Skip to main content
SpringerLink
Log in

Grasp Rehabilitation of Stroke Patients Through Object Manipulation with an Intelligent Power Assist Robotic System

  • Conference paper
  • First Online:
Proceedings of the Future Technologies Conference (FTC) 2019 (FTC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1070))

Included in the following conference series:

  • 1531 Accesses

Abstract

A 1-DOF experimental power assist robotic system was developed for grasping and lifting objects tied with it by stroke patients as a part of their upper arm grasp rehabilitation practices. A human-in-the-loop model of the targeted dynamics for lifting objects with the system was developed that considered prospective patient’s weight perception. A position control scheme based on the weight-perception-based dynamics model was developed and implemented. The control parameters were the virtual mass values used for the grasped and manipulated object. The virtual mass values that produced satisfactory human-robot interactions (HRI) in terms of maneuverability, motion, stability, health and safety, etc. were determined. The results showed that a few sets of mass values could produce satisfactory HRI. Then, stroke patients grasped and manipulated objects with the system separately for the most satisfactory virtual mass values that provided optimum kinesthetic perception. In separate experiments, the virtual mass value was exponentially reduced when the patients manipulated the objects with the system, which provided variation in kinesthetic perception. The results showed that the power-assisted manipulation with optimum kinesthetic perception significantly contributed to stroke rehabilitation. The results also showed that the rehabilitation performance varied with the variation in the kinesthetic perception. In addition, effectiveness of a proposed game-like visual interface and smart tele-rehabilitation for connected health was investigated.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 299.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 379.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barsotti, M., et al.: A full upper limb robotic exoskeleton for reaching and grasping rehabilitation triggered by MI-BCI. In: Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore, pp. 49–54 (2015)

    Google Scholar 

  2. Loureiro, R.C.V., Harwin, W.S.: Reach & grasp therapy: design and control of a 9-DOF robotic neuro-rehabilitation system. In: Proceedings of the 2007 IEEE 10th International Conference on Rehabilitation Robotics, Noordwijk, pp. 757–763 (2007)

    Google Scholar 

  3. Loureiro, R.C.V., Lamperd, B., Collin, C., Harwin, W.S.: Reach & grasp therapy: effects of the Gentle/G system assessing sub-acute stroke whole-arm rehabilitation. In: Proceedings of the 2009 IEEE International Conference on Rehabilitation Robotics, Kyoto, pp. 755–760 (2009)

    Google Scholar 

  4. Podobnik, J., Mihelj, M., Munih, M.: Upper limb and grasp rehabilitation and evaluation of stroke patients using HenRiE device. In: Proceedings of the 2009 Virtual Rehabilitation International Conference, Haifa, pp. 173–178 (2009)

    Google Scholar 

  5. Lambercy, O., et al.: Rehabilitation of grasping and forearm pronation/supination with the Haptic Knob. In: Proceedings of the 2009 IEEE International Conference on Rehabilitation Robotics, Kyoto, pp. 22–27 (2009)

    Google Scholar 

  6. Ma, S., Varley, M., Shark, L.K., Richards, J.: EMG biofeedback based VR system for hand rotation and grasping rehabilitation. In: Proceedings of the 2010 14th International Conference on Information Visualisation, London, pp. 479–484 (2010)

    Google Scholar 

  7. Crema, A., et al.: A hybrid tool for reaching and grasping rehabilitation: the ArmeoFES. In: Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, pp. 3047–3050 (2011)

    Google Scholar 

  8. Kim, H., et al.: Kinematic data analysis for post-stroke patients following bilateral versus unilateral rehabilitation with an upper limb wearable robotic system. IEEE Trans. Neural Syst. Rehabil. Eng. 21(2), 153–164 (2013)

    Article  Google Scholar 

  9. Nilsson, M., Ingvast, J., Wikander, J., von Holst, H.: The soft extra muscle system for improving the grasping capability in neurological rehabilitation. In: Proceedings of the 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, Langkawi, pp. 412–417 (2012)

    Google Scholar 

  10. Park, W., Jeong, W., Kwon, G.H., Kim, Y.H., Kim, L.: A rehabilitation device to improve the hand grasp function of stroke patients using a patient-driven approach. In: Proceedings of the 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR), Seattle, WA, pp. 1–4 (2013)

    Google Scholar 

  11. Kakoty, N.M., Hazarika, S.M., Koul, M.H., Saha, S.K.: Model predictive control for finger joint trajectory of TU Biomimetic hand. In: Proceedings of the 2014 IEEE International Conference on Mechatronics and Automation, Tianjin, pp. 1225–1230 (2014)

    Google Scholar 

  12. Altobelli, A., Bianchi, M., Serio, A., Baud-Bovy, G., Gabiccini, M., Bicchi, A.: Three-digit grasp haptic device with variable contact stiffness for rehabilitation and human grasping studies. In: Proceedings of the 22nd Mediterranean Conference on Control and Automation, Palermo, pp. 346–350 (2014)

    Google Scholar 

  13. Levin, M.F., Magdalon, E.C., Michaelsen, S.M., Quevedo, A.A.F.: Quality of grasping and the role of haptics in a 3-D immersive virtual reality environment in individuals with stroke. IEEE Trans. Neural Syst. Rehabil. Eng. 23(6), 1047–1055 (2015)

    Article  Google Scholar 

  14. Ma, Z., Ben-Tzvi, P., Danoff, J.: Hand rehabilitation learning system with an exoskeleton robotic glove. IEEE Trans. Neural Syst. Rehabil. Eng. 24(12), 1323–1332 (2016)

    Article  Google Scholar 

  15. Hussain, I., Meli, L., Pacchierotti, C., Prattichizzo, D.: A soft robotic supernumerary finger and a wearable cutaneous finger interface to compensate the missing grasping capabilities in chronic stroke patients. In: Proceedings of the 2017 IEEE World Haptics Conference (WHC), Munich, pp. 183–188 (2017)

    Google Scholar 

  16. Igarashi, K., Katsura, S.: Position based free-motion data connecting by using minimum force-differential model. In: Proceedings of the IEEE International Conference on Mechatronics, pp. 535–540 (2015)

    Google Scholar 

  17. Kazerooni, H.: Extender: a case study for human-robot interaction via transfer of power and information signals. In: Proceedings of the IEEE International Workshop on Robot and Human Communication, pp. 10–20 (1993)

    Google Scholar 

  18. Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H.: Design and control of a 1DOF power assist robot for lifting objects based on human operator’s unimanual and bimanual weight discrimination. In: Proceedings of the 2009 IEEE International Conference on Mechatronics and Automation (ICMA 2009), Changchun, China, 9–12 August 2009, pp. 3637–3644 (2009)

    Google Scholar 

  19. Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H.: Study on optimum maneuverability in horizontal manipulation of objects with power-assist based on weight perception. In: Proceedings of SPIE, vol. 7500, p. 75000P (2010)

    Google Scholar 

  20. Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H.: Displacement-load force-perceived weight relationships in lifting objects with power-assist. In: Proceedings of SPIE, vol. 7500, p. 75000S (2010)

    Google Scholar 

  21. Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H.: Lifting objects with a power assist system: effects of friction between human’s hand and object on perceived weight and load force. In: Proceedings of the 2009 IEEE/SICE International Symposium on System Integration, Tokyo, Japan, 29 November 2009, pp. 77–82 (2009)

    Google Scholar 

  22. Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H.: Human operator’s load force characteristics in lifting objects with a power assist robot in worst-cases conditions. In: Proceedings of the 2009 IEEE Workshop on Advanced Robotics and Its Social Impacts, Tokyo, Japan, 23–25 November 2009, pp. 126–131 (2009)

    Google Scholar 

  23. Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H.: Human’s weight perception and load force characteristics in lifting objects with a power assist robot. In: Proceedings of the 2009 IEEE International Symposium on Micro-NanoMechatronics and Human Science (MHS 2009), Nagoya, Japan, 8–11 November 2009, pp. 535–540 (2009)

    Google Scholar 

  24. Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H.: Control of a power assist robot for lifting objects based on human operator’s perception of object weight. In: Proceedings of the 18th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2009), Toyama, Japan, 27 September–2 October 2009, pp. 84–90 (2009)

    Google Scholar 

  25. Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H.: Unimanual and bimanual weight discrimination in lifting objects with a power assist system. In: Proceedings of the 2009 IEEE/ICROS-SICE International Joint Conference (ICCAS-SICE 2009), Fukuoka, Japan, 18–21 August 2009, pp. 4787–4792 (2009)

    Google Scholar 

  26. Gordon, A., Forssberg, H., Johansson, R., Westling, G.: Visual size cues in the programming of manipulative forces during precision grip. Exp. Brain Res. 83(3), 477–482 (1991)

    Google Scholar 

  27. Rahman, S.M.M., Ikeura, R.: Cognition-based variable admittance control for active compliance in flexible manipulation of heavy objects with a power assist robotic system. Robot. Biomim. 5(7), 1–25 (2018)

    Google Scholar 

  28. Rahman, S.M.M., Ikeura, R.: Weight-perception-based fixed and variable admittance control algorithms for unimanual and bimanual lifting of objects with a power assist robotic system. Int. J. Adv. Robot. Syst. 15(4), 1–15 (2018)

    Google Scholar 

  29. Rahman, S.M.M., Ikeura, R.: MPC to optimise performance in power-assisted manipulation of industrial objects. IET Electr. Power Appl. 11(7), 1235–1244 (2017)

    Article  Google Scholar 

  30. Rahman, S.M.M., Ikeura, R., Hayakawa, S., Yu, H.: Lifting objects with power-assist: weight-perception-based force control concepts to improve maneuverability. Adv. Eng. Forum-Special Theme Mechatron. Inf. Technol. 2–3, 277–280 (2012)

    Google Scholar 

  31. Rahman, S.M.M., Ikeura, R.: Cognition-based control and optimization algorithms for optimizing human-robot interactions in power assisted object manipulation. J. Inf. Sci. Eng. 32(5), 1325–1344 (2016)

    MathSciNet  Google Scholar 

  32. Rahman, S.M.M., Ikeura, R.: Weight-perception-based novel control of a power-assist robot for the cooperative lifting of light-weight objects. Int. J. Adv. Robot. Syst. 9(118), 1–13 (2012)

    Google Scholar 

  33. Rahman, S.M.M., Ikeura, R., Nobe, M., Sawai, H.: A psychophysical model of the power assist system for lifting objects. In: Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, USA, pp. 4125–4130 (2009)

    Google Scholar 

  34. Rahman, S.M.M., Ikeura, R.: Weight-prediction-based predictive optimal position and force controls of a power assist robotic system for object manipulation. IEEE Trans. Ind. Electron. 63(9), 5964–5975 (2016)

    Article  Google Scholar 

  35. Valdés, B.A., et al.: Usability testing of gaming and social media applications for stroke and cerebral palsy upper limb rehabilitation. In: Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3602–3605 (2014)

    Google Scholar 

  36. Kawai, Y., Honda, K., Kawai, H., Miyoshi, T., Fujita, M.: Tele-rehabilitation system for human lower limb using electrical stimulation based on bilateral teleoperation. In: Proceedings of the IEEE Conference on Control Technology and Applications, HI, pp. 1446–1451 (2017)

    Google Scholar 

  37. Rahman, S.M.M.: A novel variable impedance compact compliant series elastic actuator for human-friendly soft robotics applications. In: Proceedings of the 21st IEEE International Symposium on Robot and Human Interactive Communication, Paris, France, 9–13 September 2012, pp. 19–24 (2012)

    Google Scholar 

  38. Rahman, S.M.M.: A novel variable impedance compact compliant series elastic actuator: analysis of design, dynamics, materials and manufacturing. Appl. Mech. Mater. 245, 99–106 (2013)

    Article  Google Scholar 

  39. Rahman, S.M.M., Ikeura, R.: A novel variable impedance compact compliant ankle robot for overground gait rehabilitation and assistance. Procedia Eng. 41, 522–531 (2012)

    Article  Google Scholar 

  40. Rahman, S.M.M.: Design of a modular knee-ankle-foot-orthosis using soft actuator for gait rehabilitation. In: Proceedings of the 14th Annual Conference on Towards Autonomous Robotic Systems (TAROS 2013). Lecture Notes in Computer Science, vol. 8069, Oxford University, U.K., 28–30 August 2013, pp. 195–209. Springer, Heidelberg, July 2014

    Google Scholar 

  41. Yu, H., Rahman, S.M.M., Zhu, C.: Preliminary design analysis of a novel variable impedance compact compliant actuator. In: Proceedings of 2011 IEEE International Conference on Robotics and Biomimetics, Phuket, Thailand, 7–11 December 2011, pp. 2553–2558 (2011)

    Google Scholar 

  42. Rahman, S.M.M., Ikeura, R., Hayakawa, S.: Novel human-centric force control methods of power assist robots for object manipulation. In: Proceedings of 2013 IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2013), Shenzhen, China, 12–14 December 2013, pp. 340–345 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of West Florida, Pensacola, FL, 32514, USA

    S. M. Mizanoor Rahman

Authors
  1. S. M. Mizanoor Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. M. Mizanoor Rahman .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mizanoor Rahman, S.M. (2020). Grasp Rehabilitation of Stroke Patients Through Object Manipulation with an Intelligent Power Assist Robotic System. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-32523-7_16

Download citation

Publish with us

Policies and ethics

Search

Navigation