton designed with variable stiffness mechanism. IEEE Robotics and Automation Letters. 2022;7(2):2748–54. https://doi.org/10.1109/LRA.2022.3144529.

Hyun DJ, Bae K, Kim K, Nam S, Lee D-H. A light-weight passive upper arm assistive exoskeleton based on multi-linkage spring-energy dissipation mechanism for overhead tasks. Robotics and Autonomous System. 2019. https://doi.org/10.1016/j.robot.2019.103309.

Maurice P, Camernik J, Gorjan D, Schirrmeister B, Bornmann J, Tagliapietra L, Latella C, Pucci D, Fritzsche L, Ivaldi S, Babic J. Objective an subjective effects of a passive exoskeleton on overhead work. IEEE Trans Neural Syst Rehabil Eng. 2020;28(1):152–64. https://doi.org/10.1109/TNSRE.2019.2945368.

Skelex, “Skelex 360-XFR,” [Online]. Available: https://www.skelex.com/skelex-360-xfr/. [Accessed 11 February 2022].

Otto Bock HealthCare GmbH, “Agilium Freestep 3.0,” 2021. [Online]. Available: https://www.ottobock.de/orthesen/produkte/bein-und-knieorthesen/agilium-freestep-3.0/. [Accessed 11 February 2022].

HMT, “Moon,” Human Mechanical Technologies, 2022. [Online]. Available: https://www.hmt-france.com/fr/ourExoskeletons/moon. [Accessed 11 February 2022].

Hunic GmbH, “SoftExo Carry,” HUNIC SoftExo, 2022. [Online]. Available: https://hunic.com/softexo-carry/. [Accessed 11 February 2022].

Laevo Exoskeletons, “The Laevo V2,” [Online]. Available: https://www.laevo-exoskeletons.com/en/laevo-v2. [Accessed 11 February 2022].

German Bionic, “CrayX: Exoskelett für manuelles Handling,” German Bionic Systems GmbH, 2022. [Online]. Available: https://www.germanbionic.com/5th-generation/. [Accessed 11 February 2022].

eksoBIONICS, “eksoUE—upper extremity exoskeleton,” Ekso Bionics, 2020. [Online]. Available: https://eksobionics.com/de/eksoue-de/. [Accessed 11 February 2022].

SuitX, “Recreational exoskeleton—BoostX Knee,” suitx, 2021. [Online]. Available: https://www.suitx.com/boostknee. [Accessed 11 February 2022].

B. Kim and A. D. Deshpande, “An upper-body rehabilitation exoskleton Harmony with an anatomical shoulder mechanism: design, modeling, control, and performance evaluation,” The International Journal of Robotics Research, pp. 414–435, 2017, doi: https://doi.org/10.1177/0278364917706743.

A. F. Ruiz, A. Forner-Cordero, E. Rocon and J. L. Pons, “Exoskeletons for rehabilitation and motor control,” The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, pp. 601–606, 2006, doi: https://doi.org/10.1109/BIOROB.2006.1639155.

Hunic GmbH, “Hunic SoftExo Care,” HUNIC SoftExo, 2022. [Online]. Available: https://hunic.com/softexo-care/. [Accessed 11 February 2022].

SuitX, “shieldX | suitX,” suitx, 2021. [Online]. Available: https://www.suitx.com/shieldx. [Accessed 11 February 2022].

R. Robotics, “Forge Performance - Roam,” 2021. [Online]. Available: https://www.roamrobotics.com/forge. [Accessed 11 February 2022].

H. Kazerooni, J.-L. Racine, L. Huang and R. Steger, “On the control of the Berkeley Lower Extremity Exoskeleton (BLEEX),” Proceedinfs of the 2005 IEEE International Conference on Robotics and Automation, pp. 4353–4360, 2005, doi: https://doi.org/10.1109/ROBOT.2005.1570790.

German Research Center for Artifical Intelligence and Universität Bremen, “The CAPIO active upper body exoskeleton,” [Online]. Available: https://www.dfki.de/fileadmin/user_upload/import/7383_slides_RoboAssist_2014_Mallwitz.pdf. [Accessed 10 February 2022].

M. Folgheraiter, M. Jordan, L. M. Vaca Benitez, F. Grimminger, S. Schmidt, J. Albiez and F. Kirchner, “A highly integrated low pressure fluid servo-valve for applications in wearable robotic systems,” Proceedings of the 7th International Conference on Informatics in Control, Automation and Robotics, 2010.

H. Beik-Mohammadi, M. Kerzel, B. Pleintinger, T. Hulin, P. Reisich, A. Schmidt, A. Pereira, S. Wermter and N. Y. Lii, “Model mediated teleoperation with a hand-arm exoskeleton in long time delays using reinforcement learning,” 2020 29th IEEE Conference on Robot Human Interactive Communication (RO-MAN), pp. 713–720, 2020, doi: https://doi.org/10.1109/RO-MAN47096.2020.9223477..

Tendo, “Get a grip | Tendo—for people, not for symptoms,” tendoforpeople, [Online]. Available: https://www.tendoforpeople.se/tendo. [Accessed 11 February 2022].

Zhang C, Liu G, Li C, Zhao J, Yu H, Zhu Y. Development of a lower limb rehabilitation exoskeleton based on real-time gait detection and gait tracking. JOUR. 2016. https://doi.org/10.1177/1687814015627982.

S. Kumar, H. Wöhrle, M. Trampler, M. Simnofske, H. Peters, M. Mallwitz, E. A. Kirchner and F. Kirchner, “Modular design and decentralized control of the recupera exoskeleton for stroke rehabilitation,” Applied Sciences, MDPI, p. 626, 2019, https://doi.org/10.3390/app9040626.

E. A. Kirchner, S. Fairclough and F. Kirchner, “Embedded multimodal interfaces in robotics: applications, future trends, and societal implications,” The Handbook of Multimodal-Multisensor Interfaces, Morgan & Claypool Publishers, pp. 523–576, ISBN: ebook: 978–1–97000–173–0, hardcover: 978–1–97000–175–4, 2019.

J. Kerdraon, J. Previnaire, M. Tucker and et al., “Evaluation of safety and performance of the self balancing walking system Atalante in patients with complete motor spinal cord injury,” Spinal Cord Ser Cases, vol. 7, no. 71, 2021, https://doi.org/10.1038/s41394-021-00432-3.

A. U. Pehlivan, D. P. Losey and M. K. O’Malley, “Minimal assist-as-needed controller for upper limb robotic rehabilitation,” IEEE Transactions on Robotics, pp. 113–124, 2016, doi: https://doi.org/10.1109/TRO.2015.2503726.

S. Y. A. Mounis, N. Z. Azlan and F. Sado, “Assist-as-needed control strategy for upper-limb rehabilitation based on subject’s functional ability,” Measurement and Control, pp. 1354–1361, 2019, https://doi.org/10.1177/0020294019866844.

L. Zhang, S. Guo and Q. Sun, “An assist-as-needed controller for passive, assistant, active, and resistive robot-aided rehabilitation training of the upper extremity,” Appl. Sci., p. 340, 2021, https://doi.org/10.3390/app11010340.

B. Fang, Q. Zhou, F. Sun, J. Shan, M. Wang, C. Xiang and Q. Zhang, “Gait neural network for human-exoskeleton interaction,” Front. Neurorobot., 29 October 2020, https://doi.org/10.3389/fnbot.2020.00058.

B. Kleiner, N. Ziegenspeck, R. Stolyarov, H. Herr, U. Schneider and A. Verl, “A radar-based terrain mapping approach for stair detection towards enhanced prosthetic foot control,” IEEE International Conference on Biomedical Robotics and Biomechatronics (BIOROB), pp. 105–110.

Y. Zhou, J. She, Z.-T. Liu, C. Xu and Z. Yang, “Implementation of impedance control for lower-limb rehabilitation robots,” 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), pp. 700–704, 2021, doi: https://doi.org/10.1109/ICPS49255.2021.9468210.

A. Q. Keemink, H. van der Kooij and A. H. and Stienen, “Addmittance control for physical human-robot interaction,” Int. J. Rob. Res. 37, pp. 1421–1444, 2018, doi: https://doi.org/10.1177/0278364918768950.

J. C. Castiblanco, I. F. Mondragon, C. Alvarado-Rojas and J. D. Colorado, “Assist-as-needed exoskeleton for hand joint rehabilitation based on muscle effort detection,” Sensors, p. 4372, 2021, https://doi.org/10.3390/s21134372.

D. Fineberg, P. Asselin, N. Harel, I. Agranova-Breyter, S. Kornfeld, W. Baumann and A. Spungen, “Vertical ground reaction force-based analysis of powered exoskeleton-assisted walking in persons with motor complete paraplegia,” J Spinal Cord med., pp. 313–321, 2013, doi: https://doi.org/10.1179/2045772313Y.0000000126.

F. Xu, R. Huang and Cheng, H. et al., “Stair ascent strategies and performance evaluation for a lower limb exoskeleton,” Int J Intell Robot Appl, pp. 278–293, 2020, https://doi.org/10.1007/s41315-020-00123-6.

B. Laschowski, W. McNally, A. Wong and J. McPhee, “Environment classification for robotic leg prostheses and exoskeletons using deep convolutional neural networks,” Front. Neurorobot., 04 February 2022, https://doi.org/10.3389/fnbot.2021.

G. B. Prange, M. J. A. Jannink, C. G. M. Groothuis-Oudshoorn, H. J. Hermens and M. J. Ijzerman, “Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke,” J Rehabil Res Dev., pp. 171–84, 2006, doi: https://doi.org/10.1682/jrrd.2005.04.0076.

G. Kwakkel, B. J. Kollen and H. I. Krebs, “Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review,” Neurorehabil Neural Repair, pp. 111–21, 2008, doi: https://doi.org/10.1177/1545968307305457.

• H. Nam, H. Seo, J. Leigh, Y. Kim, S. Kim and M. Bang, “External robotic arm vs. upper limb exoskeleton: what do potential users need?,” Appl. Sci., p. 2471, 2019, https://doi.org/10.3390/app9122471. This work is relevant since it analyzes the demands of different groups of patients with upper body impairments. Such work is most relevant for setting new goals in research and development.

• F. Grimm and A. Gharabaghi, “Closed-loop neuroprosthesis for reach-to-grasp assistance: combining adaptive multi-channel neuromuscular stimulation with a multi-joint arm exoskeleton,” Front. Neurosci., 2016, doi: https://doi.org/10.3389/fnins.2016.00284. This work is relevant since it addresses patients with chronic impairments caused by stroke. This group of patients has very bad outcome in case of classical therapy approaches.

L. Gerez, A. Dwivedi and M. Liarokapis, “A hybrid, soft exoskeleton glove equipped with a telescopic extra thumb and abduction capabilities,” IEEE International Conference on Robotics and Automation (ICRA), pp. 9100–9106, 2020, doi: https://doi.org/10.1109/ICRA40945.2020.9197473.

Topini A, Sansom W, Secciani N, Bartalucci L, Ridolfi A, Allotta B. Variable admittance control of a hand exoskeleton for virtual reality-based rehabilitation tasks. Front Neurorobot. 2022. https://doi.org/10.3389/fnbot.2021.789743.

M. S. Al Maamari, S. S. Al Badi, A. Saleem, M. Mesbah and E. Hassan, “Design of a brain controlled hand exoskeleton for patients with motor neuron diseases,” 10th IEEE International Symposium on Mechatronics and its Application, 2015, https://doi.org/10.1109/ISMA.2015.7373470.

L. Randazzo, I. Iturrate, S. Perdikis and J. d. R. Millán, “mano: a wearable hand exoskeleton for activities of daily living and neurorehabilitation,” IEEE Robotics and Automation Letters, pp. 500–507, 2018, doi: https://doi.org/10.1109/LRA.2017.2771329.

S. R. Soekadar, M. Witkowski, N. Vitiello and N. Birbaumer, “An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand,” Biomedical Engineering / Biomedizinische Technik, pp. 199–205, 2015, https://doi.org/10.1515/bmt-2014-0126.

N.-S. Kwak, K.-R. Müller and S.-W. Lee, “A lower limb exoskeleton control system based on steady state visual evoked potentials,” J. Neural Eng., p. 056009, 2015, doi: https://doi.org/10.1088/1741-2560/12/5/056009.

K. Lee, D. Liu, L. Perroud, R. Chavarriaga and J. d. R. Millán, “A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers,” Robotics and Autonomous Systems, pp. 15–23, 2017, https://doi.org/10.1016/j.robot.2016.10.005.

J. R. Wolpaw, N. Birbaumer, D. J. McFarland, G. Pfurtscheller and T. M. Vaughan, “Brain-computer interfaces for communication and control,” Clin Neurophysiol, pp. 767–01, 2002, https://doi.org/10.1016/S1388-2457(02)00057-3.

E. Lew, R. Chavarriaga, S. Stefano and J. d. R. Millán, “Detection of self-paced reaching movement intention from EEG signals,” Frontiers in Neuroengineering, 2012, https://doi.org/10.3389/fneng.2012.00013.

A. Ferreira, T. F. Bastos-Filho, M. Sarcinelli-Filho, J. L. Martín, J. C. García and M. Mazo, “Improvements of a brain-computer interface applied to a robotic wheelchair,” Biomedical Engineering Systems and Technologies - International Joint Conference BIOSTEC, pp. 64–73, 2009, https://doi.org/10.1186/s12984-015-0082-9.

E. Hortal, D. Planelles, A. Costa, E. Iánez, A. Úbeda, J. M. Azorín and E. Fernández, “SVM-based Brain-Machine Interface for controlling a robot arm through four mental tasks,” Neurocomputing, pp. 116–121, 2014, https://doi.org/10.1016/j.neucom.2014.09.078.

L. Citi, R. Poli, C. Cinel and F. Sepulveda, “P300-based BCI mouse with genetically-optimized analogue control,” IEEE Trans Neural Syst Rehabil Eng., pp. 51–61, 2008, doi: https://doi.org/10.1109/TNSRE.2007.913184.

J. L. Sirvent, E. Iánez, A. Úbeda and J. M. Azorín, “Visual evoked potential-based brain-machine interface applications to assist disabled people,” Expert Syst Appl., pp. 7908–18, 2012, https://doi.org/10.1016/j.eswa.2012.01.110.

Chowdhury A, Raza H, Dutta A, Prasad G. EEG-EMG based hybrid brain computer interface for troggering hand exoskeleton for neuro-rehabilitation. Preceedings of the Advances in Robotics. 2017. https://doi.org/10.1145/3132446.3134909.

E. A. Kirchner, A. Seeland and M. Tabie, “Multiodal movement prediction - towards an individual assistance of patients,” PLoS ONE, Public Library of Science, p. e85060, 2014, https://doi.org/10.1371/journal.pone.0085060.

G. Pfurtscheller and C. Neuper, “Motor imagery and direct brain-computer communication,” Proceedings of the IEEE, pp. 1123–1134, 2001, doi: https://doi.org/10.1109/5.939829.

J. Zhang and M. Wang, “A survey on robots controlled by motor imagery brain-computer interfaces,” Cognitive Robotics, pp. 12–24, 2021, https://doi.org/10.1016/j.cogr.2021.02.001.

Amiri S, Pantazis D, Fazel-Rezai R, Asadpour V. A review of hybrid brain-computer interfaces systems. Advances in Human-Computer Interaction. 2013. https://doi.org/10.1155/2013/187024.

Choi I, Rhiu I, Lee Y, Yun MH, Nam CS. A systematic review of hybrid brain-computer interfaces: taxonomy and usability perspectives. PLoS ONE. 2017. https://doi.org/10.1371/journal.pone.0176674.

G. R. Burkitt, R. B. Silberstein, P. J. Cadusch and a. W. Wood, “Steady-state visual evoked potentials and travelling waves,” Clin. Neurophysiol., pp. 246–58, 2000, doi: https://doi.org/10.1016/s1388-2457(99)00194-7.

Zhu D, Bieger J, Molina GG, Aarts RM. A survey of stimulation methods used in SSVEP-based BCIs. Comput Intell Neurosci. 2010. https://doi.org/10.1155/2010/702357.

Z. Iscan and V. V. Nikulin, “Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations,” PLoS ONE, p. e0191673, 2018, https://doi.org/10.1371/journal.pone.0191673.

A. Seeland, L. Manca, F. Kirchner and E. A. Kirchner, “Spatio-temporal comparison between ERD/ERS and MRCP-based movement prediction,” Proceedings of the 8th International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-15), pp. 219–226, 2015, https://doi.org/10.5220/0005214002190226.

Li H, Huang G, Lin Q, Zhao J-L, Lo W-LA, Mao Y-R, Chen L, Zhang Z-G, Huang D-F, Li L. Combining movement-related cortical potentials and event-related desynchronization to study movement preparation and execution. Front Neurol. 2018. https://doi.org/10.3389/fneur.2018.00822.

S. He, Y. Zhou, T. Yu, R. Zhang, Q. Huang, L. Chuai, M. U. Mustafa, Z. Gu, Z. L. Yu, H. Tan and Y. Li, “EEG- and EOG-based asynchronous hybrid BCI: a system integrating a sepller, a web browser, an e-mail client, and a file explorer,” IEEE Transactions On Neural Systems and Rehabilitation Engineering, pp. 519–530, 2014, https://doi.org/10.1109/TNSRE.2019.2961309.

Zhu Y, Ying L, Jinling L, Pengcheng L. A hybrid BCI based on SSVEP and EOG for robotic arm control. Front Neurorobot. 2020. https://doi.org/10.3389/fnbot.2020.583641.

Scherer R, Müller-Putz GR, Pfurtscheller G. Self-initiation of EEG-based brain-computer communication using the heart rate response. J Neural Eng. 2007. https://doi.org/10.1088/1741-2560/4/4/L01.

E. C. Lee, J. C. Woo, J. H. Kim, M. Whang and K. R. Park, “A brain-computer interface method combined with eye tracking for 3D interaction,” J Neurosci Methods, pp. 289–98, 2010, https://doi.org/10.1016/j.jneumeth.2010.05.008.

A. Kreilinger, V. Kaiser, C. Breitwieser, J. Wiliamson, C. Neuper and G. Müller-Putz, “Switching between manual control and brain-computer interface using long term and short term quality measures,” Frontiers in Neuroscience, pp. 1–11, 2012, https://doi.org/10.3389/fnins.2011.00147.

Y.-T. Pan, Z. Lamb, J. Macievich and K. A. Strausser, “A vibrotactile feedback device for balance rehabilitation in EksoGT robotic exoskeleton,” 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), pp. 569–576, 2018, doi: https://doi.org/10.1109/BIOROB.2018.8487677.

C. Freeman, E. Rogers, A.-M. Hughes, J. H. Burridge and K. Meadmore, “Iterative learning control in health care: electrical stimulation and robotic-assisted upper-limb stroke rehabilitation,” IEEE Control Syst., pp. 18–43, 2012, doi: https://doi.org/10.1109/MCS.2011.2173261.

A. J. del-Ama, Á. Gil-Agudo, J. L. Pons and et al., “Hybrid FES-robot cooperative control of ambulatory gait rehabilitation exoskeleton,” J NeuroEngineering Rehabil, 2014, https://doi.org/10.1186/1743-0003-11-27.

S. A. Murray, R. J. Farris, M. Golfarb, C. Hartigan, C. Kandilakis and D. Truex, “FES coupled with a powered exoskeleton for cooperative muscle contribution in persons with paraplegia,” Annu Int Conf IEEE Eng Med Biol Soc, pp. 2788–2792, 2018, doi: https://doi.org/10.1109/EMBC.2018.8512810.

A. J. del-Ama, A. D. Koutsou, J. C. Moreno, A. de-los-Reyes, A. Gil-Agudo and J. L. Pons, “Review of hybrid exoskeletons to restore gait following spinal cord injury,” J Rehabil Res Dev, pp. 497–514, 2012, doi: https://doi.org/10.1682/jrrd.2011.03.0043.

Wenxiu P, Wang P, Xiaohui S, Xiaopei S, Qing X. The effects of combined low frequency repetitive transcranial magnetic stimulation and motor imagery on upper extremity motor recovery following stroke. Front Neurol. 2019. https://doi.org/10.3389/fneur.2019.00096.

O. M. Giggins, U. M. Persson and B. Caulfield, “Biofeedback in rehabilitation,” J NeuroEngineering Rehabil, 200113, https://doi.org/10.1186/1743-0003-10-60.

Miller KJ, Gallina A, Neva JL, Ivanova TD, Snow NJ, Ledwell NM, Xiao ZG, Menon C, Boyd LA, Garland SJ. Effect of repetitive transcranial magnetic stimulation combined with robot-assisted tarining on wrist muscle Activation post-stroke. Clin Neurophysiol. 2019. https://doi.org/10.1016/j.clinph.2019.04.712.

I. Akkaya, M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, M. Plappert, G. Powell, R. Ribas, J. Schneider, N. Tezak, J. Tworek, P. Welinder, L. Weng and Q. Yuan, “Solving rubik’s cube with a robot hand,” 2019. [Online]. Available: https://arxiv.org/pdf/1910.07113v1.pdf. [Accessed 21 February 2022].

J. Kober, J. A. Bagnell and J. Peters, “Reinforcement learning in robotics: a survey,” The International Journal of Robotics Research, pp. 1238–1274, 2013, https://doi.org/10.1177/0278364913495721.

G. Dulac-Arnold, N. Levine, D. J. Mankowitz, J. Li, C. Paduraru, S. Gowal and T. Hester, “An empirical investigation of the challenges of real-world reinforcement learning,” 2020. [Online]. Available: https://arxiv.org/pdf/2003.11881.pdf. [Accessed 21 February 2022].

C. Daniel, M. Viering, J. Metz, O. Kroemer and J. Peters, “Active reward learning,” Proceedings of Robotics: Science and Systems, 2014.

E. Biyik, D. P. Losey, M. Palan, N. C. Landolfi, G. Shevchuk and D. Sadigh, “Learning reward functions from diverse sources of human feedback: optimally integrating demonstrations and preferences,” The International Journal of Robotics Research, pp. 45–67, 2022, https://doi.org/10.1177/02783649211041652.

J. Lin, Z. Ma, R. Gomez, K. Nakamura, B. He and G. Li, “A review on interactive reinforcement learning from human social feedback,” IEEE Access, pp. 120757–120765, 2020, doi: https://doi.org/10.1109/ACCESS.2021.3096662.

S. K. Kim, E. A. Kirchner, A. Stefes and F. Kirchner, “Intrinsic interactive reinforcement learning—using error-related potentials for real world human-robot interaction,” Scientific Reports, p. 17562, 2017, https://doi.org/10.1038/s41598-017-17682-7.

T. Luo, Y. Fan, J. Lv and C. Zhou, “Deep reinforcement learning from error-related potentials via an EEG-based brain-computer interface,” IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 697–701, 2018, doi: https://doi.org/10.1109/BIBM.2018.8621183.

I. Iturrate, R. Chavarriaga, L. Montesano, J. Minguez and J. del R. Millán, “Teaching brain-machine interface as an alternative paradigm to neuroprosthetics control,” Scientific Reports, p. 13893, 2015, https://doi.org/10.1038/srep13893.

S. K. Ehrlich and G. Cheng, “Human-agent co-adaptation using error-related potentials,” Journal of Neural Engineering, p. 066014, 2018, https://doi.ord/https://doi.org/10.1088/1741-3553/aae069.

A. L. Thomaz, G. Hoffman and C. Breazeal, “Real-time interactive reinforcement learning for robots,” Proceedings of AAAI Workshop on Human Comprehensible Machine Learning, 2005.

C. Stahlhut, N. Navarro-Guerrero, C. Weber and S. Wermter, “Interaction in reinforcement learning reduces the need for finely tuned hyperparameters in complex tasks,” Kognitive Systeme, 2015, https://doi.org/10.17185/duepublico/40718.

C. Arzate Cruz and T. Igarashi, “A survey of interactive reinforcement learning: design principles and open challenges,” Proceedings of the 2020 ACM Designing Interactive Sytsmes Conference, pp. 1195–1209, 2020, https://doi.org/10.1145/3357236.3395525.

S. K. Kim and E. A. Kirchner, “Classifier transferability in the detection of error related potentials from observation to interaction,” IEEE International Conference on Systems, Man, and Cybernetics (SMC’13), pp. 3360–3365, 2013, https://doi.ord/https://doi.org/10.1109/SMC.2013.573.

S. K. Kim and E. A. Kirchner, “Handling few training data: classifier transfer between different types of error-related potentials,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, pp. 320–332, 2016, https://doi.org/10.1109/TNSRE.2015.2507868.

R. Chavarriaga, A. Sobolewski and J. d. R. Millán, “Errare. machinale est: the use of error-related potentials in brain-machine interfaces,” Frontiers in Neuroscience, 2014, https://doi.org/10.3389/fnins.2014.00208.

Wessel JR. Error awareness and the error-related negativity: evaluating the first decade of evidence. Front Hum Neurosci. 2012. https://doi.org/10.3389/fnhum.2012.00088.

Spüler M, Niethammer C. Error-related potentials during continuous feedback: using EEG to detect errors of different type and severity. Front Hum Neurosci. 2015. https://doi.org/10.3389/fnhum.2015.00155.

• A. Kumar, Q. Fang, J. Fu, E. Pirogova and X. Gu, “Error-Related Neural Responses Recorded by Electroencephalography During Post-stroke Rehabilitation Movements,” Frontiers in Neurorobotics, 2019, https://doi.org/10.3389/fnbot.2019.00107. Although experimental set up might need improvement, this work is relevant since it investigates intrinsic human signals in the EEG associated with self-evaluation of success of rehabilitation training that could potentially be used to automatically adapt support by a rehabilitation robot

Thông tin
Thông tin xuất bản

Current Robotics Reports

Tập 3

21-32

Thông tin tác giả