Skip to main content
SpringerLink
Log in

Predictive equation for a circular trajectory period in a cable-driven robot for rehabilitation

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Rehabilitation training is the most effective way to reduce motor impairments in stroke patients, and cable-driven robots can be an efficient way to increase the intensity of this training. The motor functions of stroke patients are often related and evaluated using circle drawing/tracing tasks as studies show that these patients produce elliptical instead of circular shapes. Training can increase the hemiparetic arm workspace and the performance of circular design in these patients. Thus, this paper presents a robot actuated by cables that can be used in rehabilitation producing circular trajectories. First, the circular trajectory and mathematical model to keep the cables in tension are presented. Next, a serious game is described that uses the circular trajectory for stroke rehabilitation. The results showed that the predictive equation proposed in the present paper represented the experimental data with an average relative error of only 1%. Finally, an optimization was performed to obtain the shortest period of a circular trajectory.

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Tappeiner L, Ottaviano E, Husty ML (2018) A cable-driven robot for upper limb rehabilitation inspired by the mirror therapy. In: Zeghloul S, Romdhane L, Laribi M (eds) Computational kinematics, vol 50. Mechanisms and machine science. Springer, Cham

    Chapter  Google Scholar 

  2. Hatem SM, Saussez G, DellaFaille M et al (2016) Rehabilitation of motor function after stroke: a multiple systematic review focused on techniques to stimulate upper extremity recovery. Front Human Neurosci 10:1–22

    Article  Google Scholar 

  3. Gonçalves RS, Krebs HI (2017) MIT-Skywalker: considerations on the design of a body weight support system. J NeuroEng Rehabilit 14:88

    Article  Google Scholar 

  4. Mao Y et al (2015) Human movement training with a cable driven ARm EXoskeleton (CAREX). IEEE Trans Neural Syst Rehabilit Eng 23:1

    Article  Google Scholar 

  5. Beyl P et al. (2009) Safe and compliant guidance in robot-assisted gait rehabilitation using proxy-based sliding mode control. In: IEEE 11th international conference on rehabilitation robotics, Japan

  6. Barbosa AM, Carvalho JCM, Gonçalves RS (2018) Cable-driven lower limb rehabilitation robot. J Brazilian Soc Mech Sci Eng 40:245

    Article  Google Scholar 

  7. Alves T, D’Carvalho MC, Gonçalves RS (2018) Assist-as-needed” control in a robotic structure driven by cables for rehabilitation of human body joints, ENEBI 2018. Águas de Lindóia, Brazil

    Google Scholar 

  8. Gonçalves RS, Lobato FS, Carvalho JCM (2016) Design of a robotic device actuated by cables for human lower limb rehabilitation using self-adaptive differential evolution and robust optimization. Biosci J (Online) 32:1689–1702

    Article  Google Scholar 

  9. Gonçalves RS, Carvalho JCM, Ribeiro JF, Salim VV (2015) Cable-driven robot for upper and lower limbs rehabilitation. In: Habib MK (ed) Handbook of research on advancements in robotics and mechatronics, 1st edn. IGI Global, Hershey, pp 284–315

    Chapter  Google Scholar 

  10. Schmidt RA et al (2018) Motor control and learning: a behavioral emphasis, 6th edn. Human Kinetics Publishers, Champaign

    Google Scholar 

  11. Ceccarelli M, Romdhane L (2010) Design issues for human-machine platform interface in cable based parallel manipulators for physiotherapy applications. J Zhejiang Univ Sci A 11(4):231–239

    Article  Google Scholar 

  12. Gonçalves RS, Carvalho JCM, Rodrigues LAO, Barbosa AM (2013) Cable-driven parallel manipulator for lower limb rehabilitation. Appl Mech Mater 459:535–542

    Article  Google Scholar 

  13. Dipietro L, Krebs HI, Fasoli SE et al (2007) Changing motor synergies in chronic stroke. J Neurophysiol 98(2):757–768

    Article  Google Scholar 

  14. Hussain A, Budhota A, Contu S et al (2017) “Quantitative assessment of motor functions post-stroke: Responsiveness of upper-extremity robotic measures and its task dependence. In: IEEE international conference on rehabilitation robotics, pp 1037–1042

  15. Krabben T, Prange GB et al (2012) Influence of gravity compensation training on synergistic movement patterns of the upper extremity after stroke, a pilot study. J NeuroEng Rehabilit 9:44

    Article  Google Scholar 

  16. Murphy G (1950) Similitude in engineering. Ronald Press, New York

    Google Scholar 

  17. Rosati G, Masiero S, Rossi A (2017) On the use of cable-driven robots in early inpatient stroke rehabilitation. In: Boschetti G, Gasparetto A (eds) Advances in italian mechanism science: proceedings of the first international conference of IFToMM Italy. https://doi.org/10.1007/978-3-319-48375-7

  18. Alves T, D’Carvalho MC, Gonçalves RS (2019) Assist-as-needed control in a cable-actuated robot for human joints rehabilitation. J Mech Eng Biomech 5(3):57–62

    Article  Google Scholar 

  19. Appel VCR et al (2014) Classifying emotions in rehabilitation robotics based on facial skin temperature. In: 5th IEEE RAS EMBS international conference on biomedical robotics and biomechatronics, p. 276

  20. Moretti CB, Andrade KO, Caurin GAP (2014) Physiotherapy support web-based system for rehabilitation robotics: an initial architecture. In: ABCM symposium series in mechatronics, Vol. 6. Part I-international congress. Section IV-Robotics

  21. Dempster WT, Gaughran GRL (1889) Properties of body segments based on size and weight. Am J Anat 18(7414):33–54

    Google Scholar 

  22. Hassanien AE, Emary E (2016) Bat Algorithm (BA). Swarm intelligence—principles advances, and applications. CRC Press, Boca Raton, pp 15–41

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Federal University of Uberlândia, João Naves de Ávila, Uberlândia, MG, 2121, Brazil

    Thiago Alves & Rogério S. Gonçalves

Authors
  1. Thiago Alves
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rogério S. Gonçalves
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thiago Alves.

Additional information

Technical Editor: Adriano Almeida Gonçalves Siqueira.

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alves, T., Gonçalves, R.S. Predictive equation for a circular trajectory period in a cable-driven robot for rehabilitation. J Braz. Soc. Mech. Sci. Eng. 42, 279 (2020). https://doi.org/10.1007/s40430-020-02309-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-020-02309-2

Keywords

Search

Navigation