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A Novel Human-Robot Cooperative Method for Upper Extremity Rehabilitation

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Abstract

There are a certain number of arm dysfunction patients whose legs could move. Considering the neuronal coupling between arms and legs during locomotion, this paper proposes a novel human-robot cooperative method for upper extremity rehabilitation. Legs motion is considered at the passive rehabilitation training of disabled arm, and its traversed trajectory is represented by the patient trunk motion. A Kinect based vision module, two computers and a WAM robot construct the human-robot cooperative upper extremity rehabilitation system. The vision module is employed to track the position of the subject trunk in horizontal; the WAM robot is used to guide the arm of post-stroke patient to do passive training with the predefined trajectory, and meanwhile the robot follows the patient trunk movement which is tracked by Kinect in real-time. A hierarchical fuzzy control strategy is proposed to improve the position tracking performance and stability of the system, which consists of an external fuzzy dynamic interpolation strategy and an internal fuzzy PD position controller. Four experiments were conducted to test the proposed method and strategy. The experimental results show that the patient felt more natural and comfortable when the human-robot cooperative method was applied; the subject could walk as he/she wished in the visual range of Kinect. The hierarchical fuzzy control strategy performed well in the experiments. This indicates the high potential of the proposed human-robot cooperative method for upper extremity rehabilitation.

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References

  1. Song A, Pan L, Xu G, Li H (2015) Adaptive motion control of arm rehabilitation robot based on impedance identification. Robotica 33(9):1795–1812. doi:10.1017/S026357471400099X

    Article  Google Scholar 

  2. Go AS, Mozaffarian D, Roger VL et al (2014) Heart disease and stroke statistics-2014 update: a report from the American Heart Association. Circulation 129:28–292. doi:10.1161/01.cir.0000441139.02102.80

    Article  Google Scholar 

  3. Maciejasz P, Eschweiler J, Gerlach-Hahn K, Jansen-Troy A, Leonhardt S (2014) A survey on robotic devices for upper limb rehabilitation. J Neuroeng Rehabil 11:3. doi:10.1186/1743-0003-11-3

    Article  Google Scholar 

  4. Krebs HI, Palazzolo JJ, Dipietro L et al (2003) Rehabilitation robotics: performance based progressive robot-assisted therapy. Auton Robots 15:7–20. doi:10.1023/A:1024494031121

    Article  Google Scholar 

  5. Rosati G, Gallina P, Masiero S (2007) Design, implementation and clinical tests of a wire-based robot for neurorehabilitation. IEEE Trans Neural Syst Rehabil Eng 15(4):560–569. doi:10.1109/TNSRE.2007.908560

    Article  Google Scholar 

  6. Spencer SJ, Klein J, Minakata K, Le V, Bobrow J E, Reinkensmeyer D J (2008) A low cost parallel robot and trajectory optimization method for wrist and forearm rehabilitation using the Wii. In: Proceedings \(2^{\rm nd}\) IEEE RAS&EMBS international conference on biomedical robotics and biomechatronics, Scottsdale, pp 869–874. doi:10.1109/BIOROB.2008.4762902

  7. Nef T, Mihelj M, Riener R (2007) ARMin: a robot for patient-cooperative arm therapy. Med Biol Eng Comput 45(9):887–900. doi:10.1007/s11517-007-0226-6

    Article  Google Scholar 

  8. Perry JC, Rosen J, Burns’ S (2007) Upper-limb powered exoskeleton design. IEEE/ASME Trans Mechatron 12(4):408–417

    Article  Google Scholar 

  9. Sanchez R, Reinkensmeyer D, Shah P et al (2004) Monitoring functional arm movement for home-based therapy after stroke. In: Annual international conference of the IEEE engineering in medicine and biology, pp 4787–4790: doi:10.1109/IEMBS.2004.1404325

  10. He W, Ge SS, Li Y, Chew E, Ng YS (2015) Neural network control of a rehabilitation robot by state and output feedback. J Intell Robot Syst 80(1):15–31. doi:10.1007/s10846-014-0150-6

    Article  Google Scholar 

  11. He W, Chen Y, Yin Z (2016) Adaptive neural network control of an uncertain robot with full-state constraints. IEEE Trans Cybern 46(3):620–629. doi:10.1109/TCYB.2015.2411285

    Article  Google Scholar 

  12. He W, Dong Y, Sun C (2016) Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Trans Syst Man Cyber Syst 46(3):334–344. doi:10.1109/TSMC.2015.2429555

    Article  Google Scholar 

  13. Xu G, Song A, Li H (2011) Control system design for an upper-limb rehabilitation robot. Adv Robot 25(1):229–251

    Article  Google Scholar 

  14. Richardson R, Brown M, Bhakta B, Levesley MC (2003) Design and control of a three degree of freedom pneumatic physiotherapy robot. Robotica 21:589–604. doi:10.1017/S0263574703005320

    Article  Google Scholar 

  15. Webster D, Celik O (2014) systematic review of kinect applications in elderly care and stroke rehabilitation. J Neuroeng Rehabil 11:108. doi:10.1186/1743-0003-11-108

    Article  Google Scholar 

  16. Du G, Zhang P (2014) Markerless human-robot interface for dual robot manipulators using Kinect sensor. Robot Comput Integr Manuf 30:150–159

    Article  Google Scholar 

  17. Chang YJ, Chen SF, Huang JD (2011) A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. Res Dev Disabil 32(6):2566–2570

    Article  Google Scholar 

  18. Hussain A, Roach N, Balasubramanian S, Burdet E (2012) A modular sensor-based system for the rehabilitation and assessment of manipulation. In: Haptics symposium (HAPTICS), 2012 IEEE, pp 247–254. doi:10.1109/HAPTIC.2012.6183798

  19. Su CJ, Chiang CY, Huang JY (2014) Kinect-enabled home-based rehabilitation system using Dynamic Time Warping and fuzzy logic. Appl Soft Comput 22:652–666

    Article  Google Scholar 

  20. Riener R, Lunenburger L, Jezernik S et al (2005) Patient cooperative strategies for robot-aided treadmill training: first experimental results. IEEE Trans Neural Syst Rehabil Eng 13(3):380–394. doi:10.1109/TNSRE.2005.848628

    Article  Google Scholar 

  21. Wolbrecht ET, Chan V, Reinkensmeyer DJ, Bobrow JE (2008) Optimizing compliant, model-based robotic assistance to promote neurorehabilitation. IEEE Trans Neural Syst Rehabil Eng 16(3):286–297. doi:10.1109/TNSRE.918389

    Article  Google Scholar 

  22. Dietz V, Fouad K, Bastiaanse CM (2001) Neuronal coordination of arm and leg movements during human locomotion. Eur J Neurosci 14(11):1906–1914. doi:10.1046/j.0953-816x.2001.01813.x

    Article  Google Scholar 

  23. Ferris DP, Huang HJ, Kao PC (2006) Moving the arms to activate the legs. Exerc Sport Sci Rev 34(3):113–120

    Article  Google Scholar 

  24. Yoon J, Novandy B, Yoon CH, Park KJ (2010) A 6-DOF gait rehabilitation robot with upper and lower limb connections that allows walking velocity updates on various terrains. IEEE/ASME Trans Mechatron 15(2):201–215. doi:10.1109/TMECH.2010.2040834

    Article  Google Scholar 

  25. Ni C, Fu J, Han R et al (2000) Rehabilitation therapy on recovery of the paretic arm functions in stroke patients. Chin J Phys Med Rehabil 22:204–206

    Google Scholar 

  26. Cong G, Pu S (2001) Effects of early rehabilitation on motor function of upper and lower extremities and activities of daily living in patients with hemiplegia after stroke. Chin J Rehabil Med 16(1):27–29

    Google Scholar 

  27. Pan L, Song A, Xu G, Xu B, Xiong P (2013) Hierarchical safety supervisory control strategy for robot-assisted rehabilitation exercise. Robotica 31:757–766. doi:10.1017/S0263574713000052

    Article  Google Scholar 

  28. Song A, Wu J, Qin G, Huang W (2007) A novel self-decoupled four degree-of-freedom wrist force/torque sensor. Measurement 40(9):883–891. doi:10.1016/j.measurement.2006.11.018

    Article  Google Scholar 

  29. Yu T (2014) Kinect application and development: natural human-machine interactive. China machine press, Beijing

    Google Scholar 

  30. Pan L, Song A, Xu G, Li H, Xu B (2013) Novel dynamic interpolation strategy for upper-limb rehabilitation robot. J Rehabil Robot 1:19–27

    Google Scholar 

  31. Long Y, Du Z, Wang W (2015) control and experiment for exoskeleton robot based on Kalman prediction of human motion intent. Robot 37(3):304–309

    Google Scholar 

  32. Cai Z (2000) Robotics. Tsinghua University Press, Beijing

    Google Scholar 

  33. Scaglia G, Rosales A, Quintero L, Mut V, Agarwal R (2010) A linear-interpolation-based controller design for trajectory tracking of mobile robots. Control Eng Pract 18:318–329. doi:10.1016/j.conengprac.2009.11.011

    Article  Google Scholar 

  34. Li THS, Su YT, Lai SW, Hu JJ (2011) Walking motion generation, synthesis, and control for biped robot by using PGRL, LPI, and Fuzzy Logic. IEEE Trans Syst Man Cybern Part B (Cybern) 41(3):736–748. doi:10.1109/TSMCB.2010.2089978

    Article  Google Scholar 

  35. Harada K, Hattori S, Hirukawa H et al (2010) Two-stage time-parametrized gait planning for humanoid robots. IEEE/ASME Trans Mechatron 15(5):694–703. doi:10.1109/TMECH.2009.2032180

    Article  Google Scholar 

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Acknowledgements

The authors appreciate all colleagues in Robot Sensor and Control Laboratory who sent valuable contributions to this work. This work was supported by the National Natural Science Foundation of China (No. 61325018, 61673114); Natural Science Foundation of Jiangsu Province (No. BK20141284); Science and Technology Support Program of Jiangsu Province (No. BE2014132); National Key Research and Development Plan (No.2016YFB1001300). The authors would also like to thank anonymous reviewers for their useful comments.

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Authors and Affiliations

  1. School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China

    Jing Bai, Aiguo Song, Baoguo Xu, Jieyan Nie & Huijun Li

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  1. Jing Bai
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  2. Aiguo Song
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  3. Baoguo Xu
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  4. Jieyan Nie
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Correspondence to Aiguo Song.

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Bai, J., Song, A., Xu, B. et al. A Novel Human-Robot Cooperative Method for Upper Extremity Rehabilitation. Int J of Soc Robotics 9, 265–275 (2017). https://doi.org/10.1007/s12369-016-0393-4

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  • DOI: https://doi.org/10.1007/s12369-016-0393-4

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