Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
129 tokens/sec
GPT-4o
28 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Adaptive Control for Triadic Human-Robot-FES Collaboration in Gait Rehabilitation: A Pilot Study (2402.00775v2)

Published 1 Feb 2024 in cs.RO

Abstract: The hybridisation of robot-assisted gait training and functional electrical stimulation (FES) can provide numerous physiological benefits to neurological patients. However, the design of an effective hybrid controller poses significant challenges. In this over-actuated system, it is extremely difficult to find the right balance between robotic assistance and FES that will provide personalised assistance, prevent muscle fatigue and encourage the patient's active participation in order to accelerate recovery. In this paper, we present an adaptive hybrid robot-FES controller to do this and enable the triadic collaboration between the patient, the robot and FES. A patient-driven controller is designed where the voluntary movement of the patient is prioritised and assistance is provided using FES and the robot in a hierarchical order depending on the patient's performance and their muscles' fitness. The performance of this hybrid adaptive controller is tested in simulation and on one healthy subject. Our results indicate an increase in tracking performance with lower overall assistance, and less muscle fatigue when the hybrid adaptive controller is used, compared to its non adaptive equivalent. This suggests that our hybrid adaptive controller may be able to adapt to the behaviour of the user to provide assistance as needed and prevent the early termination of physical therapy due to muscle fatigue.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (39)
  1. S. Campagnini, P. Liuzzi, A. Mannini, R. Riener, and M. C. Carrozza, “Effects of control strategies on gait in robot-assisted post-stroke lower limb rehabilitation: a systematic review,” Journal of NeuroEngineering and Rehabilitation, vol. 19, no. 1, pp. 1–16, 2022.
  2. B. Shi, X. Chen, Z. Yue, S. Yin, Q. Weng, X. Zhang, J. Wang, and W. Wen, “Wearable Ankle Robots in Post-stroke Rehabilitation of Gait: A Systematic Review,” Frontiers in Neurorobotics, vol. 13, no. August, pp. 1–16, 2019.
  3. C.-Y. Fang, J.-L. Tsai, G.-S. Li, A. S.-Y. Lien, and Y.-J. Chang, “Effects of Robot-Assisted Gait Training in Individuals with Spinal Cord Injury: A Meta-analysis,” BioMed Research International, vol. 2020, pp. 1–13, mar 2020.
  4. S. C. Hayes, C. R. James Wilcox, H. S. Forbes White, and N. Vanicek, “The effects of robot assisted gait training on temporal-spatial characteristics of people with spinal cord injuries: A systematic review,” Journal of Spinal Cord Medicine, vol. 41, no. 5, pp. 529–543, 2018.
  5. J. S. Tedla, S. Dixit, K. Gular, and M. Abohashrh, “Robotic-Assisted Gait Training Effect on Function and Gait Speed in Subacute and Chronic Stroke Population: A Systematic Review and Meta-Analysis of Randomized Controlled Trials,” European Neurology, vol. 81, no. 3-4, pp. 103–111, 2019.
  6. E. Swinnen, D. Beckwée, R. Meeusen, J. P. Baeyens, and E. Kerckhofs, “Does robot-assisted gait rehabilitation improve balance in stroke patients? a systematic review,” Topics in Stroke Rehabilitation, vol. 21, no. 2, pp. 87–100, 2014.
  7. F. Anaya, P. Thangavel, and H. Yu, “Hybrid FES–robotic gait rehabilitation technologies: a review on mechanical design, actuation, and control strategies,” International Journal of Intelligent Robotics and Applications, vol. 2, no. 1, pp. 1–28, 2018.
  8. A. J. Del-Ama, A. D. Koutsou, J. C. Moreno, A. De-los Reyes, N. Gil-Agudo, and J. L. Pons, “Review of hybrid exoskeletons to restore gait following spinal cord injury,” The Journal of Rehabilitation Research and Development, vol. 49, no. 4, p. 497, 2012.
  9. C. Marquez-Chin and M. R. Popovic, “Functional electrical stimulation therapy for restoration of motor function after spinal cord injury and stroke: A review,” BioMedical Engineering Online, vol. 19, no. 1, pp. 1–25, 2020.
  10. M. R. Popovic, K. Masani, and S. Micera, “Functional Electrical Stimulation Therapy: Recovery of Function Following Spinal Cord Injury and Stroke,” in Neurorehabilitation Technology.   Cham: Springer International Publishing, 2016, vol. 16, pp. 513–532.
  11. P. H. Peckham and J. S. Knutson, “Functional Electrical Stimulation for Neuromuscular Applications,” Annual Review of Biomedical Engineering, vol. 7, no. 1, pp. 327–360, aug 2005.
  12. M. O. Ibitoye, N. A. Hamzaid, N. Hasnan, A. K. A. Wahab, and G. M. Davis, “Strategies for rapid muscle fatigue reduction during FES exercise in individuals with spinal cord injury: A systematic review,” PLoS ONE, vol. 11, no. 2, pp. 1–28, 2016.
  13. C. S. Bickel, C. M. Gregory, and J. C. Dean, “Motor unit recruitment during neuromuscular electrical stimulation: a critical appraisal,” European Journal of Applied Physiology, vol. 111, no. 10, pp. 2399–2407, oct 2011.
  14. C. L. Lynch and M. R. Popovic, “Functional Electrical Stimulation,” IEEE Control Systems, vol. 28, no. 2, pp. 40–50, 2008.
  15. T. Schauer, “Sensing motion and muscle activity for feedback control of functional electrical stimulation: Ten years of experience in Berlin,” Annual Reviews in Control, vol. 44, pp. 355–374, 2017.
  16. M. Goldfarb and W. K. Durfee, “Design of a controlled-brake orthosis for FES-aided gait,” IEEE Transactionl on Rehabilitation Engineering, vol. 4, no. 1, pp. 13–24, 1996.
  17. R. Kobetic, C. S. To, J. R. Schnellenberger, M. L. Audu, T. C. Bulea, R. Gaudio, G. Pinault, S. Tashman, and R. J. Triolo, “Development of hybrid orthosis for standing, walking, and stair climbing after spinal cord injury,” The Journal of Rehabilitation Research and Development, vol. 46, no. 3, p. 447, 2009.
  18. N. Sharma, V. Mushahwar, and R. Stein, “Dynamic optimization of FES and orthosis-based walking using simple models,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 22, no. 1, pp. 114–126, 2014.
  19. Y. Stauffer, Y. Allemand, M. Bouri, J. Fournier, R. Clavel, P. Metrailler, R. Brodard, and F. Reynard, “The WalkTrainer - A new generation of walking reeducation device combining orthoses and muscle stimulation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 17, no. 1, pp. 38–45, 2009.
  20. K. H. Ha, S. A. Murray, and M. Goldfarb, “An Approach for the Cooperative Control of FES With a Powered Exoskeleton During Level Walking for Persons With Paraplegia,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 4, pp. 455–466, 2016.
  21. A. J. Del-Ama, Á. Gil-Agudo, J. L. Pons, and J. C. Moreno, “Hybrid FES-robot cooperative control of ambulatory gait rehabilitation exoskeleton,” Journal of NeuroEngineering and Rehabilitation, vol. 11, no. 1, pp. 1–15, 2014.
  22. N. Kirsch, N. Alibeji, and N. Sharma, “Nonlinear model predictive control of functional electrical stimulation,” Control Engineering Practice, vol. 58, pp. 319–331, 2017.
  23. V. Molazadeh, Q. Zhang, X. Bao, B. E. Dicianno, and N. Sharma, “Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning,” Frontiers in Robotics and AI, vol. 8, no. November, pp. 1–13, 2021.
  24. N. A. Alibeji, V. Molazadeh, B. E. Dicianno, and N. Sharma, “A control scheme that uses dynamic postural synergies to coordinate a hybrid walking neuroprosthesis: Theory and experiments,” Frontiers in Neuroscience, vol. 12, no. APR, pp. 1–15, 2018.
  25. H. Vallery, T. Stützle, M. Buss, and D. Abel, “Control of a hybrid motor prosthesis for the knee joint,” IFAC Proceedings Volumes (IFAC-PapersOnline), vol. 38, no. 1, pp. 76–81, 2005.
  26. F. Romero-Sánchez, J. Bermejo-García, J. Barrios-Muriel, and F. J. Alonso, “Design of the cooperative actuation in hybrid orthoses: A theoretical approach based on muscle models,” Frontiers in Neurorobotics, vol. 13, no. July, pp. 1–15, 2019.
  27. A. A. Blank, J. A. French, A. U. Pehlivan, and M. K. O’Malley, “Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy,” Current Physical Medicine and Rehabilitation Reports, vol. 2, no. 3, pp. 184–195, 2014.
  28. S. Paolucci, A. Di Vita, R. Massicci, M. Traballesi, I. Bureca, A. Matano, M. Iosa, and C. Guariglia, “Impact of participation on rehabilitation results: a multivariate study.” European journal of physical and rehabilitation medicine, vol. 48, no. 3, pp. 455–66, sep 2012.
  29. L. E. Kahn, P. S. Lum, W. Z. Rymer, and D. J. Reinkensmeyer, “Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does?” Journal of Rehabilitation Research and Development, vol. 43, no. 5, pp. 619–629, 2006.
  30. A. Kaelin-Lane, L. Sawaki, and L. G. Cohen, “Role of voluntary drive in encoding an elementary motor memory,” Journal of Neurophysiology, vol. 93, no. 2, pp. 1099–1103, 2005.
  31. D. F. N. Gordon, A. Christou, T. Stouraitis, M. Gienger, and S. Vijayakumar, “Adaptive assistive robotics: a framework for triadic collaboration between humans and robots,” Royal Society Open Science, vol. 10, no. 6, jun 2023.
  32. A. Christou, D. Gordon, T. Stouraitis, and S. Vijayakumar, “Designing Personalised Rehabilitation Controllers using Offline Model-Based Optimisation,” in 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO).   IEEE, dec 2022, pp. 148–155.
  33. A. Duschau-Wicke, J. Von Zitzewitz, A. Caprez, L. Lünenburger, and R. Riener, “Path control: A method for patient-cooperative robot-aided gait rehabilitation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 18, no. 1, pp. 38–48, 2010.
  34. R. Riener and T. Fuhr, “Patient-driven control of FES-supported standing up: A simulation study,” IEEE Transactions on Rehabilitation Engineering, vol. 6, no. 2, pp. 113–124, 1998.
  35. M. Gfohler, T. Angeli, and P. Lugner, “Modeling of artificially activated muscle and application to FES cycling,” Journal of Mechanics in Medicine and Biology, vol. 04, no. 01, pp. 77–92, 2004.
  36. R. Riener, J. Quintern, and G. Schmidt, “Biomechanical model of the human knee evaluated by neuromuscular stimulation,” Journal of Biomechanics, vol. 29, no. 9, pp. 1157–1167, 1996.
  37. N. Sharma, N. A. Kirsch, N. A. Alibeji, and W. E. Dixon, “A non-linear control method to compensate for muscle fatigue during neuromuscular electrical stimulation,” Frontiers Robotics AI, vol. 4, no. DEC, 2017.
  38. A. Seth, J. L. Hicks, T. K. Uchida, A. Habib, C. L. Dembia, J. J. Dunne, C. F. Ong, M. S. DeMers, A. Rajagopal, M. Millard, S. R. Hamner, E. M. Arnold, J. R. Yong, S. K. Lakshmikanth, M. A. Sherman, J. P. Ku, and S. L. Delp, “OpenSim: Simulating musculoskeletal dynamics and neuromuscular control to study human and animal movement,” PLOS Computational Biology, vol. 14, no. 7, p. e1006223, 2018.
  39. M. Millard, T. Uchida, A. Seth, and S. L. Delp, “Flexing computational muscle: Modeling and simulation of musculotendon dynamics,” Journal of Biomechanical Engineering, vol. 135, no. 2, pp. 1–11, 2013.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now