Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-25T05:27:15.817Z Has data issue: false hasContentIssue false
  • English
  • Français

Hierarchical safety supervisory control strategy for robot-assisted rehabilitation exercise

Published online by Cambridge University Press:  01 February 2013

Lizheng Pan ,
Aiguo Song ,
Guozheng Xu ,
Huijun Li ,
Baoguo Xu  and
Pengwen Xiong
Lizheng Pan
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
Aiguo Song*
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
Guozheng Xu
Affiliation:
College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210003, P. R. China
Huijun Li
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
Baoguo Xu
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
Pengwen Xiong
Affiliation:
School of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Clinical outcomes have shown that robot-assisted rehabilitation is potential of enhancing quantification of therapeutic process for patients with stroke. During robotic rehabilitation exercise, the assistive robot must guarantee subject's safety in emergency situations, e.g., sudden spasm or twitch, abruptly severe tremor, etc. This paper presents a hierarchical control strategy, which is proposed to improve the safety and robustness of the rehabilitation system. The proposed hierarchical architecture is composed of two main components: a high-level safety supervisory controller (SSC) and low-level position-based impedance controller (PBIC). The high-level SSC is used to automatically regulate the desired force for a reasonable disturbance or timely put the emergency mode into service according to the evaluated physical state of training impaired limb (PSTIL) to achieve safety and robustness. The low-level PBIC is implemented to achieve compliance between the robotic end-effector and the impaired limb during the robot-assisted rehabilitation training. The results of preliminary experiments demonstrate the effectiveness and potentiality of the proposed method for achieving safety and robustness of the rehabilitation robot.

Keywords

Rehabilitation robotSafety and robustnessRehabilitation exerciseCompliance control
Type
Articles
Information
Robotica , Volume 31 , Issue 5 , August 2013 , pp. 757 - 766
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Ducruet, A. F. and Connolly, E. S., “Targeting lymphocytes in ischemic stroke,” World Neurosurg. 76 (3), 212–214 (2011).CrossRefGoogle ScholarPubMed
2.Culmer, P. R., Jackson, A. E. and Makower, S., “A control strategy for upper limb robotic rehabilitation with a dual robot system,” IEEE/ASME Trans. Mechatronics 15 (4), 575–585 (2010).CrossRefGoogle Scholar
3.Barkana, D. E., Das, J. and Wang, F., “Incorporating verbal feedback into a robot-assisted rehabilitation system,” Robotica 29, 433–443 (2011).CrossRefGoogle Scholar
4.Khalessi, A. A., “Stroke and the neurosurgeon: Supplement to world neurosurgery,” World Neurosurg. 76 (3), 248–249 (2011).CrossRefGoogle ScholarPubMed
5.Wolf, S. L., Blanton, S., Baer, H., Breshears, J. and Butler, A. J., “Repetitive task practice: A critical review of constraint induced therapy in stroke,” Neurologist 8, 325–338 (2002).Google Scholar
6.Kwakkel, G., Wagenaar, R. C., Twisk, J. W., Lankhorst, G. J. and Koetsier, J. C., “Intensity of leg and armtraining after primarymiddle-cerebral artery stroke: A randomized trial,” Lancet 354, 191–196 (1999).CrossRefGoogle Scholar
7.Taub, E., Uswatte, G. and Pidikiti, R. D., “Contstraint-induced (CI) movement therapy: A new family of techniques with broad application to physical rehabilitation–-a clinical review,” J. Rehabil. Res. Dev. 36, 237–251 (1999).Google ScholarPubMed
8.Choi, Y., Gordon, J. and Kim, D., “An adaptive automated robotic task-practice system for rehabilitation of arm functions after stroke,” IEEE Trans. Robot. 25 (3), 556–568 (2009).CrossRefGoogle Scholar
9.Kwakkel, G., Kollen, B. J. and Krebs, H. I., “Effects of robot-assisted therapy on upper limb recovery after stroke: A systematic review,” Neurorehabil. Neural. Repair. 22 (2), 111–121 (2008).CrossRefGoogle Scholar
10.Eva, S., Saartje, D., Baeyens, J. P., Meeusen, R. and Eric, K., “Effectiveness of robot-assisted gait training in persons with spinal cord injury: A systematic review,” J. Rehabil. Med. 42, 520–526 (2010).Google Scholar
11.Bovolental, F. and Sale, P., “Robot-aided therapy for upper limbs in patients with stroke-related lesions: Brief report of a clinical experience,” J. Neuro Eng. Rehabil. 8 (18), (2011). doi:10.1186/1743-0003-8-18Google Scholar
12.Laura, M. C. and David, J. R.. “Review of control strategies for robotic movement training after neurologic injury,” J. Neuro Eng. Rehabil. 6 (20) (2009). doi:10.1186/1743-0003-6-20Google Scholar
13.Loureiro, R., Amirabdollahian, F., Topping, M., Driessen, B. and Harwin, W., “Upper limb mediated stroke therapy-GENTLE/s approach,” Auton. Robot. 15, 35–51 (2003).CrossRefGoogle Scholar
14.Johnson, M. J., “Recent trends in robot-assisted therapy environments to improve real-life functional performance after stroke,” J. Neuro Eng. Rehabil. 3 (29), 1–6 (2006).Google ScholarPubMed
15.Tejima, N. and Stefanov, D., “Fail-Safe Components for Rehabilitation Robots-A Reflex Mechanism and Fail-Safe Force Sensor,” Proceedings of the 9th International Conference on Rehabilitation Robotics, Chicago, Illinois (Jun. 28–Jul. 1, 2005).Google Scholar
16.Brian, D. and Yoky, M., “Modeling and system identification of a life-size brake-actuated manipulator,” IEEE Trans. Robot. 25 (3), 481–491 (2009).Google Scholar
17.Kirihara, K. and Saga, N., “Design and control of an upper limb rehabilitation support device for disabled people using a pneumatic cylinder,” Ind. Robot-An Int. 37 (4), 354–363 (2010).CrossRefGoogle Scholar
18.Morales, R., Badesa, F. J. and Sabater, J. M., “Pneumatic robotic systems for upper limb rehabilitation,” Med. Biol. Eng. Comput. 49 (10), 1145–1156 (2011).CrossRefGoogle ScholarPubMed
19.Fusco, F. and Gallerini, R., “Robotic Arm: The Problem of Preventing Collisions,” Proceedings of the 6th ESA Workshop on Advanced Space Technologies for Robotics and Automation, ESTEC, Noordwijk, Netherlands (Dec. 2000).Google Scholar
20.Aslam, P. and Jeha, R., “Safe physical human–robot interaction of mobility assistance robots: Evaluation index and control,” Robotica 29, 767–785 (2011).Google Scholar
21.Sorin, M. G., Thorsten, L., Christos, F., Cyriacks, M. and Graser, A., “A BCI-controlled robotic assistant for quadriplegic people in domestic and professional life,” Robotica 30, 419–431 (2012).Google Scholar
22.Heike, V., Alexander, D. W. and Robert, R., “Generalized Elasticities Improve Patient-Cooperative Control of Rehabilitation Robots,” Proceedings of the 11th International Conference on Rehabilitation Robotics, Kyoto, Japan (Jun. 23–26, 2009).Google Scholar
23.Krebs, H. I., Ferraro, M., Buerger, S. P., Newbery, M. J., Makiyama, A., Sandmann, M., Lynch, D., Volpe, B. T. and Hogan, N., “Rehabilitation robotics: Pilot trial of a spatial extension for MIT-Manus,” J. Neuro Eng. Rehabil. 1 (5), (2004).Google ScholarPubMed
24.Xu, G. Z., Song, A. G. and Li, H. J., “Control system design for an upper-limb rehabilitation robot,” Adv. Robot. 25 (1), 229–251 (2011).CrossRefGoogle Scholar
25.Song, A. G., Wu, J., Qin, G. and Huang, W. Y., “A novel self-decoupled four degree-of-freedom wrist force/torque sensor,” Measurement 40, 883–891 (2007).CrossRefGoogle Scholar
26.Ju, M. S., Lin, C. C. K., Lin, D. H. and Hwang, I. S., “A rehabilitation robot with force-position hybrid fuzzy controller: Hybrid fuzzy control of rehabilitation robot,” IEEE Trans. Neural Syst. Rehabil. Eng. 13 (3), 349–358 (2005).Google ScholarPubMed
27.Choi, J., “Approximate MAP detection with ordering and successive processing for iterative detection and decoding in MIMO systems,” IEEE J. Sel. Top. Signal Process. 5 (8), 1415–1425 (2011).CrossRefGoogle Scholar
28.Hogan, N., “Impedance control: An approach to manipulation: Part 1–theory, part 2–implementation, and part 3–application,” ASME J. Dyn. Syst. Meas. Control 107 (1), 1–24 (1985).CrossRefGoogle Scholar
29.Lawrence, D. A., “Impedance control stability properties in common implementations,” Proceedings of IEEE International Conference on Robotics and Automation, New York, USA (1185–1190, 1988).Google Scholar
30.Yang, Y., Wang, L., Tong, J. and Zhang, L. X., “Arm rehabilitation robot impedance control and experimentation,” Proceedings of IEEE International Conference on Robotics and Biomimetics, Kunming, China (December 914–918, 2006).Google Scholar
31.Qiao, B. and Lu, R. J., “Impedance force control for position controlled robotic manipulators under the constraint of unknown environments,” J. Southeast Univ. (English edition). 19 (4), 359–363 (2003).Google Scholar
32.Beyl, P., Knaepen, K., Duerinck, S., Damme, M. V., Vanderborght, B., Meeusen, R. and Lefeber, D., “Safe and compliant guidance by a powered knee exoskeleton for robot-assisted rehabilitation of gait,” Adv. Robot. 25 (5), 513–535 (2011).CrossRefGoogle Scholar
33.Tejima, N., Akioka, T., Kimura, S. and Araki, K., “Rehabilitation Robot Risk Reduction by Three-Dimensional Force Limitation and Reflex Mechanism,” Proceedings of the ICORR2003, Daeieon, Korea (April 23–25, 2003).Google Scholar
34.Mendoza, M. G., Reyes, F., Bonilla, I. G. and Gonzalez, E. G., “Proportional-derivative impedance control of robot manipulators for interaction tasks,” Proc. IMechE, Part I: J. Syst. Control Eng. 225 (6), 315–329 (2011).Google Scholar
35.Jiang, Z. H., “Impedance control of flexible robot arms with parametric uncertainties,” J. Intell. Robot. Syst. 42, 113–133 (2005).CrossRefGoogle Scholar
36.Pushparaj, M. P., Amalendu, M. and Anirvan, D., “Impedance control of space robots using passive degrees of freedom in controller domain,” Trans. ASME 127 (12), 564–578 (2005).Google Scholar
37.Spong, M. W., Hutchinson, S. and Vidyasagar, M., Robot Modeling And Control (John Wiley, New York, 2006).Google Scholar
38.Cooper, G. and Rodnitzky, R., “The many forms of tremor: Precise classification guides selection of therapy,” Postgrad. Med. 108 (1), 57–8, 61–4, 70 (2000).CrossRefGoogle ScholarPubMed
39.Hellwig, B. and Mund, P., “A longitudinal study of tremor frequencies in Parkinson's disease and essential tremor,” Clin. Neurophysiol. 120 (2), 431–435 (2009).CrossRefGoogle ScholarPubMed