Modeling and Evaluation of Human Motor Learning by Finger Manipulandum | SpringerLink
Skip to main content
Springer Nature Link
Log in

Modeling and Evaluation of Human Motor Learning by Finger Manipulandum

  • Conference paper
  • First Online:
Social Robotics (ICSR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13817))

Included in the following conference series:

Abstract

A finger manipulandum was developed to assess human motor learning in a virtual mirror game. The task is the leader-follower modality in the mirror paradigm. The follower in the virtual dynamic system is controlled by the force generated by the interaction between human and manipulandum due to pinching. One participant played the game for five consecutive days. The player's kinematic tracking error was found to fit the free energy model leading to motor learning. In addition, the acquired data were processed with a machine learning algorithm to predict the retention data. Both the free energy model and predictors were found to provide promising results for more detailed motor learning models of healthy subjects and stroke patients.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 10295
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 12869
Price includes VAT (Japan)
  • 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

Similar content being viewed by others

References

  1. Raffin, E., Hummel, F.: Restoring motor functions after stroke: multiple approaches and opportunities. Neuroscientist 24(4), 400–416 (2017)

    Article  Google Scholar 

  2. Hatem, S., et al.: Rehabilitation of motor function after stroke: a multiple systematic review focused on techniques to stimulate upper extremity recovery. Front. Hum. Neurosci. 10 (2016)

    Google Scholar 

  3. Krebs, H., Hogan, N., Aisen, M., Volpe, B.: Robot-aided neurorehabilitation. IEEE Trans. Rehabil. Eng. 6(1), 75–87 (1998)

    Article  Google Scholar 

  4. Colombo, R., et al.: Design strategies to improve patient motivation during robot-aided rehabilitation. J. NeuroEng. Rehab. 4(1) (2007)

    Google Scholar 

  5. Volpe, B., Krebs, H., Hogan, N., Edelstein, L., Diels, C., Aisen, M.: A novel approach to stroke rehabilitation. Neurology 54(10), 1938–1944 (2000)

    Article  Google Scholar 

  6. Oña, E., Garcia-Haro, J., Jardón, A., Balaguer, C.: Robotics in health care: perspectives of robot-aided interventions in clinical practice for rehabilitation of upper limbs. Appl. Sci. 9(13), 2586 (2019)

    Article  Google Scholar 

  7. Babaiasl, M., Mahdioun, S., Jaryani, P., Yazdani, M.: A review of technological and clinical aspects of robot-aided rehabilitation of upper-extremity after stroke. Disab. Rehab. Assist. Technol. 1–18 (2015)

    Google Scholar 

  8. Sveistrup, H.: J. NeuroEng. Rehab. 1(1), 10 (2004)

    Google Scholar 

  9. Colombo, R., Sanguineti, V.: Assistive controllers and modalities for robot-aided neurorehabilitation. Rehab. Robot. 63–74 (2018)

    Google Scholar 

  10. Krakauer, J.: The applicability of motor learning to neurorehabilitation. Oxford Textbook of Neurorehabilitation, pp. 55–64 (2015). https://doi.org/10.1093/med/9780199673711.003.0007

  11. Krakauer, J., Hadjiosif, A., Xu, J., Wong, A., Haith, A.: Motor Learning. Comprehensive Physiology, pp. 613–663 (2019). https://doi.org/10.1002/cphy.c170043

  12. Friston, K.: The free-energy principle: a rough guide to the brain? Trends Cogn. Sci. 13(7), 293–301 (2009)

    Article  Google Scholar 

  13. Demekas, D., Parr, T., Friston, K.J.: An investigation of the free energy principle for emotion recognition. Front. Comput. Neurosci. 14 (2020)

    Google Scholar 

  14. Brookes, J., et al.: Exploring disturbance as a force for good in motor learning (2019)

    Google Scholar 

  15. Haith, A., Krakauer, J.: Model-based and model-free mechanisms of human motor learning. Advances in Experimental Medicine and Biology, pp. 1–21 (2013). https://doi.org/10.1007/978-1-4614-5465-6_1

  16. Ueyama, Y.: System identification of neural mechanisms from trial-by-trial motor behaviour: modelling of learning, impairment and recovery. Adv. Robot. 31(3), 107–117 (2016). https://doi.org/10.1080/01691864.2016.1266966

  17. Casadio, M., Sanguineti, V.: Learning, retention, and slacking: a model of the dynamics of recovery in robot therapy. IEEE Trans. Neural Syst. Rehab. Eng. 20(3), 286–296 (2012). https://doi.org/10.1109/tnsre.2012.2190827

  18. Reinkensmeyer, D., et al.: Computational neurorehabilitation: modeling plasticity and learning to predict recovery. J. NeuroEng. Rehab. 13(1) (2016). https://doi.org/10.1186/s12984-016-0148-3

  19. Reinkensmeyer, D., Guigon, E., Maier, M.: A computational model of use-dependent motor recovery following a stroke: Optimizing corticospinal activations via reinforcement learning can explain residual capacity and other strength recovery dynamics. Neural Networks 29–30, 60–69 (2012). https://doi.org/10.1016/j.neunet.2012.02.002

  20. Yağmur, O.: Model-based Evaluation of the Control Strategies of a Hand Rehabilitation Robot Based on Motor Learning Principles. Middle East Technical University, MSc (2022)

    Google Scholar 

  21. Konvalinka, I., Vuust, P., Roepstorff, A., Frith, C.: Follow you, follow me: continuous mutual prediction and adaptation in joint tapping. Quar. J. Exper. Psychol. 63(11), 2220–2230 (2010)

    Article  Google Scholar 

  22. Noy, L., Dekel, E., Alon, U.: The mirror game as a paradigm for studying the dynamics of two people improvising motion together. Proc. Natl. Acad. Sci. 108(52), 20947–20952 (2011)

    Article  Google Scholar 

  23. Takagi, A., Ganesh, G., Yoshioka, T., Kawato, M., Burdet, E.: Physically interacting individuals estimate the partner’s goal to enhance their movements. Nature Hum. Behav. 1(3) (2017)

    Google Scholar 

  24. Ganesh, G., Takagi, A., Osu, R., Yoshioka, T., Kawato, M., Burdet, E.: Two is better than one: physical interactions improve motor performance in humans. Sci. Rep. 4(1) (2014)

    Google Scholar 

  25. Künzell, S., Sießmeir, D., Ewolds, H.: Validation of the continuous tracking paradigm for studying implicit motor learning. Exper. Psychol. 63(6), 318–325 (2016). https://doi.org/10.1027/1618-3169/a000343

  26. Özen, Ö., Buetler, K., Marchal-Crespo, L.: Promoting motor variability during robotic assistance enhances motor learning of dynamic tasks. Front. Neurosci. 14 (2021). https://doi.org/10.3389/fnins.2020.600059

  27. Howard, I., Ingram, J., Wolpert, D.: A modular planar robotic manipulandum with end-point torque control. J. Neurosci. Methods 181(2), 199–211 (2009)

    Article  Google Scholar 

  28. Millman, P., Colgate, J.: Design of a four degree-of-freedom force-reflecting manipulandum with a specified force/torque workspace. In: Proceedings. 1991 IEEE International Conference on Robotics and Automation (1991)

    Google Scholar 

  29. Metzger, J., Lambercy, O., Chapuis, D., Gassert, R.: Design and characterization of the ReHapticKnob, a robot for assessment and therapy of hand function. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (2011). https://doi.org/10.1109/iros.2011.6094882

  30. Metzger, J., Lambercy, O., Gassert, R.: High-fidelity rendering of virtual objects with the ReHapticKnob - novel avenues in robot assisted rehabilitation of hand function. In: 2012 IEEE Haptics Symposium (HAPTICS) (2012). https://doi.org/10.1109/haptic.2012.6183769

  31. Karl, F.: A free energy principle for biological systems. Entropy 14(11), 2100–2121 (2012)

    Article  MATH  Google Scholar 

  32. Clark, A.: Whatever next? predictive brains, situated agents, and the future of cognitive science. Behav. Brain Sci. 36(3), 181–204 (2013)

    Article  Google Scholar 

  33. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735

  34. Sherstinsky, A.: Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network. Phys. D Nonlinear Phenomena 404, 132306 (2020). https://doi.org/10.1016/j.physd.2019.132306

  35. Zhai, C., Alderisio, F., Słowiński, P., Tsaneva-Atanasova, K., di Bernardo, M.: Design of a virtual player for joint improvisation with humans in the mirror game. PLoS ONE 11(4), e0154361 (2016)

    Article  Google Scholar 

  36. Friston, K.J., Stephan, K.E.: Free-energy and the brain. Synthese 159(3), 417–458 (2007). https://doi.org/10.1007/s11229-007-9237-y

    Article  Google Scholar 

  37. Annis, J., Miller, B.J., Palmeri, T.J.: Bayesian inference with Stan: a tutorial on adding custom distributions. Behav. Res. Methods 49(3), 863–886 (2016). https://doi.org/10.3758/s13428-016-0746-9

    Article  Google Scholar 

  38. Muth, C., Oravecz, Z., Gabry, J.: User-friendly bayesian regression modeling: a tutorial with RSTANARM and Shinystan. Quant. Meth. Psychol. 14(2), 99–119 (2018). https://doi.org/10.20982/tqmp.14.2.p099

  39. Pan, S., Duraisamy, K.: On the structure of time-delay embedding in linear models of non-linear dynamical systems (2019)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Middle East Technical University, Ankara, Turkey

    Amr Okasha, Ozancan Özdemir, Ceylan Yozgatlıgil & Ali E. Turgut

  2. TED University, Ankara, Turkey

    Sabahat Şengezer & Kutluk B. Arıkan

Authors
  1. Amr Okasha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sabahat Şengezer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ozancan Özdemir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ceylan Yozgatlıgil
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ali E. Turgut
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kutluk B. Arıkan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amr Okasha .

Editor information

Editors and Affiliations

  1. University of Florence, Florence, Italy

    Filippo Cavallo

  2. Qatar University, Doha, Qatar

    John-John Cabibihan

  3. University of Florence, Florence, Italy

    Laura Fiorini

  4. University of Florence, Florence, Italy

    Alessandra Sorrentino

  5. Wichita State University, Wichita, KS, USA

    Hongsheng He

  6. Qingdao University, Qingdao, China

    Xiaorui Liu

  7. National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan

    Yoshio Matsumoto

  8. National University of Singapore, Singapore, Singapore

    Shuzhi Sam Ge

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Okasha, A., Şengezer, S., Özdemir, O., Yozgatlıgil, C., Turgut, A.E., Arıkan, K.B. (2022). Modeling and Evaluation of Human Motor Learning by Finger Manipulandum. In: Cavallo, F., et al. Social Robotics. ICSR 2022. Lecture Notes in Computer Science(), vol 13817. Springer, Cham. https://doi.org/10.1007/978-3-031-24667-8_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-24667-8_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24666-1

  • Online ISBN: 978-3-031-24667-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation