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Analysis of Matrix Factorization Techniques for Extraction of Motion Motor Primitives

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XXVII Brazilian Congress on Biomedical Engineering (CBEB 2020)

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Abstract

Through the study and analysis of motor primitives, it is possible not only to perform motor control to aid impaired people, but also to develop techniques for neuromuscular rehabilitation. Human motor control is executed through a basic set of signals that govern the motor behavior. This basic set of signals is called primitive motor movements. From the knowledge of an individual’s motor primitives, it is possible to develop control strategies, which are capable of assisting individuals with some sort of mobility impairment. In order to extract these motor primitives in a precise way to carry out the development of assisting devices, factorization techniques are fundamental and many methods exist to perform this task. The present work analyzes the results yielded by four of the most used matrix factoring techniques (PCA, ICA, NNMF, SOBI) using electromyography signals. The results suggest that the PCA is the technique that best managed to reconstruct the EMG signals after their factorization, with a virtually zero relative error. The SOBI technique also yielded satisfactory results, followed by NNMF and finally ICA, which presented a reconstructed signal quite different from the original.

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References

  1. Turvey MT (1990) Coordination. Am Psychol 45:938

    Article  Google Scholar 

  2. Guigon E (2011) Models and architectures for motor control: simple or complex. Motor Control 20:478–502

    Google Scholar 

  3. Giszter SF (2015) Motor primitives new data and future questions. Curr Opin Neurobiol 33:156–165

    Article  Google Scholar 

  4. Overduin SA, dAvella A, Carmena JM, Bizzi E (2012) Microstimulation activates a handful of muscle synergies. Neuron 76:1071–1077

    Article  Google Scholar 

  5. Berger DJ, Gentner R, Edmunds T, Pai DK, d’Avella A (2013) Differences in adaptation rates after virtual surgeries provide direct evidence for modularity. J Neurosci 33:12384–12394

    Article  Google Scholar 

  6. Ruckert E, d’Avella A (2013) Learned parametrized dynamic movement primitives with shared synergies for controlling robotic and musculoskeletal systems. Front Comput Neurosci 7:138

    Article  Google Scholar 

  7. Degallier S, Ijspeert A (2010) Modeling discrete and rhythmic movements through motor primitives: a review. Biol Cybern 103:319–338

    Article  Google Scholar 

  8. Nunes PF, Nogueira SL, Siqueira AAG (2018) Analyzing motor primitives of healthy subjects wearing a lower limb exoskeleton, pp 1–6

    Google Scholar 

  9. Person K (1901) On lines and planes of closest fit to system of points in space. Philos Mag 2:559–572

    Article  Google Scholar 

  10. Tresch MC, Cheung VC, d’Avella A (2006) Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. J Neurophysiol 95:2199–2212

    Article  Google Scholar 

  11. Lambert-Shirzad N, Van der Loos HM (2016) On identifying kinematic and muscle synergies: a comparison of matrix factorization methods using experimental data from the healthy population. J Neurophysiol 117:290–302

    Article  Google Scholar 

  12. Ebied A, Kinney-Lang E, Spyrou L, Escudero J (2018) Evaluation of matrix factorisation approaches for muscle synergy extraction. Med Eng Phys 57:51–60

    Article  Google Scholar 

  13. Nunes PF, Santos WM, Siqueira AAG (2018) Influence of an exoskeleton on kinetic characteristics and muscles during the march using motion primitives, pp 1–7

    Google Scholar 

  14. Nunes PF, Santos WM, Siqueira AAG (2018) Control strategy based on kinetic motor primitives for lower limbs exoskeletons. IFAC-PapersOnLine 51:402–406

    Article  Google Scholar 

  15. Garate VR, Parri A, Yan T et al (2016) A novel bioinspired framework using motor primitives for locomotion assistance through a wearable cooperative exoskeleton. IEEE Robot Autom Mag 1070:83–95

    Article  Google Scholar 

  16. Ruiz Garate V, Parri A, Yan T et al (2016) Motor primitive-based control for lower-limb exoskeletons, pp 655–661

    Google Scholar 

  17. Saltiel P, Wyler-Duda K, D’Avella A, Tresch MC, Bizzi E (2001) Muscle synergies encoded within the spinal cord: evidence from focal intraspinal NMDA iontophoresis in the frog. J Neurophysiol 85:605–619

    Article  Google Scholar 

  18. d’Avella A, Saltiel P, Bizzi E (2003) Combinations of muscle synergies in the construction of a natural motor behavior. Nat Neurosci 6:300

    Article  Google Scholar 

  19. Hart CB, Giszter S (2013) Distinguishing synchronous and time-varying synergies using point process interval statistics: motor primitives in frog and rat. Front Comput Neurosci 7:52

    Article  Google Scholar 

  20. Roh J, Rymer WZ, Beer RF (2015) Evidence for altered upper extremity muscle synergies in chronic stroke survivors with mild and moderate impairment. Front Hum Neurosci 9:6

    Article  Google Scholar 

  21. Vigario R, Sarela J, Jousmaki V, Hamalainen M, Oja E (2000) Independent component approach to the analysis of EEG and MEG recordings. IEEE Trans Biomed Eng 47:589–593

    Article  Google Scholar 

  22. Liebermeister W (2002) Linear modes of gene expression determined by independent component analysis. Bioinformatics 18:51–60

    Article  Google Scholar 

  23. Levine E, Domany E (2001) Resampling method for unsupervised estimation of cluster validity. Neural Comput 13:2573–2593

    Article  Google Scholar 

  24. Delfosse N, Loubaton P (1995) Adaptive blind separation of independent sources: a deflation approach. Signal Process 45:59–83

    Article  Google Scholar 

  25. Hyvarinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9:1483–1492

    Article  Google Scholar 

  26. Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2:433–459

    Article  Google Scholar 

  27. An Q, Ishikawa Y, Nakagawa J et al (2013) Muscle synergy analysis of human standing-up motion with different chair heights and different motion speeds, pp 3579–3584

    Google Scholar 

  28. Steele KM, Tresch MC, Perreault EJ (2013) The number and choice of muscles impact the results of muscle synergy analyses. Front Comput Neurosci 7:105

    Article  Google Scholar 

  29. Belouchrani A, Abed-Meraim K, Cardoso JF, Moulines E (1997) A blind source separation technique using second-order statistics. IEEE Trans Signal Process 45:434–444

    Article  Google Scholar 

  30. Lambert-Shirzad N, Van der Loos HM (2017) Data sample size needed for analysis of kinematic and muscle synergies in healthy and stroke populations. In: 2017 International conference on rehabilitation robotics (ICORR). IEEE, pp 777–782

    Google Scholar 

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Acknowledgements

This work was supported by the Coordination for the Improvement of Higher Education Personnel (CAPES), Support Program for Graduate Studies and Scientific and Technological Research for Assistive Technology in Brazil (PGPTA), process no. 3457/2014, Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), process no. 2019/05937-7.

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Correspondence to P. F. Nunes .

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Nunes, P.F., Ostan, I., dos Santos, W.M., Siqueira, A.A.G. (2022). Analysis of Matrix Factorization Techniques for Extraction of Motion Motor Primitives. In: Bastos-Filho, T.F., de Oliveira Caldeira, E.M., Frizera-Neto, A. (eds) XXVII Brazilian Congress on Biomedical Engineering. CBEB 2020. IFMBE Proceedings, vol 83. Springer, Cham. https://doi.org/10.1007/978-3-030-70601-2_95

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  • DOI: https://doi.org/10.1007/978-3-030-70601-2_95

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