Advances in robotics research

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Challenges in neuro-machine interaction based active robotic rehabilitation of stroke patients

  • Song, Aiguo (School of Instrument Science and Engineering, Southeast University) ;
  • Yang, Renhuan (School of Instrument Science and Engineering, Southeast University) ;
  • Xu, Baoguo (School of Instrument Science and Engineering, Southeast University) ;
  • Pan, Lizheng (School of Instrument Science and Engineering, Southeast University) ;
  • Li, Huijun (School of Instrument Science and Engineering, Southeast University)
  • Received : 2013.04.01
  • Accepted : 2013.12.20
  • Published : 2014.04.25
⟨ Previous Next ⟩

Abstract

Study results in the last decades show that amount and quality of physical exercises, then the active participation, and now the cognitive involvement of patient in rehabilitation training are known of crux to enhance recovery outcome of motor dysfunction patients after stroke. Rehabilitation robots mainly have been developing along this direction to satisfy requirements of recovery therapy, or focusing on one or more of the above three points. Therefore, neuro-machine interaction based active rehabilitation robot has been proposed for assisting paralyzed limb performing designed tasks, which utilizes motor related EEG, UCSDI (Ultrasound Current Source Density Imaging), EMG for rehabilitation robot control and feeds back the multi-sensory interaction information such as visual, auditory, force, haptic sensation to the patient simultaneously. This neuro-controlled and perceptual rehabilitation robot will bring great benefits to post-stroke patients. In order to develop such kind of robot, some key technologies such as noninvasive precise detection of neural signal and realistic sensation feedback need to be solved. There are still some grand challenges in solving the fundamental questions to develop and optimize such kind of neuro-machine interaction based active rehabilitation robot.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

References

  1. Blaya, J.A. and Herr, H. (2004), "Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait", IEEE T. Neur. Sys. Reh., 12(1), 24-31. https://doi.org/10.1109/TNSRE.2003.823266
  2. Burgar, C.G., Lum, P.S. and Shor, P.C. (2000), "Development of robots for rehabilitation therapy: the Polo Altova/Stanford experience", J. Rehabil. Res. Dev., 37(6), 663-673.
  3. Cai, L.L., Fong, A.J., Otoshi, C.K., Liang, Y., Burdick, J.W., Roy, R.R. and Edgerton, V.R. (2006), "Implications of assist-as-needed robotic step training after a complete spinal cord injury on intrinsic strategies of motor learning", J. Neurosci., 26(41),10564-10568. https://doi.org/10.1523/JNEUROSCI.2266-06.2006
  4. Carel, C., Loubinoux, I., Boulanouar, K., Manelfe, C. and Rascol, O. (2000), "Neural substrate for the effects of passive training on sensorimotor cortical representation: a study with functional magnetic resonance imaging in healthy subjects", J. Cereb. Blood Flow Metab., 20(3), 478-484. https://doi.org/10.1097/00004647-200003000-00006
  5. Crespo, L.M and Reinkensmeyer, D.J. (2000), "Review of control strategies for robotic movement training after neurologic injury", J. Neuro Eng. Rehabil., 6, doi:10.1186/1743-0003-6-20.
  6. Carey, J.R., Kimberley, T.J., Lewis, S.M., Auerbach, E.J. and Dorsey, L. (2002), "Analysis of fMRI and finger tracking training in subjects with chronic stroke", Brain, 125(4), 773-788. https://doi.org/10.1093/brain/awf091
  7. Duygun, E., Vishnu, M. and Nilanjan, S. (2005), "A new control approach to robot assisted rehabilitation", The 2005 IEEE 9th International Conference on Rehabilitation Robotics, Chicago, USA, June.
  8. Eilenberg, M.F., Geyer, H. and Herr, H. (2010), "Control of a powered ankle-foot prosthesisbased on a neuromuscular model", IEEE T. Neur. Sys. Reh., 18(2), 164-173. https://doi.org/10.1109/TNSRE.2009.2039620
  9. Ferilli, M., Rossini, L. and Rossini, P.M. (2012), "A non-invasive tool for brain-plasiticity-based therapy: transcranial magnetic stimulation in post-stroke rehabilitation", The fourth IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, Rome, Italy, June.
  10. Frackowiak, R.S.J. (2002), "Imaging neuroscience: Lessons from studies of brain plasticity", The 5th IEEE EMBS International Summer School on Biomedical Imaging, London, UK, June.
  11. Glanz, M., Klawansky, S., Stason, W., Berkey, C. and Chalmers, T.C. (1996), "Functional electrostimulation in post-stroke rehabilitation: a meta-analysis of the randomized controlled trials", Arch. Phys. Med. Rehabil., 77(6), 549-553. https://doi.org/10.1016/S0003-9993(96)90293-2
  12. Guadagnoli, M. and Lee, T. (2004), "Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning", J. Mot. Behav., 36(2), 212-224. https://doi.org/10.3200/JMBR.36.2.212-224
  13. He, B. (2004), Modeling and imaging of bioelectrical activity-principles and applications, Kluwer Academic Publishers.
  14. He, B. (2005), Neural engineering, Norwell, MA: Kluwer/Plenum.
  15. Huang, H.P., Huang, T.H., Liu, Y.H., Kang, Z.H. and Teng, J.T. (2011), "A brain-controlled rehabilitation system with multiple kernel learning", The 2011 IEEE International Conference on Systems, Man and Cybernetics, Taipei, Taiwan, October.
  16. Holden, M. (2005), "Virtual environments for motor rehabilitation: review", Cyberpsychol. Behav., 8(3), 187-211. https://doi.org/10.1089/cpb.2005.8.187
  17. He, B., Yang, L., Wilke, C. and Yuan, H. (2011a), "Electrophysiological imaging of brain activity and connectivity-challenges and opportunities", IEEE T. Biomed. Eng., 58(7), 1918-1931. https://doi.org/10.1109/TBME.2011.2139210
  18. Johansson, B.B. (2012), "Multisensory stimulation in stroke rehabilitation", Front. Human Neurosci., 6(60), doi: 10.3389/fnhum.2012.00060.
  19. Johansson, B.B., Haker, E., Arbin, M.V. and Britton, M. (2001), "Acupuncture and trancutaneous nerve stimulation in stroke rehabilitation", Stroke, 32(3), 707-713. https://doi.org/10.1161/01.STR.32.3.707
  20. Kahn, L.E., Rymer, W.Z. and Reinkensmeyer, D.J. (2004), "Adaptive assistance for guided force training in chronic stroke", The 26th Annual International Conference of the IEEE EMBS, San Francisco, USA, September.
  21. Krebs, H.I., Hogan, N., Aisen, M.L. and Volpe, B.T. (1998), "Robot-aided neurorehabilitation", IEEE T. Rehab. Eng, 6(2), 75-87. https://doi.org/10.1109/86.662623
  22. Krebs, H.I., Volpe, B.T., Aisen, M.L. and Hogan, N. (2000), "Increasing productivity and quality of care: robot-aided neuro-rehabilitation", J. Rehabil. Res. Dev., 37(6), 639-652.
  23. Kwakkel, G., Kollen, B.J. and Wagenaar, R.C. (1999), "Therapy impact on functional recovery in stroke rehabilitation: a critical review of the literature", Physiotherapy, 13, 377-379.
  24. Kwakkel, G., Wagenaar, R.C., Twisk, J.W., Lankhorst, G.J. and Koetsier, J.C. (1999), "Intensity of leg and arm training after primary middle-cerebralartery stroke: a randomised trial", Lancet, 354, 191-196. https://doi.org/10.1016/S0140-6736(98)09477-X
  25. Lang, A.K., Luft, A.R., Sawaki, L., Burstein, A.H., Sohn, Y.H. and Cohen, L.G. (2002), "Modulation of human corticomotor excitability by somatosensory input", J. Physiol., 540(2), 623-633. https://doi.org/10.1113/jphysiol.2001.012801
  26. Lenzi, T., Rossi, S.D., Vitiello, N. and Carrozza, M.C. (2012), "Intention-based EMG control for powered exoskeletons", IEEE T. Biomed. Eng., 59(8), 2180-2190. https://doi.org/10.1109/TBME.2012.2198821
  27. Li, T., Song, A.G. and Fei, S.M. (2011), "Master-slave synchronization for delayed lurie systems using timedelay feedback control", Asian J. Control, 13(6), 879-892. https://doi.org/10.1002/asjc.198
  28. Li, T., Song, A.G., Xue, M. and Zhang, H. (2011), "Stability analysis on delayed neural networks based on an improved delay-partitioning approach", J. Comput. Appl. Math., 235(9), 3086-3095. https://doi.org/10.1016/j.cam.2010.10.002
  29. Lindberg, P., Schmitz, C., Forssberg, H., Engardt, M. and Borg, J. (2004), "Effects of passive-active movement training on upper limb motor function and cortical activation in chronic patients with stroke: a pilot study", J. Rehabil. Med., 36(3), 117-123. https://doi.org/10.1080/16501970410023434
  30. Lum, P.S., Burgar, C.G. and Loos, M.V. (1997), "The use of a robotic device for post-stroke movement therapy", The Conference on Rehabilitation Robotics, Bath, U.K., 107-110.
  31. Lum, P.S., Burgar, C.G., Shor, P.C. and Majmundar, M. (2002), "Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke", Arch. Phys. Med. Rehabil., 83(7), 952-959. https://doi.org/10.1053/apmr.2001.33101
  32. Lunenburger, L., Colombo, G. and Riener, R. (2007), "Biofeedback for robotic gait rehabilitation", J. Neuro Eng. Rehabil., 4(1), doi: 10.1186/1743-0003-4-1.
  33. Ma, J.Q. and Song, A.G. (2011), "Development of a novel two-axis force sensor for Chinese massage robot", The 3rd International Conference on Precision Instrumentation and Measurement, Xiangtan, China, July.
  34. Marshall, R.S., Perera, G.M., Lazar, R.M., Krakauer, J.W., Constantine, R.C. and DeLaPaz, R.L. (2000), "Evolution of cortical activation during recovery from corticospinal tract infarction", Stroke, 31(3), 656-661. https://doi.org/10.1161/01.STR.31.3.656
  35. Mauro, A.D., Carrasco, E., Oyarzun, D. and Ardanza, A. (2012), "Advanced hybrid technology for neurorehabilitation: the HYPER project", Advances in Robotics & Virtual Reality, Springer-Verlag Berlin Heidelberg, 89-108.
  36. Metrailler, P., Brodard, R., Stauffer, Y., Clavel, R. and Frischknecht, R. (2007), "Cyberthosis: rehabilitation robotics with controlled electrical muscle stimulation", Rehabilitation robotics, 303-318.
  37. Michel, V.E.G., Driessen, B.J.F. and Michel, D. (2005), "A motorized gravity compensation mechanism used for active rehabilitation of upper limbs", The 2005 IEEE 9th International Conference on Rehabilitation Robotics, Chicago, USA, June.
  38. Nudo, R.J., Plautz, E.J. and Frost, S.B. (2001), "Role of adaptive plasticity in recovery of function after damage to motor cortex", Muscle Nerve, 24(8), 1000-1019. https://doi.org/10.1002/mus.1104
  39. Olafsson, R., Witte, R.S., Huang, S.W. and Donnell, M.O. (2008), "Ultrasound current source density imaging", IEEE T. Biomed. Eng., 55(7), 1840-1848. https://doi.org/10.1109/TBME.2008.919115
  40. Olafsson, R., Witte, R.S., Jia, C.X., Huang, S.W., Kim, K. and Donnell, M.O. (2009), "Cardiac activation mapping using ultrasound current source density imaging (UCSDI)", IEEE T. Ultrason. Ferr., 56(3), 565-574. https://doi.org/10.1109/TUFFC.2009.1073
  41. Olafsson, R., Witte, R.S., Kim, K., Ashkenazi, S. and Donnell, M.O. (2006), "Electric current mapping using the acoustoelectric effect", Medical Imaging 2006: Ultrasonic Imaging and Signal Processing, San Diego, USA, February.
  42. Pan, L.Z., Song, A.G. and Xu, G.Z. (2011), "Robot-assisted upper-limb fuzzy adaptive passive movement training and clinical experiment", Appl. Mech. Mater., 130, 227-231.
  43. Parker, J., Mountain, G. and Hammerton, J. (2011), "A review of the evidence underpinning the use of visual and auditory feedback for computer technology in post-stroke upper-limb rehabilitation", Disabil. Rehabil., 6, 465-472. https://doi.org/10.3109/17483107.2011.556209
  44. Pennycott, A., Hunt, K.J., Jack, L.P., Perret, C. and Kakebeeke, T.H. (2009), "Estimation and volitional feedback control of active work rate during robot-assisted gait", Control Eng. Pract, 17(2), 322-328. https://doi.org/10.1016/j.conengprac.2008.09.011
  45. Platz, T., Winter, T., Muller, N., Pinkowski, C., Eickhof, C. and Mauritz, K.H. (2001), "Arm ability training for stroke and traumatic brain injury patients with mild arm paresis: a single-blind, randomized, controlled trial", Arch. Phys. Med. Rehabil., 82(7),961-968. https://doi.org/10.1053/apmr.2001.23982
  46. Qian, Y., Song, A.G. and Yan, R.Q. (2011), "Design and realization of an array pulse detecting tactile sensor", Instrumentation and Measurement Technology Conference (I2MTC), Hangzhou, China, May.
  47. Reinkensmeyer, D.J., Kahn, L.E. and Arerbuch, M. (2000), "Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM Guide", J. Rehabil. Res. Dev., 37(6), 653-662.
  48. Ridding, M.C., Brouwer, B., Miles, T.S., Pitcher, J.B. and Thompson, P.D. (2000), "Changes in muscle responses to stimulation of the motor cortex induced by peripheral nerve stimulation in human subjects", Exp. Brain Res., 131(1), 135-143. https://doi.org/10.1007/s002219900269
  49. Riener, R., Lunenburger, L., Jezernik, S. and Anderschitz, M. (2005), "Patient-cooperative strategies for robot-aided treadmill training: first experimental results", IEEE T. Neur. Sys. Reh., 13(3), 380-394. https://doi.org/10.1109/TNSRE.2005.848628
  50. Riener, R., Nef, T. and Colombo, G. (2005), "Robot-aided neurorehabilitation of the upper extremities", Med. Biol. Eng. Comput., 43(1), 2-10. https://doi.org/10.1007/BF02345116
  51. Saposnik, G. and Levin, M. (2011), "Virtual reality in stroke rehabilitation: a meta-analysis and implications for clinicians", Stroke, 42(5), 1380-1386. https://doi.org/10.1161/STROKEAHA.110.605451
  52. Shaw, S.E., Morris, D.M., Uswatte, G., McKay, S., Meythaler, J.M. and Taub, E. (2005), "Constraintinduced movement therapy for recovery of upper-limb function following traumatic brain injury", J. Rehabil. Res Dev., 42(6),769-778. https://doi.org/10.1682/JRRD.2005.06.0094
  53. Sonde, L., Kalimo, H., Fernaeus, S.E. and Viittanen, M. (2000), "Low TENS treatment on post-stroke paretic arm: a three-year follow-up", Clin. Rehabil., 14(1), 14-19. https://doi.org/10.1191/026921500673534278
  54. Sugarman, H., Dayan, E., Lauden, A., Eichler, A.W. and Tiran, J. (2008), "Investigating the use of force feedback joysticks for low-cost, robotmediated therapy", Int. J. Disabil. Human Dev., 7(1), 95-100.
  55. Sumi, C. (2009), "Expression for noninvasive measurement of internal map of local Joule heat consumption using acousto-electric effect", Acoust. Sci. Technol., 30(4), 310-312. https://doi.org/10.1250/ast.30.310
  56. Taub, E., Uswatte, G. and Elbert, T. (2002), "New treatments in neurorehabilitation founded on basic research", Nature Rev. Neurosci, 3(3), 228-236. https://doi.org/10.1038/nrn754
  57. Tan, H.G., Zhang, H.H., Wang, C.C., Shee, C.Y., Ang, W.T. and Guan, C.T. (2008), "Arm flexion and extension exercises using a brain-computer interface and functional electrical stimulation", The Sixth IASTED International Conference on Biomedical Engineering, Innsbruck, Austria, February.
  58. Wang, C., Phua, K.S. and Ang, K.K. (2009), "A feasibility study of non-invasive motor-imagery BCI-based robotic rehabilitation for stroke patients", The 4th IEEE International Conference on Neural Engineering, Antalya, Turkey, April.
  59. Wang, L. and Buchanan, T.S. (2002), "Prediction of joint moments using a neural network model of muscle activations from EMG signals", IEEE T. Neur. Sys. Reh., 10(1), 30-37. https://doi.org/10.1109/TNSRE.2002.1021584
  60. Wang, Z.H., Olafsson, R., Ingram, P., Li, Q. and Witte, R.S. (2011), "Four-dimensional ultrasound current source density imaging of a dipole field", Appl. Phys. Lett., 99(11), 113701. https://doi.org/10.1063/1.3632034
  61. Weiller, C. (1998), "Imaging functional recovery from stroke", Exp. Brain Res., 123(1), 13-17. https://doi.org/10.1007/s002210050539
  62. Weiss, P., Kizony, R., Feintuch, U. and Katz, N. (2006), "Virtual reality in neurorehabilitation", Textbook Neur. Repair Neurorehabil., 2, 182-197. https://doi.org/10.1017/CBO9780511545078.015
  63. Witte, R.S., Olafsson, R. and Donnell, M.O. (2006), "Acousto-electric detection of current flow in a neural recording chamber", The IEEE Int. Ultrason. Symp., 5-8.
  64. Witte, R.S., Olafsson, R., Huang, S.W. and Donnell, M.O. (2007), "Imaging current flow in lobster nerve cord using the acousto-electric effect", Appl. Phy. Lett., 90(16), 163902. https://doi.org/10.1063/1.2724901
  65. Xiong, P.W., Song, A.G., Qian, K., Zhang, L.X. and Xu, X.N. (2012), "Operation modes and control schemes for a telerobot with time delay", Int. J. Adv. Robot. Syst., 9(57), doi: 10.5772/50649.
  66. Xu, B.G., Peng, S., Song, A.G., Yang, R.H. and Pan, L.Z. (2011), "Robot-aided upper-limb rehabilitation based on motor imagery EEG", Int. J. Adv. Robot. Syst., 8(4), 88-97.
  67. Xu, G.Z., Song, A.G. and Li, H.J. (2011a), "Control system design for an upper-limb rehabilitation robot", Adv. Robot., 25(1), 229-251. https://doi.org/10.1163/016918610X538561
  68. Xu, G.Z., Song, A.G. and Li, H.J. (2011b), "Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural networks", J. Intell. Robot Syst., 62(3), 501-525. https://doi.org/10.1007/s10846-010-9462-3
  69. Xu, G.Z., Song, A.G., Pan, L.Z., Li, H.J., Liang, Z.W., Zhu, S.H., Xu, B.G. and Li, J.F. (2012), "Adaptive hierarchical control for the muscle strength training of stroke survivors in robot-aided upper-limb rehabilitation", Int. J. Adv. Robot. Syst., 9(122), 2071-2083.
  70. Yang, R.H., Song, A.G. and Xu, B.G. (2010), "Feature extraction of motor imagery EEG based on wavelet transform and higher-order statistics", Wavelets Multiresolution Inform. Process., 8(3), 373-384. https://doi.org/10.1142/S0219691310003535
  71. Yang, R.H., Li, X., Liu, J. and He, B. (2011), "3D current source density imaging based on the acoustoelectric effect: a simulation study using unipolar pulses", Phys. Med. Biol., 56(13), 3825-3842. https://doi.org/10.1088/0031-9155/56/13/006
  72. Yang, R.H., Li, X., Song, A.G., He, B. and Yan, R.Q. (2012a), "A 3D reconstruction solution to current density imaging based on acoustoelectric effect by deconvolution: a simulation study", IEEE T. Biomed. Eng., 59(12), doi: 10.1109/TBME.2012.2228641.
  73. Yang, R.H., Li, X., Song, A.G,, He, B. and Yan, R.Q. (2012b), "Three-dimensional noninvasive ultrasound Joule heat tomography based on the acousto-electric effect using unipolar pulses: a simulation study", Phys. Med. Biol., 57(22), 7689-7708. https://doi.org/10.1088/0031-9155/57/22/7689
  74. Zajac, F.E. (1989), "Muscle and tendon: Properties, models, scaling, and application to biomechanics and motor control", Crit. Rev. Biomed. Eng., 17(4), 359-411.
  75. Zhang, H. and Wang, L.H. (2004), "Acousto-electric tomography," Photons plus Ultrasound: Imaging and Sensing, San Jose, USA, January.
  76. Zhang, Y.B., Wang, Z.X., Ji, L.H. and Bi, S. (2005), "The clinical application of the upper extremity compound movements rehabilitation training robot", The 2005 IEEE 9th International Conference on Rehabilitation Robotics, Chicago, USA, June.