Skip to main content
SpringerLink
Log in

Learning-Based Compensation-Corrective Control Strategy for Upper Limb Rehabilitation Robots

  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

Abstract

Trunk compensations are commonly observed when stroke patients perform reaching tasks, that negatively affect their long-term motor recovery. To restrain the compensatory patterns, this study proposes a learning-based compensation-corrective (LBCC) control strategy for upper limb rehabilitation robots. The proposed LBCC strategy comprises a learning and a reproduction phase. Specifically, a learning from demonstration framework is employed to generalize the referenced task in the learning phase. The compensatory patterns are corrected by shoulder restraint, hand assistive, and coupling force feedback, which are generated by the LBCC control strategy, in the reproduction phase. Experiments were carried out on ten healthy subjects as a feasibility study. The trunk compensations were significantly reduced in three types of reaching tasks with the force feedback. In addition, the proposed LBCC control strategy significantly enhances the upper limb motor performance, therefore, providing a user experience similar to human-assisted rehabilitation for stroke patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Burton JK, Ferguson EEC, Barugh AJ et al (2018) Predicting discharge to institutional long-term care after stroke: a systematic review and metaanalysis. J Am Geriatr Soc 66:161–169

    Article  Google Scholar 

  2. Liu L, Wang D, Lawrence Wong KS, Wang Y (2011) Stroke and stroke care in China huge burden, significant workload, and a national priority. Stroke 42:3651–3654

    Article  Google Scholar 

  3. Veerbeek JM, Van Wegen E, Van Peppen R et al (2014) What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS One 9:e87987

    Article  Google Scholar 

  4. Levin MF, Kleim JA, Wolf SL (2009) What do motor “recovery’’ and “compensationg’’ mean in patients following stroke? Neurorehabilit Neural Repair 23:313–319

    Article  Google Scholar 

  5. Levin MF, Liebermann DG, Parmet Y, Berman S (2016) Compensatory versus noncompensatory shoulder movements used for reaching in stroke. Neurorehabilit Neural Repair 30:635–646

    Article  Google Scholar 

  6. Dewald JPA, Beer RF (2001) Abnormal joint torque patterns in the paretic upper limb of subjects with hemiparesis. Muscle Nerve 24:273–283

    Article  Google Scholar 

  7. Alaverdashvili M, Foroud A, Lim DH, Whishaw IQ (2008) “Learned baduse’’ limits recovery of skilled reaching for food after forelimb motor cortex stroke in rats: a new analysis of the effect of gestures on success. Behav Brain Res 188:281–290

    Article  Google Scholar 

  8. Wee SK, Hughes AM, Warner M, Burridge JH (2014) Trunk restraint to promote upper extremity recovery in stroke patients: a systematic review and meta-analysis. Neurorehabilit Neural Repair 28:660–677

    Article  Google Scholar 

  9. Greisberger A, Aviv H, Garbade SF, Diermayr G (2016) Clinical relevance of the effects of reach-to-grasp training using trunk restraint in individuals with hemiparesis poststroke: a systematic review. J Rehabil Med 48:405–416

    Article  Google Scholar 

  10. Ernst E (1990) A review of stroke rehabilitation and physiotherapy. Stroke 21:1081–1085

    Article  Google Scholar 

  11. Pain LM, Baker R, Richardson D, Agur AMR (2015) Effect of trunk-restraint training on function and compensatory trunk, shoulder and elbow patterns during post-stroke reach: a systematic review. Disabil Rehabil 37:553–562

    Article  Google Scholar 

  12. Thielman G (2010) Rehabilitation of reaching poststroke: a randomized pilot investigation of tactile versus auditory feedback for trunk control. J Neurol Phys Ther 34:138–144

    Article  Google Scholar 

  13. Van Vugt FT, Kafczyk T, Kuhn W et al (2016) The role of auditory feedback in music-supported stroke rehabilitation: a single-blinded randomised controlled intervention. Restor Neurol Neurosci 34:297–311

    Google Scholar 

  14. Alankus G, Kelleher C (2015) Reducing compensatory motions in motion-based video games for stroke rehabilitation. Hum Comput Interact 30:232–262

    Article  Google Scholar 

  15. Lin S, Mann J, Mansfield A et al (2019) Investigating the feasibility and acceptability of real-time visual feedback in reducing compensatory motions during self-administered stroke rehabilitation exercises: a pilot study with chronic stroke survivors. J Rehabil Assist Technol Eng 6:205566831983163

    Google Scholar 

  16. Cirstea MC, Levin MF (2000) Compensatory strategies for reaching in stroke. Brain 123:940–953

    Article  Google Scholar 

  17. Foreman M (2018) Changes in trunk compensation and reaching performance as a result of a novel virtual reality-based intervention for people with chronic stroke. Am J Occup Ther 72:7211515272p1–7211515272p1

  18. Valdés BA, Schneider AN, Van der Loos HFM (2017) Reducing trunk compensation in stroke survivors: a randomized crossover trial comparing visual and force feedback modalities. Arch Phys Med Rehabil 98:1932–1940

    Article  Google Scholar 

  19. Valdés BA, Van der Loos HFM (2018) Biofeedback vs. game scores for reducing trunk compensation after stroke: a randomized crossover trial. Top Stroke Rehabil 25:96–113

    Article  Google Scholar 

  20. Cai S, Li G, Su E et al (2020) Real-time detection of compensatory patterns in patients with stroke to reduce compensation during robotic rehabilitation therapy. IEEE J Biomed Heal Inform 24:2630–2638

    Article  Google Scholar 

  21. Cai S, Wei X, Su E et al (2020) Online compensation detecting for real-time reduction of compensatory motions during reaching: a pilot study with stroke survivors. J Neuroeng Rehabil 17:1–11

    Article  Google Scholar 

  22. Mayo NE, Wood-Dauphinee S, Ahmed S et al (1999) Disablement following stroke. Disabil Rehabil 21:258–268

    Article  Google Scholar 

  23. Maaref M, Rezazadeh A, Shamaei K et al (2016) A bicycle cranking model for assist-as-needed robotic rehabilitation therapy using learning from demonstration. IEEE Robot Autom Lett 1:653–660

    Article  Google Scholar 

  24. Lin C, Wu W, Lin G et al (2021) Design and control of a seven degrees-of-freedom semi-exoskeleton upper limb robot. International Conference on Social Robotics. Springer, Cham, 2021: 596–605

  25. Chen CH, Ramanan D (2017) 3D human pose estimation = 2D pose estimation + matching. In: Proceedings of the 30th IEEE conference on computer vision and pattern recognition, CVPR 2017 2017-Jan, pp 5759–5767

  26. Huang J, Huo W, Xu W et al (2015) Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition. IEEE Trans Autom Sci Eng 12:1257–1270

    Article  Google Scholar 

  27. Chen SH, Lien WM, Wang WW et al (2016) Assistive control system for upper limb rehabilitation robot. IEEE Trans Neural Syst Rehabil Eng 24:1199–1209

    Article  Google Scholar 

  28. Hogan N (1985) Impedance control: an approach to manipulation: part I-theory. J Dyn Syst Meas Control Trans ASME 107:1–7

    Article  MATH  Google Scholar 

  29. Culmer PR, Jackson AE, Makower S et al (2010) A control strategy for upper limb robotic rehabilitation with a dual robot system. IEEE/ASME Trans Mechatron 15:575–585

    Article  Google Scholar 

  30. Yu W, Perrusquía A (2020) Simplified stable admittance control using end-effector orientations. Int J Soc Robot 12:1061–1073

    Article  Google Scholar 

  31. Calinon S (2016) A tutorial on task-parameterized movement learning and retrieval. Intell Serv Robot 9:1–29

    Article  Google Scholar 

  32. Akgun B, Cakmak M, Jiang K, Thomaz AL (2012) Keyframe-based learning from demonstration: method and evaluation. Int J Soc Robot 4:343–355

    Article  Google Scholar 

  33. Suay HB, Toris R, Chernova S (2012) A practical comparison of three robot learning from demonstration algorithm. Int J Soc Robot 4:319–330

    Article  Google Scholar 

  34. Shahbazi M, Atashzar SF, Tavakoli M, Patel RV (2018) Position-force domain passivity of the human arm in telerobotic systems. IEEE/ASME Trans Mechatron 23:552–562

    Article  Google Scholar 

  35. Ranganathan R, Wang R, Dong B, Biswas S (2017) Identifying compensatory movement patterns in the upper extremity using a wearable sensor system. Physiol Meas 38:2222–2234

    Article  Google Scholar 

  36. Ma K, Chen Y, Zhang X et al (2019) sEMG-based trunk compensation detection in rehabilitation training. Front Neurosci 13:1–12

    Article  Google Scholar 

  37. Huang S, Cai S, Li G et al (2019) SEMG-based detection of compensation caused by fatigue during rehabilitation therapy: a pilot study. IEEE Access 7:127055–127065

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Weifeng Wu, Gengliang Lin and Yonghao Song for functional assessments and useful discussions. The authors also would like to thank Professor Haizhou Li for his insightful comments.

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant No. 52075177), the National Key Research and Development Program of China (Grant No. 2021YFB3301400), Research Foundation of Guangdong Province (Grant Nos. 2019A050505001 and 2018KZDXM002), Guangzhou Research Foundation (Grant Nos. 202002030324 and 201903010028), Zhongshan Research Foundation (Grant Nos. 2020B2020 and 2021B2022).

Author information

Author notes

  1. Peimin Xie and Chengqi Lin have contributed equally to this work.

Authors and Affiliations

  1. Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, Guangdong, China

    Peimin Xie, Chengqi Lin & Longhan Xie

  2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Siqi Cai

  3. Institute of Modern Industrial Technology, South China University of Technology, Zhongshan, Guangdong, China

    Longhan Xie

Authors
  1. Peimin Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengqi Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Siqi Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Longhan Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Siqi Cai or Longhan Xie.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Statement

Informed consent was obtained from all the subjects to complete the protocol approved by the Guangzhou First People’s Hospital Department of Ethics Committee. All the procedures were performed in accordance with the Declaration of Helsinki.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xie, P., Lin, C., Cai, S. et al. Learning-Based Compensation-Corrective Control Strategy for Upper Limb Rehabilitation Robots. Int J of Soc Robotics (2022). https://doi.org/10.1007/s12369-022-00943-5

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12369-022-00943-5

Keywords

Search

Navigation