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Clinical experimental research on adaptive robot-aided therapy control methods for upper-limb rehabilitation

Published online by Cambridge University Press:  15 January 2014

Guozheng Xu ,
Aiguo Song ,
Lizheng Pan ,
Xiang Gao ,
Zhiwei Liang ,
Jinfei Li  and
Baoguo Xu
Guozheng Xu*
Affiliation:
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210046, P. R. China
Aiguo Song
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
Lizheng Pan
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
Xiang Gao
Affiliation:
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210046, P. R. China
Zhiwei Liang
Affiliation:
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210046, P. R. China
Jinfei Li
Affiliation:
Department of Rehabilitation Medicine of Nanjing Tongren Hospital, Nanjing 211102, P. R. China
Baoguo Xu
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
*
*Corresponding author. E-mail: [email protected]
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Summary

This study presents novel robotic therapy control algorithms for upper-limb rehabilitation, using newly developed passive and progressive resistance therapy modes. A fuzzy-logic based proportional-integral-derivative (PID) position control strategy, integrating a patient's biomechanical feedback into the control loop, is proposed for passive movements. This allows the robot to smoothly stretch the impaired limb through increasingly rigorous training trajectories. A fuzzy adaptive impedance force controller is addressed in the progressive resistance muscle strength training and the adaptive resistive force is generated according to the impaired limb's muscle strength recovery level, characterized by the online estimated impaired limb's bio-damping and bio-stiffness. The proposed methods are verified with a custom constructed therapeutic robot system featuring a Barrett WAM™ compliant manipulator. Twenty-four recruited stroke subjects were randomly allocated in experimental and control groups and enrolled in a 20-week rehabilitation training program. Preliminary results show that the proposed therapy control strategies can not only improve the impaired limb's joint range of motion but also enhance its muscle strength.

Keywords

Rehabilitation robotClinical experimentFuzzy logicPosition controlImpedance control
Type
Articles
Information
Robotica , Volume 32 , Issue 7 , October 2014 , pp. 1081 - 1100
Copyright
Copyright © Cambridge University Press 2014 

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References

1.Riener, R., Nef, T. and Colombo, G., “Robot-aided neurorehabilitation of the upper extremities,” Med. Biol. Eng. Comput. 43, 2–10 (2005).CrossRefGoogle ScholarPubMed
2.Crespo, L. M. and Reinkensmeyer, D. J., “Review of control strategies for robotic movement training after neurologic injury,” J. Neuro Eng. Rehabil. 6 (20), 1–15 (2009).Google Scholar
3.Anh, H. P. H. and Ahn, K. K., “Online tuning gain scheduling MIMO neural PID control of the 2-axes pneumatic artificial muscle (PAM) robot arm,” Expert Syst. Appl. 37, 6547–6560 (2010).Google Scholar
4.Chang, M. K., “An adaptive self-organizing fuzzy sliding mode controller for a 2-DOF rehabilitation robot actuated by pneumatic muscle actuators,” Control Eng. Pract. 18 (1), 13–32 (2010).Google Scholar
5.Novak, D., Mihelj, M. and Riener, R., “Psychophysiological measurements in a bio-cooperative feedback loop for upper extremity rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 19 (4), 400–410 (2011).Google Scholar
6.Frisolia, A., Salsedoa, F., Bergamascoa, M., Rossib, B. and Carboncini, M. C., “A force-feedback exoskeleton for upper-limb rehabilitation in virtual reality,” Appl. Bionics Biomech. 6, 115–126 (2009).Google Scholar
7.Veneman, J. F., Kruidhof, R., Hekman, E. E., Ekkelenkamp, R., Van Asseldonk, E. H. and Van der Kooij, H., “Design and evaluation of the LOPES exoskeleton robot for interactive gait rehabilitation,” IEEE Trans. Neural Syst. Rehabil. Eng. 15 (3), 379–385 (2007).Google Scholar
8.Kiguchi, K. and Hayashi, Y., “An EMG-based control for an upper-limb power-assist exoskeleton robot,” IEEE Trans. Syst. Man. Cybern. B Cybern. 42 (4), 1064–1071 (2012).Google Scholar
9.Kiguchi, K., Rahman, M. H., Sasaki, M. and Teramoto, K., “Development of a 3 DOF mobile exoskeleton robot for human upper-limb motion assist,” Robot. Auton. Syst. 56, 678–691 (2008).Google Scholar
10.Xu, G., Song, A. and Li, H., “Control system design for an upper-limb rehabilitation robot,” Adv. Robot. 25 (1), 229–251 (2011).CrossRefGoogle Scholar
11.Xu, G., Song, A. and Li, H., “Adaptive impedance control for upper-limb rehabilitation robot using evolutionary dynamic recurrent fuzzy neural network,” J. Intell. Robot. Syst. 62, 501–529 (2011).Google Scholar
12.Mizanoor Rahman, S. M. and Ikeura, R., “A novel variable impedance compact compliant ankle robot for overground gait rehabilitation and assistance,” Procedia Eng. 41, 522–531 (2012).CrossRefGoogle Scholar
13.Jiménez-Fabián, R. and Verlinden, O., “Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons,” Med. Eng. Phys. 34 (4), 397–408 (2012).CrossRefGoogle ScholarPubMed
14.Hughes, A. M., Freeman, C. T., Burridge, J. H., Chappell, P. H., Lewin, P. L. and Rogers, E., “Shoulder and elbow muscle activity during fully supported trajectory tracking in people who have had a stroke,” J. Electro. Kines. 20 (3), 465–476 (2010).CrossRefGoogle ScholarPubMed
15.Xu, G., Song, A., Pan, L., Li, H., Liang, Z., Zhu, S., Xu, B. and Li, J., “Adaptive hierarchical control for muscle strength training of stroke survivors in robot-aided upper-limb rehabilitation,” Int. J. Adv. Robot. Syst. 9, 1–12 (2012).CrossRefGoogle Scholar
16.Song, A., Wu, J., Qin, G. and Huang, W., “A novel self-decoupled four degree-of-freedom wrist force/torque sensor,” Measurement 40, 883–891 (2007).Google Scholar
17.Ryu, J. H., Kwon, D. S. and Hannaford, B., “Stable teleoperation with time-domain passivity control,” IEEE Trans. Robot. Autom. 20 (2), 365–373 (2004).Google Scholar
18.Song, A., Pan, L., Xu, G. and Li, H., “Impedance Identification and Adaptive Control of Rehabilitation Robot for Upper Limb Passive Training,” Proceedings of the 7th International Conference on Intelligent Systems and Knowledge Engineering, Beijing, China (Dec. 15ndash;17, 2012).Google Scholar
19.Noritsugu, T. and Tanaka, T., “Application of rubber artificial muscle manipulator as a rehabilitation robot,” IEEE Trans. Mechanics 2 (4), 259–267 (1997).Google Scholar
20.Zhang, L. Q., Chung, S. G., Bai, Z., Xu, D., van Rey, E. M., Rogers, M. W., Johnson, M. E. and Roth, E. J., “Intelligent stretching of ankle joints with contracture/spasticity,” IEEE Trans. Neural Syst. Rehabil. Eng. 10, 149–157 (2002).Google Scholar
21.Mallapragada, V., Erol, D. and Sarkar, N., “A new method of force control for unknown environments,” Int. J. Adv. Robot. Syst. 4 (3), 313–322 (2007).Google Scholar