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Assist-As-Needed Control of a Hip Exoskeleton, Using Central Pattern Generators in a Stride Management Strategy

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Abstract

This paper proposes a novel stride management strategy for rehabilitation exoskeletons. This method incorporates a Central Pattern Generator (CPG) into an Assist-As-Needed (AAN) controller to induce an optimal stride length for the wearers and optimize the energy consumption. Most AAN controllers rely on a predefined and fixed trajectory to measure the required amount of assistance. However, for a stride management approach, the trajectory should be updated regularly according to the wearer’s performance to induce the optimal stride length eventually. The proposed stride management strategy deals with this challenge by integrating a CPG into the control loop. The CPG updates the desired trajectory right after each swing phase. The AAN controller uses a recently introduced Strength Index (SI) for continuous measurement of the wearer’s ability in tracking the desired trajectory. A virtual tunnel around the desired trajectory is defined, and the tunnel boundaries are adjusted according to the SI. Then, an adaptive impedance controller determines the assistive force according to the distance between the actual trajectory of the user and the tunnel boundaries. The performance of the proposed method is evaluated in OpenSim software, and reductions in the metabolic cost and muscles’ activity are observed.

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References

  1. Kalani, H., Moghimi, S., Akbarzadeh, A.: Towards an SEMG-based tele-operated robot for masticatory rehabilitation. Comput. Biol. Med. 75, 243–256 (2016)

    Article  Google Scholar 

  2. Chen, Q., Zi, B., Sun, Z., Li, Y., Xu, Q.: Design and development of a new cable-driven parallel robot for waist rehabilitation. IEEE/ASME Trans. Mechatron. 24(4), 1497–1507 (2019)

    Article  Google Scholar 

  3. Washabaugh, E.P., Guo, J., Chang, C.-K., Remy, C.D., Krishnan, C.: A portable passive rehabilitation robot for upper-extremity functional resistance training. IEEE Trans. Biomed. Eng. 66(2), 496–508 (2018)

    Article  Google Scholar 

  4. Mazzoleni, S., Tran, V.-D., Dario, P., Posteraro, F.: Wrist robot-assisted rehabilitation treatment in subacute and chronic stroke patients: from distal-to-proximal motor recovery. IEEE Trans. Neural Syst. Rehabil. Eng. 26(9), 1889–1896 (2018)

    Article  Google Scholar 

  5. Kardan, I., Akbarzadeh, A.: Robust output feedback assistive control of a compliantly actuated knee exoskeleton. Robot. Auton. Syst. 98, 15–29 (2017)

    Article  Google Scholar 

  6. Banala, S.K., Agrawal, S.K., Scholz, J.P.: Active Leg Exoskeleton (ALEX) for gait rehabilitation of motor-impaired patients. In: 2007 IEEE 10th international conference on rehabilitation robotics 2007, pp. 401–407. IEEE

  7. Riener, R., Lunenburger, L., Jezernik, S., Anderschitz, M., Colombo, G., Dietz, V.: Patient-cooperative strategies for robot-aided treadmill training: first experimental results. IEEE Trans. Neural Syst. Rehabil. Eng. 13(3), 380–394 (2005)

    Article  Google Scholar 

  8. Hussain, S., Jamwal, P.K., Ghayesh, M.H., Xie, S.Q.: Assist-as-needed control of an intrinsically compliant robotic gait training orthosis. IEEE Trans. Industr. Electron. 64(2), 1675–1685 (2016)

    Article  Google Scholar 

  9. Lopes, J.M., Figueiredo, J., Pinheiro, C., Reis, L.P., Santos, C.P.: Biomechanical assessment of adapting trajectory and human-robot interaction stiffness in impedance-controlled ankle orthosis. J. Intell. Rob. Syst. 102(4), 76 (2021). https://doi.org/10.1007/s10846-021-01423-0

    Article  Google Scholar 

  10. Asl, H.J., Narikiyo, T., Kawanishi, M.: An assist-as-needed control scheme for robot-assisted rehabilitation. In: 2017 American control conference (acc) 2017, pp. 198–203. IEEE

  11. Asl, H.J., Yoon, J.: Stable assist-as-needed controller design for a planar cable-driven robotic system. Int. J. Control Autom. Syst. 15(6), 2871–2882 (2017). https://doi.org/10.1007/s12555-016-0492-x

    Article  Google Scholar 

  12. Duschau-Wicke, A., Von Zitzewitz, J., Caprez, A., Lunenburger, L., Riener, R.: Path control: a method for patient-cooperative robot-aided gait rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 18(1), 38–48 (2010)

    Article  Google Scholar 

  13. Naghavi, N., Akbarzadeh, A., Tahamipour-Z, S.M., Kardan, I.: Assist-As-Needed control of a hip exoskeleton based on a novel strength index. Robot. Autonomous Syst. 134, 103667 (2020). https://doi.org/10.1016/j.robot.2020.103667

    Article  Google Scholar 

  14. Ferdowsi University of Mashhad, Robotics Laboratory, HEXA Project. https://www.fumrobotics.ir/projects/fumhexa/.

  15. Taherifar, A., Vossoughi, G., Ghafari, A.S.: Variable admittance control of the exoskeleton for gait rehabilitation based on a novel strength metric. Robotica 36(3), 427–447 (2018). https://doi.org/10.1017/S0263574717000480

    Article  Google Scholar 

  16. Emken, J.L., Bobrow, J.E., Reinkensmeyer, D.J.: Robotic movement training as an optimization problem: designing a controller that assists only as needed. In: 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005. 2005, pp. 307–312. IEEE

  17. Emken, J.L., Benitez, R., Reinkensmeyer, D.J.: Human-robot cooperative movement training: learning a novel sensory motor transformation during walking with robotic assistance-as-needed. J. Neuroeng. Rehabil. 4(1), 8 (2007)

    Article  Google Scholar 

  18. Carmichael, M.G., Liu, D.: Estimating physical assistance need using a musculoskeletal model. IEEE Trans. Biomed. Eng. 60(7), 1912–1919 (2013)

    Article  Google Scholar 

  19. Ijspeert, A.J., Nakanishi, J., Hoffmann, H., Pastor, P., Schaal, S.: Dynamical movement primitives: learning attractor models for motor behaviors. Neural Comput. 25(2), 328–373 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  20. Tutsoy, Ö.: Cpg Based RL Algorithm Learns to Control of a humanoid robot leg. Int. J. Robotics Autom. 30 (2015).

  21. Akkawutvanich, C., Knudsen, F.I., Riis, A.F., Larsen, J.C., Manoonpong, P.: Adaptive parallel reflex- and decoupled CPG-based control for complex bipedal locomotion. Robot. Autonomous Syst. 134, 103663 (2020). https://doi.org/10.1016/j.robot.2020.103663

    Article  Google Scholar 

  22. Gui, K., Liu, H., Zhang, D.: Toward multimodal human-robot interaction to enhance active participation of users in gait rehabilitation. IEEE Trans. Neural Syst. Rehabil. Eng. 25(11), 2054–2066 (2017). https://doi.org/10.1109/TNSRE.2017.2703586

    Article  Google Scholar 

  23. Zanotto, D., Stegall, P., Agrawal, S.K.: Adaptive assist-as-needed controller to improve gait symmetry in robot-assisted gait training. In: 2014 IEEE international conference on robotics and automation (ICRA) 2014, pp. 724–729. IEEE

  24. Ahmed, A.I., Cheng, H., Liangwei, Z., Omer, M., Lin, X.: On-line walking speed control in human-powered exoskeleton systems based on dual reaction force sensors. J. Intell. Robot. Syst. 87(1), 59–80 (2017). https://doi.org/10.1007/s10846-017-0491-z

    Article  Google Scholar 

  25. Santos, C.P., Alves, N., Moreno, J.C.: Biped locomotion control through a biomimetic cpg-based controller. J. Intell. Rob. Syst. 85(1), 47–70 (2017). https://doi.org/10.1007/s10846-016-0407-3

    Article  Google Scholar 

  26. Song, G., Huang, R., Qiu, J., Cheng, H., Fan, S.: Model-based control with interaction predicting for human-coupled lower exoskeleton systems. J. Intell. Rob. Syst. 100(2), 389–400 (2020). https://doi.org/10.1007/s10846-020-01200-5

    Article  Google Scholar 

  27. Han, Y., Zhu, S., Gao, H., Wu, Z., Xu, Y., Zhou, W.: The swing control of knee exoskeleton based on admittance model and nonlinear oscillator. J. Intell. Rob. Syst. 99(3), 747–756 (2020). https://doi.org/10.1007/s10846-019-01133-8

    Article  Google Scholar 

  28. Khodaei-Mehr, J., Sharifi, M., Mushahwar, V.K., Tavakoli, M.: Intelligent Locomotion planning with enhanced postural stability for lower-limb exoskeletons. IEEE Robotics and Automation Letters, 1–1 (2021). https://doi.org/10.1109/LRA.2021.3098915

  29. Mokhtari, M., Taghizadeh, M., Mazare, M.: Hybrid adaptive robust control based on CPG and ZMP for a lower limb exoskeleton. Robotica 39(2), 181–199 (2021). https://doi.org/10.1017/S0263574720000260

    Article  Google Scholar 

  30. Tanaka, N., Matsushita, S., Sonoda, Y., Maruta, Y., Fujitaka, Y., Sato, M., Simomori, M., Onaka, R., Harada, K., Hirata, T.: Effect of stride management assist gait training for poststroke hemiplegia: a single center, open-label, randomized controlled trial. J. Stroke Cerebrovasc. Dis. 28(2), 477–486 (2019)

    Article  Google Scholar 

  31. Lim, B., Lee, J., Jang, J., Kim, K., Park, Y.J., Seo, K., Shim, Y.: Delayed output feedback control for gait assistance with a robotic hip exoskeleton. IEEE Trans. Rob. 35(4), 1055–1062 (2019)

    Article  Google Scholar 

  32. Kitatani, R., Ohata, K., Takahashi, H., Shibuta, S., Hashiguchi, Y., Yamakami, N.: Reduction in energy expenditure during walking using an automated stride assistance device in healthy young adults. Arch. Phys. Med. Rehabil. 95(11), 2128–2133 (2014). https://doi.org/10.1016/j.apmr.2014.07.008

    Article  Google Scholar 

  33. Buesing, C., Fisch, G., O’Donnell, M., Shahidi, I., Thomas, L., Mummidisetty, C.K., Williams, K.J., Takahashi, H., Rymer, W.Z., Jayaraman, A.: Effects of a wearable exoskeleton stride management assist system (SMA®) on spatiotemporal gait characteristics in individuals after stroke: a randomized controlled trial. J. Neuroeng. Rehabil. 12(1), 69 (2015). https://doi.org/10.1186/s12984-015-0062-0

    Article  Google Scholar 

  34. Jayaraman, A., O’brien, M.K., Madhavan, S., Mummidisetty, C.K., Roth, H.R., Hohl, K., Tapp, A., Brennan, K., Kocherginsky, M., Williams, K.J.: Stride management assist exoskeleton vs functional gait training in stroke: a randomized trial. Neurology 92(3), e263–e273 (2019)

    Article  Google Scholar 

  35. Zarrugh, M.Y., Radcliffe, C.W.: Predicting metabolic cost of level walking. Eur. J. Appl. Physiol. 38(3), 215–223 (1978). https://doi.org/10.1007/BF00430080

    Article  Google Scholar 

  36. Maalouf, N., Elhajj, I.H., Shammas, E., Asmar, D.: Biomimetic energy-based humanoid gait design. J. Intell. Rob. Syst. 100(1), 203–221 (2020). https://doi.org/10.1007/s10846-020-01179-z

    Article  Google Scholar 

  37. Jamwal, P.K., Hussain, S., Tsoi, Y.H., Xie, S.Q.: Musculoskeletal model for path generation and modification of an ankle rehabilitation robot. IEEE Transactions on Human-Machine Systems (2020).

  38. Ma, Y., Xie, S., Zhang, Y.: A patient-specific EMG-driven neuromuscular model for the potential use of human-inspired gait rehabilitation robots. Comput. Biol. Med. 70, 88–98 (2016)

    Article  Google Scholar 

  39. Uchida, T.K., Seth, A., Pouya, S., Dembia, C.L., Hicks, J.L., Delp, S.L.: Simulating ideal assistive devices to reduce the metabolic cost of running. PLoS ONE 11(9), e0163417 (2016)

    Article  Google Scholar 

  40. Dembia, C.L., Silder, A., Uchida, T.K., Hicks, J.L., Delp, S.L.: Simulating ideal assistive devices to reduce the metabolic cost of walking with heavy loads. PLoS ONE 12(7), e0180320 (2017)

    Article  Google Scholar 

  41. Delp, S.L., Anderson, F.C., Arnold, A.S., Loan, P., Habib, A., John, C.T., Guendelman, E., Thelen, D.G.: OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans. Biomed. Eng. 54(11), 1940–1950 (2007)

    Article  Google Scholar 

  42. Seth, A., Hicks, J.L., Uchida, T.K., Habib, A., Dembia, C.L., Dunne, J.J., Ong, C.F., DeMers, M.S., Rajagopal, A., Millard, M., Hamner, S.R., Arnold, E.M., Yong, J.R., Lakshmikanth, S.K., Sherman, M.A., Ku, J.P., Delp, S.L.: OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement. PLoS Comput Biol 14(7), e1006223 (2018). https://doi.org/10.1371/journal.pcbi.1006223

    Article  Google Scholar 

  43. OpenSim. https://simtk.org/projects/opensim.

  44. Winter, D.A.: Biomechanics and motor control of human movement. John Wiley & Sons, (2009)

  45. Schaal, S., Atkeson, C.G.: Constructive incremental learning from only local information. Neural Comput. 10(8), 2047–2084 (1998)

    Article  Google Scholar 

  46. Bovi, G., Rabuffetti, M., Mazzoleni, P., Ferrarin, M.: A multiple-task gait analysis approach: kinematic, kinetic and EMG reference data for healthy young and adult subjects. Gait Posture 33(1), 6–13 (2011)

    Article  Google Scholar 

  47. Nagarajan, U., Aguirre-Ollinger, G., Goswami, A.: Integral admittance shaping: A unified framework for active exoskeleton control. Robot. Auton. Syst. 75, 310–324 (2016). https://doi.org/10.1016/j.robot.2015.09.015

    Article  Google Scholar 

  48. https://simtk-confluence.stanford.edu:8443/display/OpenSim/How+to+Use+the+CMC+Tool, accessed: 2022–12–02.

  49. https://simtk.org/api_docs/opensim/api_docs/classOpenSim_1_1Umberger2010MuscleMetabolicsProbe.html #details, accessed: 2022–12–02.

  50. Shimada, H., Kimura, Y., Suzuki, T., Hirata, T., Sugiura, M., Endo, Y., Yasuhara, K., Shimada, K., Kikuchi, K., Hashimoto, M., Ishikawa, M., Oda, K., Ishii, K., Ishiwata, K.: The use of positron emission tomography and [18F] Fluorodeoxyglucose for functional imaging of muscular activity during exercise with a stride assistance system. IEEE Trans. Neural Syst. Rehabil. Eng. 15(3), 442–448 (2007). https://doi.org/10.1109/TNSRE.2007.903978

    Article  Google Scholar 

  51. Shimada, H., Suzuki, T., Kimura, Y., Hirata, T., Sugiura, M., Endo, Y., Yasuhara, K., Shimada, K., Kikuchi, K., Oda, K., Ishii, K., Ishiwata, K.: Effects of an automated stride assistance system on walking parameters and muscular glucose metabolism in elderly adults. Br. J. Sports Med. 42(11), 922 (2008). https://doi.org/10.1136/bjsm.2007.039453

    Article  Google Scholar 

  52. Shimada, H., Hirata, T., Kimura, Y., Naka, T., Kikuchi, K., Oda, K., Ishii, K., Ishiwata, K., Suzuki, T.: Effects of a robotic walking exercise on walking performance in community-dwelling elderly adults. Geriatr. Gerontol. Int. 9(4), 372–381 (2009). https://doi.org/10.1111/j.1447-0594.2009.00546.x

    Article  Google Scholar 

  53. Zhang, J., Fiers, P., Witte, K.A., Jackson, R.W., Poggensee, K.L., Atkeson, C.G., Collins, S.H.: Human-in-the-loop optimization of exoskeleton assistance during walking. Science 356(6344), 1280 (2017). https://doi.org/10.1126/science.aal5054

    Article  Google Scholar 

  54. Wei, D., Li, Z., Wei, Q., Su, H., Song, B., He, W., Li, J.: Human-in-the-loop control strategy of unilateral exoskeleton robots for gait rehabilitation. IEEE Transactions on Cognitive and Developmental Systems 13(1), 57–66 (2021). https://doi.org/10.1109/TCDS.2019.2954289

    Article  Google Scholar 

  55. Song, S., Collins, S.H.: Optimizing exoskeleton assistance for faster self-selected walking. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 786–795 (2021). https://doi.org/10.1109/TNSRE.2021.3074154

    Article  Google Scholar 

  56. IEEE Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being. IEEE Std 7010–2020, 1–96 (2020). https://doi.org/10.1109/IEEESTD.2020.9084219

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Acknowledgements

We would like to first thank all members of the Ferdowsi University of Mashhad Robotics Lab for their kind participation and cooperation.

Funding

This research is supported by grant #101120 from the Ferdowsi University of Mashhad-Iran and grant #962297 from the National Institute for Medical Research Development of Iran. This research is also supported by the National Elites Foundation of Iran.

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Authors and Affiliations

  1. Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran

    Naeim Naghavi, Alireza Akbarzadeh, Omid Khaniki & Iman Kardan

  2. Center of Excellence On Soft Computing and Intelligent Information Processing (SCIIP), Ferdowsi University of Mashhad, Mashhad, Iran

    Naeim Naghavi, Alireza Akbarzadeh & Omid Khaniki

  3. Center of Advanced Rehabilitation and Robotics Research (FUM-CARE), Ferdowsi University of Mashhad, Mashhad, Iran

    Naeim Naghavi, Alireza Akbarzadeh, Omid Khaniki, Iman Kardan & Ali Moradi

  4. Orthopedic Research Center, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran

    Ali Moradi

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Naghavi, N., Akbarzadeh, A., Khaniki, O. et al. Assist-As-Needed Control of a Hip Exoskeleton, Using Central Pattern Generators in a Stride Management Strategy. J Intell Robot Syst 107, 53 (2023). https://doi.org/10.1007/s10846-023-01854-x

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