Design and analysis of a multi-DOF compliant gait rehabilitation robot

References

• Agrawal, S. K., and A. Fattah. 2004. Theory and design of an orthotic device for full or partial gravity-balancing of a human leg during motion. IEEE Transactions on Neural Systems and Rehabilitation Engineering 12 (2):157–65. doi:10.1109/TNSRE.2004.827221.
   PubMed Web of Science ®Google Scholar
• Banala, S. K., S. K. Agrawal, A. Fattah, V. Krishnamoorthy, W. Hsu, J. Scholz, and K. Rudolph. 2006. Gravity-balancing leg orthosis and its performance evaluation. IEEE Transactions on Robotics 22 (6):1228–39. doi:10.1109/TRO.2006.882928.
   Web of Science ®Google Scholar
• Beyl, P., M. van Damme, R. van Ham, B. Vanderborght, and D. Lefeber. 2009. Design and control of a lower limb exoskeleton for robot-assisted gait training. Applied Bionics and Biomechanics 6 (2):229–43. doi:10.1080/11762320902784393.
   Google Scholar
• Brockett, C. L., and G. J. Chapman. 2016. Biomechanics of the ankle. Orthopaedics and Trauma 30 (3):232–8. doi:10.1016/j.mporth.2016.04.015.
   PubMedGoogle Scholar
• Cao, J., S. Q. Xie, and R. Das. 2018. MIMO sliding mode controller for gait exoskeleton driven by pneumatic muscles. IEEE Transactions on Control Systems Technology 26 (1):274–81. doi:10.1109/TCST.2017.2654424.
   Web of Science ®Google Scholar
• Carlo, A. 2009. Human and economic burden of stroke. Age and Aging 38:4–5.
   PubMed Web of Science ®Google Scholar
• Cempini, M., S. M. M. De Rossi, T. Lenzi, N. Vitiello, and M. C. Carrozza. 2013. Self-alignment mechanisms for assistive wearable robots: A kinetostatic compatibility method. IEEE Transactions on Robotics 29 (1):236–50. doi:10.1109/TRO.2012.2226381.
   Web of Science ®Google Scholar
• Chang, M. K. 2010. An adaptive self-organizing fuzzy sliding mode controller for a 2-DOF rehabilitation robot actuated by pneumatic muscle actuators. Control Engineering Practice 18 (1):13–22. doi:10.1016/j.conengprac.2009.08.005.
   Web of Science ®Google Scholar
• Chaparro-Cárdenas, S. L., A. A. Lozano-Guzmán, J. A. Ramirez-Bautista, and A. Hernández-Zavala. 2018. A review in gait rehabilitation devices and applied control techniques. Disability and Rehabilitation. Assistive Technology 13 (8):819–34. doi:10.1080/17483107.2018.1447611.
   PubMed Web of Science ®Google Scholar
• Chou, C.-P, and B. Hannaford. 1994. Static and dynamic characteristics of McKibben pneumatic artificial muscles. Proceedings - IEEE International Conference on Robotics and Automation.
   Google Scholar
• Cieza, A., K. Causey, K. Kamenov, S. Hanson, S. Chatterji, and T. Vos. 2020. Global estimates of the need for rehabilitation based on the Global Burden of Disease study 2019: A systematic analysis for the Global Burden of Disease Study 2019. The Lancet 396 (10267):2006–17. doi:10.1016/S0140-6736(20)32340-0.
   PubMed Web of Science ®Google Scholar
• Colbrunn, R. W., G. M. Nelson, and R. D. Quinn. 2001. Modeling of braided pneumatic actuators for robotic control. Proceedings of the International Conference on Intelligent Robots and Systems, Oct 29–Nov 03 2001.
   Google Scholar
• Festo_Corporation. 2022. Fluidic Muscle DMSP. https://www.festo.com/us/en/p/fluidic-muscle-id_DMSP/.
   Google Scholar
• Giovacchini, F., F. Vannetti, M. Fantozzi, M. Cempini, M. Cortese, A. Parri, T. Yan, D. Lefeber, and N. Vitiello. 2015. A light-weight active orthosis for hip movement assistance. Robotics and Autonomous Systems 73:123–34. doi:10.1016/j.robot.2014.08.015.
   Web of Science ®Google Scholar
• Gray, H. A., S. Guan, L. T. Thomeer, A. G. Schache, R. de Steiger, and M. G. Pandy. 2019. Three-dimensional motion of the knee-joint complex during normal walking revealed by mobile biplane x-ray imaging. Journal of Orthopaedic Research 37 (3):615–30. doi:10.1002/jor.24226.
   PubMed Web of Science ®Google Scholar
• Hogan, N., H. Krebs, B. Rohrer, J. Palazzolo, L. Dipietro, S. Fasoli, J. Stein, R. Hughes, W. Frontera, D. Lynch, et al. 2006. Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery. Journal of Rehabilitation Research and Development 43 (5):605–18. doi:10.1682/jrrd.2005.06.0103.
   PubMedGoogle Scholar
• Huang, J., X. Tu, and J. He. 2016. Design and evaluation of the RUPERT wearable upper extremity exoskeleton robot for clinical and in-home therapies. IEEE Transactions on Systems, Man, and Cybernetics: Systems 46 (7):926–35. doi:10.1109/TSMC.2015.2497205.
   Web of Science ®Google Scholar
• Huang, M., X. Huang, X. Tu, Z. Li, and Y. Wen. 2016. An online gain tuning proxy-based sliding mode control using neural network for a gait training robotic orthosis. Cluster Computing 19 (4):1987–2000. doi:10.1007/s10586-016-0629-y.
   Web of Science ®Google Scholar
• Hussain, S. 2014. State-of-the-art robotic gait rehabilitation orthoses: Design and control aspects. NeuroRehabilitation 35 (4):701–9. doi:10.3233/NRE-141174.
   PubMed Web of Science ®Google Scholar
• Hussain, S., P. K. Jamwal, P. Van Vliet, and M. H. Ghayesh. 2020. State-of-the-art robotic devices for wrist rehabilitation: Design and control aspects. IEEE Transactions on Human-Machine Systems 50 (5):361–72. doi:10.1109/THMS.2020.2976905.
   Web of Science ®Google Scholar
• Hussain, S., P. K. Jamwal, M. H. Ghayesh, and S. Q. Xie. 2017. Assist-as-needed control of an intrinsically compliant robotic gait training orthosis. IEEE Transactions on Industrial Electronics 64 (2):1675–85. doi:10.1109/TIE.2016.2580123.
   Web of Science ®Google Scholar
• Hyun, D. J., H. Park, T. Ha, S. Park, and K. Jung. 2017. Biomechanical design of an agile, electricity-powered lower-limb exoskeleton for weight-bearing assistance. Robotics and Autonomous Systems 95:181–95. doi:10.1016/j.robot.2017.06.010.
   Web of Science ®Google Scholar
• Jamwal, P. K., and S. Hussain. 2016. Multicriteria design optimization of a parallel ankle rehabilitation robot: Fuzzy dominated sorting evolutionary algorithm approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems 46 (5):589–97. doi:10.1109/TSMC.2015.2478389.
   Web of Science ®Google Scholar
• Jamwal, P. K., S. Hussain, M. H. Ghayesh, and S. V. Rogozina. 2016. Impedance control of an intrinsically compliant parallel ankle rehabilitation robot. IEEE Transactions on Industrial Electronics 63 (6):3638–47. doi:10.1109/TIE.2016.2521600.
   Web of Science ®Google Scholar
• Jamwal, P. K., S. Hussain, N. Mir-Nasiri, M. H. Ghayesh, and S. Q. Xie. 2018. Tele-rehabilitation using in-house wearable ankle rehabilitation robot. Assistive Technology 30 (1):24–33. doi:10.1080/10400435.2016.1230153.
   PubMed Web of Science ®Google Scholar
• Jamwal, P. K., S. Q. Xie, Y. H. Tsoi, and K. C. Aw. 2010. Forward kinematics modelling of a parallel ankle rehabilitation robot using modified fuzzy inference. Mechanism and Machine Theory 45 (11):1537–54. doi:10.1016/j.mechmachtheory.2010.06.017.
   Web of Science ®Google Scholar
• Junius, K., M. Degelaen, N. Lefeber, E. Swinnen, B. Vanderborght, and D. Lefeber. 2017. Bilateral, misalignment-compensating, full-DOF hip exoskeleton: Design and kinematic validation. Applied Bionics and Biomechanics 2017:1–14. doi:10.1155/2017/5813154.
   Web of Science ®Google Scholar
• Kapsalyamov, A., S. Hussain, and P. K. Jamwal. 2020. State-of-the-art assistive powered upper limb exoskeletons for elderly. IEEE Access 8:178991–9001. doi:10.1109/ACCESS.2020.3026641.
   Web of Science ®Google Scholar
• Kapsalyamov, A., P. K. Jamwal, S. Hussain, and M. H. Ghayesh. 2019. State of the art lower limb robotic exoskeletons for elderly assistance. IEEE Access 7:95075–86. doi:10.1109/ACCESS.2019.2928010.
   Web of Science ®Google Scholar
• Kashyap, A. K., D. R. Parhi, and A. Pandey. 2022. Multi-objective optimization technique for trajectory planning of multi-humanoid robots in cluttered terrain. ISA Transactions 125:591–613. doi:10.1016/j.isatra.2021.06.017.
   PubMed Web of Science ®Google Scholar
• Kim, I., T. Lee, and S. H. Lee. 2021. Ankle intention detection algorithm using electromyography signal. Journal of Computational Design and Engineering 8 (5):1234–42. doi:10.1093/jcde/qwab042.
   Web of Science ®Google Scholar
• Klein, J., S. J. Spencer, J. Allington, K. Minakata, E. T. Wolbrecht, R. Smith, J. E. Bobrow, and D. J. Reinkensmeyer. 2008. Biomimetic orthosis for the neurorehabilitation of the elbow and shoulder (BONES). 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.
   Google Scholar
• Lloyd, S., R. A. Irani, and M. Ahmadi. 2022. Fast and robust inverse kinematics of serial robots using Halley’s method. IEEE Transactions on Robotics 38 (5):2768–80. doi:10.1109/TRO.2022.3162954.
   Web of Science ®Google Scholar
• Marchal-Crespo, L. 2007. Review of control strategies for robotic movement training after neurologic injury. Journal of NeuroEngineering and Rehabilitation. 14:22–44.
   Google Scholar
• Mat Dzahir, M. A., and S-i Yamamoto. 2014. Recent trends in lower-limb robotic rehabilitation orthosis: Control scheme and strategy for pneumatic muscle actuated gait trainers. Robotics 3 (2):120–48. doi:10.3390/robotics3020120.
   Google Scholar
• Mena, D., J. M. Mansour, and S. R. Simon. 1981. Analysis and synthesis of human swing leg motion during gait and its clinical applications. Journal of Biomechanics 14 (12):823–32. doi:10.1016/0021-9290(81)90010-5.
   PubMed Web of Science ®Google Scholar
• Otten, A., C. Voort, A. Stienen, R. Aarts, E. v. Asseldonk, and H. v. d. Kooij. 2015. LIMPACT:A hydraulically powered self-aligning upper limb exoskeleton. IEEE/ASME Transactions on Mechatronics 20 (5):2285–98. doi:10.1109/TMECH.2014.2375272.
   Web of Science ®Google Scholar
• Qian, K., Z. Li, S. Chakrabarty, Z. Zhang, and S. Q. Xie. 2023. Robust iterative learning control for pneumatic muscle with uncertainties and state constraints. IEEE Transactions on Industrial Electronics 70 (2):1802–10. doi:10.1109/TIE.2022.3159970.
   Web of Science ®Google Scholar
• Ramirez-Bautista, J. A., A. Hernández-Zavala, S. L. Chaparro-Cárdenas, and J. A. Huerta-Ruelas. 2018. Review on plantar data analysis for disease diagnosis. Biocybernetics and Biomedical Engineering 38 (2):342–61. doi:10.1016/j.bbe.2018.02.004.
   Web of Science ®Google Scholar
• Ramirez-Bautista, J. A., J. A. Huerta-Ruelas, S. L. Chaparro-Cárdenas, and A. Hernández-Zavala. 2017. A review in detection and monitoring gait disorders using in-shoe plantar measurement systems. IEEE Reviews in Biomedical Engineering 10:299–309. doi:10.1109/RBME.2017.2747402.
   PubMedGoogle Scholar
• Refaai, M. R. A. 2022. An improved inverse kinematics solution for a robot arm trajectory using multiple adaptive neuro-fuzzy inference systems. Advances in Materials Science and Engineering 2022:1–12. doi:10.1155/2022/1413952.
   Web of Science ®Google Scholar
• Reinkensmeyer, D. J., J. L. Emken, and S. C. Cramer. 2004. Robotics, motor learning, and neurologic recovery. Annual Review of Biomedical Engineering 6:497–525. doi:10.1146/annurev.bioeng.6.040803.140223.
   PubMed Web of Science ®Google Scholar
• Reynolds, D., D. Repperger, C. Phillips, and G. Bandry. 2003. Modeling the dynamic characteristics of pneumatic muscle. Annals of Biomedical Engineering 31 (3):310–7. doi:10.1114/1.1554921.
   PubMed Web of Science ®Google Scholar
• Saccares, L., I. Sarakoglou, and N. G. Tsagarakis. 2016. It-knee: An exoskeleton with ideal torque transmission interface for ergonomic power augmentation. IEEE International Conference on Intelligent Robots and Systems.
   Google Scholar
• Sarkisian, S. V., M. K. Ishmael, G. R. Hunt, and T. Lenzi. 2020. Design, development, and validation of a self-aligning mechanism for high-torque powered knee exoskeletons. IEEE Transactions on Medical Robotics and Bionics 2 (2):248–59. doi:10.1109/TMRB.2020.2981951.
   Google Scholar
• Schiele, A., and F. C. T. Van Der Helm. 2006. Kinematic design to improve ergonomics in human machine interaction. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14 (4):456–69. doi:10.1109/TNSRE.2006.881565.
   PubMed Web of Science ®Google Scholar
• Stienen, A. H. A., E. E. G. Hekman, F. C. T. v. d. Helm, and H. v. d. Kooij. 2009. Self-aligning exoskeleton axes through decoupling of joint rotations and translations. IEEE Transactions on Robotics 25 (3):628–33. doi:10.1109/TRO.2009.2019147.
   Web of Science ®Google Scholar
• Tondu, B., and P. Lopez. 1995. Theorie d‘un muscle artificiel pneumatique et application a la modelisation du muscle artificiel de McKibben. Theory of an Artificial Pneumatic Muscle and Application to the Modelling of McKibben Artificial Muscle." 320 (3):105–14.
   Google Scholar
• Tsagarakis, N., and D. G. Caldwell. 2000. Improved modelling and assessment of pneumatic muscle actuators. Proceedings - IEEE International Conference on Robotics and Automation.
   Google Scholar
• Tschiersky, M., E. E. G. Hekman, D. M. Brouwer, and J. L. Herder. 2019. Gravity balancing flexure springs for an assistive elbow orthosis. IEEE Transactions on Medical Robotics and Bionics 1 (3):177–88. doi:10.1109/TMRB.2019.2930341.
   Google Scholar
• Wang, D., K. M. Lee, J. Guo, and C. J. Yang. 2014. Adaptive knee joint exoskeleton based on biological geometries. IEEE/ASME Transactions on Mechatronics 19 (4):1268–78. doi:10.1109/TMECH.2013.2278207.
   Web of Science ®Google Scholar
• Weber, L. M., and J. Stein. 2018. The use of robots in stroke rehabilitation: A narrative review. NeuroRehabilitation 43 (1):99–110. doi:10.3233/NRE-172408.
   PubMed Web of Science ®Google Scholar
• Winter, D. A. 2009. Biomechanics and motor control of human movement. USA: Wiley Online Library.
   Google Scholar
• Xie, S. Q., and P. K. Jamwal. 2011. An iterative fuzzy controller for pneumatic muscle driven rehabilitation robot. Expert Systems with Applications 38 (7):8128–37. doi:10.1016/j.eswa.2010.12.154.
   Web of Science ®Google Scholar
• Xu, J., W. Wang, and Y. Sun. 2010. Two optimization algorithms for solving robotics inverse kinematics with redundancy. Journal of Control Theory and Applications 8 (2):166–75. doi:10.1007/s11768-010-0011-1.
   Google Scholar
• Yao, J., N. Guo, Y. Xiao, Z. Li, Y. Li, F. Pu, and Y. Fan. 2019. Lower limb joint motion and muscle force in treadmill and over-ground exercise. Biomedical Engineering Online 18 (1):89. doi:10.1186/s12938-019-0708-4.
   PubMed Web of Science ®Google Scholar
• Young, A. J., and D. P. Ferris. 2017. State of the art and future directions for lower limb robotic exoskeletons. IEEE Transactions on Neural Systems and Rehabilitation Engineering 25 (2):171–82. doi:10.1109/TNSRE.2016.2521160.
   PubMed Web of Science ®Google Scholar
• Zanotto, D., Y. Akiyama, P. Stegall, and S. K. Agrawal. 2015. Knee joint misalignment in exoskeletons for the lower extremities: Effects on user’s gait. IEEE Transactions on Robotics 31 (4):978–87. doi:10.1109/TRO.2015.2450414.
   Web of Science ®Google Scholar
• Zhang, J., J. Sheng, C. T. O'Neill, C. J. Walsh, R. J. Wood, J. H. Ryu, J. P. Desai, and M. C. Yip. 2019. Robotic artificial muscles: Current progress and future perspectives. IEEE Transactions on Robotics 35 (3):761–81. doi:10.1109/TRO.2019.2894371.
   Web of Science ®Google Scholar
• Zhong, G., B. Peng, and W. Dou. 2022. Kinematics analysis and trajectory planning of a continuum manipulator. International Journal of Mechanical Sciences 222:107206. doi:10.1016/j.ijmecsci.2022.107206.
   Web of Science ®Google Scholar
• Zhou, L., W. Chen, W. Chen, S. Bai, J. Zhang, and J. Wang. 2020. Design of a passive lower limb exoskeleton for walking assistance with gravity compensation. Mechanism and Machine Theory 150:103840. doi:10.1016/j.mechmachtheory.2020.103840.
   Web of Science ®Google Scholar