Skip to main content

Motor Learning and Machine Learning: Predicting the Amount of Sessions to Learn the Tracing Task

  • 403 Accesses

Part of the Communications in Computer and Information Science book series (CCIS,volume 1068)

Abstract

Motor activities are the main way of interacting with the world. Therefore, loss of ability to perform some of these activities, e.g. as a result of a neurological disease, is a serious injury to the individual. The present work aims to propose an approach to estimate the number of sessions required to learn a motor task. In the literature, there are many works on motor learning, mostly looking for ways to decrease the time of skill acquisition or motor rehabilitation. However, few works concentrate on trying to estimate the training time needed to achieve certain motor performance. In the experiment, three sessions - one session per day - were performed for each participant, whose purpose was to predict in which tracing task session the participant would reach a certain error based on their profile and initial performance. The classification models were: Algorithm K-Neighbors Nearer (KNN), Neural Network Multi Layer Perceptron (MLP), Decision Tree (AD) and Support Vector Machine (SVM). They were compared using three metrics, namely: Accuracy, F1-Score and Cohen Kappa coefficient. MLP, SVM and AD, had similar results for Accuracy and Cohen Kappa coefficient, but better than KNN, whereas for the F1-Score MPL performed better than all. This work showed the possibility of estimating the number of sessions to achieve a certain performance using prediction algorithms. This finding suggests that a similar approach may be used to estimate the amount of training a patient requires to rehabilitate.

Keywords

  • Motor skill learning
  • Machine learning
  • Tracing task

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-36636-0_2
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-36636-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

Notes

  1. 1.

    https://invokeit.wordpress.com/frequency-word-lists/.

  2. 2.

    www.theleagueofmoveabletype.com.

References

  1. Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J.: An inventory for measuring depression. Arch. Gen. Psychiatry 4(6), 561–571 (1961)

    CrossRef  Google Scholar 

  2. Carvalho, M.B.F.: Evaluate plataform of automatic motor learning skill, monograph (Computer Engineering), UFMA (Federal Uniersvity of Maranhão), São Luís, Brazil (2018)

    Google Scholar 

  3. Espírito-Santo, H., Pires, C.F., Garcia, I.Q., Daniel, F., Silva, A.G., Fazio, R.L.: Preliminary validation of the portuguese edinburgh handedness inventory in an adult sample. Appl. Neuropsychol. Adult 24(3), 275–287 (2017)

    CrossRef  Google Scholar 

  4. Hahne, J.M., et al.: Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control. IEEE Trans. Neural Syst. Rehabil. Eng. 22(2), 269–279 (2014)

    CrossRef  Google Scholar 

  5. Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)

    MathSciNet  CrossRef  Google Scholar 

  6. Kitago, T., Krakauer, J.W.: Motor learning principles for neurorehabilitation. Handb. Clin. Neurol. 110, 93–103 (2013)

    CrossRef  Google Scholar 

  7. Lemm, S., Blankertz, B., Dickhaus, T., Müller, K.R.: Introduction to machine learning for brain imaging. Neuroimage 56(2), 387–399 (2011)

    CrossRef  Google Scholar 

  8. Norvig, P., Russell, S.: Inteligência Artificial, vol. 1, 3 edn. Elsevier, Brasil (2014)

    Google Scholar 

  9. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  10. Prichard, G., Weiller, C., Fritsch, B., Reis, J.: Effects of different electrical brain stimulation protocols on subcomponents of motor skill learning. Brain Stimulation 7(4), 532–540 (2014)

    CrossRef  Google Scholar 

  11. Reis, J., et al.: Noninvasive cortical stimulation enhances motor skill acquisition over multiple days through an effect on consolidation. Proc. Nat. Acad. Sci. U.S.A. 106(5), 1590–1595 (2009)

    CrossRef  Google Scholar 

  12. Rossum, G.: Python reference manual. Technical report, Amsterdam, The Netherlands (1995)

    Google Scholar 

  13. Santos, M.R., et al.: Machine learning to estimate the amount of training to learn a motor skill. In: Duffy, V.G. (ed.) HCII 2019. LNCS, vol. 11581, pp. 198–209. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22216-1_15

    CrossRef  Google Scholar 

  14. Sato, A., Fujita, T., Ohashi, Y., Yamamoto, Y., Suzuki, K., Otsuki, K.: A prediction model for activities of daily living for stroke patients in a convalescent rehabilitation ward. J. Allied Health Sci. 7(1), 1–6 (2016)

    CrossRef  Google Scholar 

  15. Shinners, P.: Pygame. http://pygame.org/ (2011)

  16. Sonoda, S., Saitoh, E., Nagai, S., Okuyama, Y., Suzuki, T., Suzuki, M.: Stroke outcome prediction using reciprocal number of initial activities of daily living status. J. Stroke Cerebrovasc. Dis. 14(1), 8–11 (2005)

    CrossRef  Google Scholar 

  17. Tsuji, T., Liu, M., Sonoda, S., Domen, K., Chino, N.: The stroke impairment assessment set: its internal consistency and predictive validity. Arch. Phys. Med. Rehabil. 81(7), 863–868 (2000)

    CrossRef  Google Scholar 

Download references

Acknowledgment

The authors acknowledge FAPEMA for the financial support for this research specially for scholarship, Proc. UNIVERSAL-01294/16 and Proc. 2019/10012-2, Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paulo Rogério de Almeida Ribeiro .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

de Souza, E.D.F., dos Santos, M.R., da Silva, L.C.C., de Oliveira, A.C.M., Neto, A.d.A., Ribeiro, P.R.d.A. (2019). Motor Learning and Machine Learning: Predicting the Amount of Sessions to Learn the Tracing Task. In: Cota, V., Barone, D., Dias, D., Damázio, L. (eds) Computational Neuroscience. LAWCN 2019. Communications in Computer and Information Science, vol 1068. Springer, Cham. https://doi.org/10.1007/978-3-030-36636-0_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-36636-0_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36635-3

  • Online ISBN: 978-3-030-36636-0

  • eBook Packages: Computer ScienceComputer Science (R0)