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Compliant Manipulation Method for a Nursing Robot Based on Physical Structure of Human Limb

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Abstract

Nursing robots can be a good substitute to do nursing works for severely disabled patients who have completely lost the ability of movement. In order to improve the safety and comfort of human-robot interaction, we propose a novel compliant manipulation method for a nursing robot based on physical structure of human limb, which is applicable to severely disabled patients and can reduce the high performance requirements of sensors in existing human-robot interaction systems. The method aims to obtain the optimal manipulative force in static and dynamic manipulations. In the aspect of static calculation, static equilibrium conditions are used to obtain optimal static manipulative force. And in the aspect of dynamic calculation, lagrangian method is used to obtain optimal dynamic manipulative force based on predefined movement trajectory. Adams simulations and experiments are executed by manipulating limb model to conduct rehabilitation movements. Experimental results show that the proposed method can provide compliant manipulation method for a nursing robot.

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References

  1. Basteris, A., Nijenhuis, S.M., Stienen, A.H., Buurke, J.H., Prange, G.B., Amirabdollahian, F.: Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review. Journal of NeuroEngineering and Rehabilitation 11(111) (2014)

  2. Chinimilli, P.T., Qiao, Z., Sorkhabadi, S.M.R., Jhawar, V., Fong, I.H., Zhang, W.: Automatic virtual impedance adaptation of a knee exoskeleton for personalized walking assistance. Robotics and Autonomous Systems 114(66-76) (2019)

  3. Grosu, V., Grosu, S., Vanderborght, B., Lefeber, D., Rodriguez-Guerrero, C.: Multi-axis force sensor for human–robot interaction sensing in a rehabilitation robotic device. Sensors 17 (2017)

  4. Guo, J., Li, N., Guo, S., Gao, J.: A labview-based human-computer interaction system for the exoskeleton hand rehabilitation robot. 2017 IEEE International Conference on Mechatronics and Automation (ICMA) (571-576) (2017)

  5. kahn, L.E., Zygman, M.L., Rymer, W.Z., Reinkensmeyer, D.J.: Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: A randomized controlled pilot study. Journal of NeuroEngineering and Rehabilitation 3(12) (2006)

  6. Jones, K.B., Durney, K.M., Hung, C.T., Ateshian, G.A.: The friction coefficient of shoulder joints remains remarkably low over 24 h of loading. Journal of Biomechanics 48(3945-3949) (2015)

  7. Li, N., Yang, T., Yu, P., Chang, J., Zhao, L., Zhao, X., Elhajj, I. H., Xi, N.: Bio-inspired upper limb soft exoskeleton to reduce stroke-induced complications. Bioinspiration and Biomimetics 13 (2018)

  8. Limb, D.: Biomechanics of the elbow. European Surgical Orthopaedics and Traumatology (1305-1316) (2014)

  9. Limb, D.: Biomechanics of the shoulder. European Surgical Orthopaedics and Traumatology (847-864) (2014)

  10. Long, Y., jiang Du, Z., dong Wang, W., He, L., wang Mao, X., Dong, W.: Physical human-robot interaction estimation based control scheme for a hydraulically actuated exoskeleton designed for power amplification. Frontiers of Information Technology and Electronic Engineering 19(1076–1085) (2018)

  11. Losey, D.P., McDonald, C.G., Battaglia, E., O’Malley, M.K.: A review of intent detection, arbitration, and communication aspects of shared control for physical x GerardA.uman-robot interaction. Applied Mechanics Reviews 70 (2018)

  12. Meng, W., Liu, Q., Zhou, Z., Ai, Q., Sheng, B., Xie, S.S.: Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics 31(132-145) (2015)

  13. Modares, H., Ranatunga, I., Lewis, F.L., Popa, D.O.: Optimized assistive human–robot interaction using reinforcement learning. IEEE Transactions on Cybernetics 46(655-667) (2016)

  14. Mörtl, A., Lawitzky, M., Kucukyilmaz, A., Sezgin, M., Basdogan, C., Hirche, S.: The role of roles: Physical cooperation between humans and robots. International Journal of Robotics Research 31(1656-1674) (2012)

  15. Peña, G.G., Consoni, L.J., dos Santos, W.M., Siqueira, A.A.: Feasibility of an optimal emg-driven adaptive impedance control applied to an active knee orthosis. Robotics and Autonomous Systems 112(98-108) (2019)

  16. Reinkensmeyer, D.J., kahn, L.E., Averbuch, M., Mckenna-Cole, A., Schmit, B.D., Rymer, W.Z.: Understanding and treating arm movement impairment after chronic brain injury: progress with the arm guide. Journal of Rehabilitation Research and Development 37(653-662) (2000)

  17. Roemhildt, M., Gardner-Morse, M., Morgan, C., Beynnon, B., Badger, G.: Calcium phosphate particulates increase friction in the rat knee joint. Osteoarthritis and Cartilage 22(706-709) (2014)

  18. Sandoval-Gonzalez, O., Jacinto-Villegas, J., Herrera-Aguilar, I., Portillo-Rodiguez, O., Tripicchio, P., Hernandez-Ramos, M., Flores-Cuautle, A., Avizzano, C.: Design and development of a hand exoskeleton robot for active and passive rehabilitation. International Journal of Advanced Robotic Systems 13 (2016)

  19. Schultz, A.E., Kuiken, T.A.: Neural interfaces for control of upper limb prostheses: The state of the art and future possibilities. PM and R 3(55-67) (2011)

  20. Shao, Q.: Danieln.bassett, KurtManal, ThomasS.Buchanan: An emg-driven model to estimate muscle forces and joint moments in stroke patients. Computers in Biology and Medicine 39(1083-1088) (2009)

  21. Sun, H., Zhang, L., Li, C.: Dynamic analysis of horizontal lower limbs rehabilitative robot. 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (656-660) (2009)

  22. Valevicius, A.M., Jun, P.Y., Hebert, J.S., Vette, A.H.: Use of optical motion capture for the analysis of normative upper body kinematics during functional upper limb tasks: A systematic review. Journal of Electromyography and Kinesiology 40(1-15) (2018)

  23. Wang, D., Meng, Q., Meng, Q., Li, X., Yu, H.: Design and development of a portable exoskeleton for hand rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 26(2376-2386) (2018)

  24. Wang, W., Hou, Z.G., Tong, L., Chen, Y., Peng, L.: Tan, M.: Dynamics modeling and identification of the human-robot interface based on a lower limb rehabilitation robot. 2014 IEEE International Conference on Robotics and Automation (ICRA) (6012-6017) (2014)

  25. Yu, H., Huang, S., Chen, G., Pan, Y., Guo, Z.: Human–robot interaction control of rehabilitation robots with series elastic actuators. IEEE Transactions on Robotics 31(1089-1100) (2015)

  26. Zeiaee, A., Soltani-Zarrin, R., Langari, R., Tafreshi, R.: Design and kinematic analysis of a novel upper limb exoskeleton for rehabilitation of stroke patients. 2017 IEEE International Conference on Rehabilitation Robotics (759-764) (2017)

  27. Zhang, L., Li, J., Su, P., Song, Y., Dong, M., Cao, Q.: Improvement of human–machine compatibility of upper-limb rehabilitation exoskeleton using passive joints. Robotics and Autonomous Systems 112(22-31) (2019)

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Acknowledgements

This work is supported by Key R & D project of Shandong Province (Grant No.2019GGX104038) and Fundamental Research Funds of Shandong University (Grant No.2016JC001).

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Authors and Affiliations

  1. Key Laboratory of High Efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan, China

    Zhongqiu Zhao, Xueyong Li, Changhou Lu & Ming Zhang

  2. Department of Physical Medicine and Rehabilitation, Qilu Hospital, Shandong University, Jinan, 250012, China

    Yonghui Wang

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  1. Zhongqiu Zhao
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  2. Xueyong Li
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  3. Changhou Lu
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  4. Ming Zhang
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  5. Yonghui Wang
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Correspondence to Xueyong Li.

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Zhao, Z., Li, X., Lu, C. et al. Compliant Manipulation Method for a Nursing Robot Based on Physical Structure of Human Limb. J Intell Robot Syst 100, 973–986 (2020). https://doi.org/10.1007/s10846-020-01221-0

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  • DOI: https://doi.org/10.1007/s10846-020-01221-0

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