FEED FORWARD NEURAL NETWORK BCI-BASED TRAJECTORY-CONTROLLED LOWER-LIMB EXOSKELETON: A BIOMECHATRONICS APPROACH, 430-440.

Ganesh Roy, Dinesh Bhatia, and Subhasis Bhaumik

References

  1. [1] R. Sinha, W.J.A. van den Heuvel, and P. Arokiasamy, Factorsaffecting quality of life in lower limb amputees, Prosthetics andOrthotics International, 35(1), 2011, 90–96.
  2. [2] R. Bionics, REX BIONICS product information,https://www.rexbionics.com/product-information/ (accessedFeb. 4, 2021).
  3. [3] R. Robotics, ReWalk: More than walking, https://rewalk.com/rewalk-personal-3/ (accessed Feb. 2, 2021).
  4. [4] H. Kawamoto and Y. Sankai, Power assist method based onphase sequence and muscle force condition for HAL, AdvancedRobotics, 19(7), 2005, 717–734.
  5. [5] A. Zoss, H. Kazerooni, and A. Chu, On the mechanical designof the Berkeley lower extremity exoskeleton (BLEEX), Proc.2005 IEEE/RSJ International Conf. on Intelligent Robots andSystems, Edmonton, AB, 2005, 3465–3472.
  6. [6] S. Wang, L. Wang, C. Meijneke, E. Van Asseldonk, T.Hoellinger, G. Cheron, Y. Ivanenko, V. La Scaleia, F.Sylos-Labini, M. Molinari, F. Tamburella, I. Pisotta, F.Thorsteinsson, M. Ilzkovitz, J. Gancet, Y. Nevatia, R. Hauffe,F.Zanow, and H. van der Kooij, Design and control of themindwalker exoskeleton, IEEE Transactions on Neural Systemsand Rehabilitation Engineering, 23(2), 2014, 277–286.
  7. [7] W. Huo, S. Mohammed, J.C. Moreno, and Y. Amirat, Lowerlimb wearable robots for assistance and rehabilitation: A stateof the art, IEEE Systems Journal, 10(3), 2016, 1068–1081.
  8. [8] G. Roy and S. Bhaumik, Classification of MI EEG signal usingminimum set of channels to control a lower limb assistivedevice, Journal of The Institution of Engineers (India): SeriesB, 2022, 1–7.
  9. [9] K. Renuga Devi and H. Hannah Inbarani, Neighborhood baseddecision theoretic rough set under dynamic granulation forBCI motor imagery classification, Journal on Multimodal UserInterfaces, 15(3), 2021, 301–321.
  10. [10] J. Hong and X. Qin, Wheelchair active rehabilitation methodfor lower limb of stroke patients, International Journal ofRobotics and Automation, 2022, 346–351.
  11. [11] A.J. Young and D.P. Ferris, State of the art and future directionsfor lower limb robotic exoskeletons, IEEE Transactions onNeural Systems and Rehabilitation Engineering, 25(2), 2016,171–182.
  12. [12] L. Hocoma, Lokomat-Hocoma, https://www.hocoma.com/solutions/lokomat/ (accessed Feb. 5, 2021).
  13. [13] R. Rea, C. Beck, R. Rovekamp, P. Neuhaus, and M. Diftler,X1: A robotic exoskeleton for in-space countermeasures anddynamometry, Proc. AIAA Space 2013 Conf. and Exposition,2013, 5510.
  14. [14] IHMC Robotics, Mina V2 by IHMC Robotics, http://robots.ihmc.us/mobility-exoskeletons (accessed Jan. 25, 2021).
  15. [15] D. Sanz-Merodio, M. Cestari, J. Carlos Arevalo, and E. Garcia,A lower-limb exoskeleton for gait assistance in quadriplegia,Proc. 2012 IEEE International Conf. on Robotics andBiomimetics (ROBIO), Guangzhou, 2012, 122–127.
  16. [16] S.K. Banala, S.H. Kim, S.K. Agrawal, and J.P. Scholz, Robotassisted gait training with active leg exoskeleton (ALEX),439IEEE Transactions on Neural Systems and RehabilitationEngineering, 17(1), 2008, 2–8.
  17. [17] J.F. Veneman, R. Kruidhof, E.E.G. Hekman, R. Ekkelenkamp,E.H.F. Van Asseldonk, and H. Van Der Kooij, Design andevaluation of the lopes exoskeleton robot for interactive gaitrehabilitation, IEEE Transactions on Neural Systems andRehabilitation Engineering, 15(3), 2007, 379–386.
  18. [18] G. Song, R. Huang, J. Qiu, H. Cheng, and S. Fan, Model-basedcontrol with interaction predicting for human-coupled lowerexoskeleton systems, Journal of Intelligent & Robotic Systems,100(2), 2020, 389–400.
  19. [19] M.A. G´alvez-Z´u˜niga and A. Aceves-L´opez, A review oncompliant joint mechanisms for lower limb exoskeletons,Journal of Robotics, 2016, 2016.
  20. [20] G. Roy, S. Mukherjee, T. Das, and S. Bhaumik, Single supportphase gait kinematics and kinetics for a humanoid lowerlimb exoskeleton, Proc. 2020 IEEE Region 10 Symposium(TENSYMP), Dhaka, 2020, 138–141.
  21. [21] S. Chattopadhyay, G. Roy, and M. Panda, Simple design ofa PID controller and tuning of its parameters using labviewsoftware, Sensors & Transducers, 129(6), 2011, 69–85.
  22. [22] G. Roy, A.K. Bhoi, S. Das, and S. Bhaumik, Cross-correlatedspectral entropy-based classification of EEG motor imagerysignal for triggering lower limb exoskeleton, Signal, Image andVideo Processing, 16(7), 2022, 1831–1839.
  23. [23] F. First, K.-R. Mller, B. Blankertz, C.B. Franklin, andG. Curio, Data set IVa for the BCI competition III(bbci.de), https://www.bbci.de/competition/iii/desc_IVa.html (accessed Dec. 24, 2019).
  24. [24] G. Roy, A.K. Bhoi, and S. Bhaumik, A comparative approachfor MI-based eeg signals classification using energy, power andentropy, IRBM, 43(5), 2022, 434–446.

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