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Should We Look at Curvature or Velocity to Extract a Motor Program?

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Intertwining Graphonomics with Human Movements (IGS 2022)

Abstract

Experimental studies led by Lashley and Raibert in the early phase of human movement science highlighted the phenomenon of motor equivalence, according to which complex movements are represented in the brain abstractly, in a way that is independent of the effector used for the execution of the movement. This abstract representation is known as motor program and it defines the temporal sequence of target points the effector has to move towards to accomplish the desired movement. We present and compare two algorithms for the extraction of motor programs from handwriting samples. One algorithm considers that lognormal velocity profiles are an invariant characteristic of reaching movements and it identifies the position of the target points by analysing the velocity profile of samples. The other algorithm seeks target points by identifying the trajectory points corresponding to maximum curvature variations because experimental studies have shown that the activity of the primary motor cortex encodes the direction of the movement. We have compared the performance of the two algorithms in terms of the number of virtual target points extracted by handwriting samples generated by 32 subjects with their dominant and non-dominant hands. The results have shown that the two algorithms show a similar performance over \(\sim \)55% of samples but the extraction of motor programs by analysing the curvature variations is more robust to noise and unmodeled motor variability.

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References

  1. Alstermark, B., Isa, T.: Circuits for skilled reaching and grasping. Annu. Rev. Neurosci. 35, 559–578 (2012)

    Article  Google Scholar 

  2. Chen, S., Lach, J., Lo, B., Yang, G.Z.: Toward pervasive gait analysis with wearable sensors: a systematic review. IEEE J. Biomed. Health Inform. 20(6), 1521–1537 (2016)

    Article  Google Scholar 

  3. Cilia, N.D., De Gregorio, G., De Stefano, C., Fontanella, F., Marcelli, A., Parziale, A.: Diagnosing alzheimer’s disease from on-line handwriting: a novel dataset and performance benchmarking. Eng. Appl. Artif. Intell. 111, 104822 (2022). https://doi.org/10.1016/j.engappai.2022.104822

    Article  Google Scholar 

  4. De Stefano, C., Fontanella, F., Impedovo, D., Pirlo, G., di Freca, A.S.: Handwriting analysis to support neurodegenerative diseases diagnosis: a review. Pattern Recogn. Lett. 121, 37–45 (2019)

    Article  Google Scholar 

  5. De Stefano, C., Guadagno, G., Marcelli, A.: A saliency-based segmentation method for online cursive handwriting. Int. J. Pattern Recognit. Artif. Intell. 18(07), 1139–1156 (2004)

    Article  Google Scholar 

  6. Diaz, M., Ferrer, M.A., Parziale, A., Marcelli, A.: Recovering western on-line signatures from image-based specimens. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1204–1209. IEEE (2017)

    Google Scholar 

  7. Faundez-Zanuy, M., Fierrez, J., Ferrer, M.A., Diaz, M., Tolosana, R., Plamondon, R.: Handwriting biometrics: applications and future trends in e-security and e-health. Cogn. Comput. 12(5), 940–953 (2020). https://doi.org/10.1007/s12559-020-09755-z

    Article  Google Scholar 

  8. Ferrer, M.A., Diaz, M., Carmona-Duarte, C., Plamondon, R.: iDeLog: iterative dual spatial and kinematic extraction of sigma-lognormal parameters. IEEE Trans. Pattern Anal. Mach. Intell. 42(1), 114–125 (2018)

    Article  Google Scholar 

  9. Georgopoulos, A.P., Schwartz, A.B., Kettner, R.E.: Neuronal population coding of movement direction. Science 233(4771), 1416–1419 (1986)

    Article  Google Scholar 

  10. Gerth, S., et al.: Is handwriting performance affected by the writing surface? comparing preschoolers’, second graders’, and adults’ writing performance on a tablet vs. paper. Front. Psychol. 7, 1308 (2016)

    Article  Google Scholar 

  11. Herzfeld, D.J., Shadmehr, R.: Motor variability is not noise, but grist for the learning mill. Nat. Neurosci. 17(2), 149–150 (2014)

    Article  Google Scholar 

  12. Huang, J., Zhang, Z.: A novel sigma-lognormal parameter extractor for online signatures. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12823, pp. 459–473. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86334-0_30

    Chapter  Google Scholar 

  13. Liu, M., Guo, X., Wang, G.: Stroke parameters identification algorithm in handwriting movements analysis by synthesis. IEEE J. Biomed. Health Inform. 19(1), 317–324 (2014)

    Article  Google Scholar 

  14. Marcelli, A., Parziale, A., Senatore, R.: Some observations on handwriting from a motor learning perspective. In: AFHA, vol. 1022, pp. 6–10. Citeseer (2013)

    Google Scholar 

  15. Morasso, P.: Spatial control of arm movements. Exp. Brain Res. 42(2), 223–227 (1981). https://doi.org/10.1007/BF00236911

    Article  Google Scholar 

  16. O’Reilly, C., Plamondon, R.: Development of a sigma-lognormal representation for on-line signatures. Pattern Recogn. 42(12), 3324–3337 (2009)

    Article  MATH  Google Scholar 

  17. Parziale, A., Carmona-Duarte, C., Ferrer, M.A., Marcelli, A.: 2D vs 3D online writer identification: a comparative study. In: Lladós, J., Lopresti, D., Uchida, S. (eds.) ICDAR 2021. LNCS, vol. 12823, pp. 307–321. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86334-0_20

    Chapter  Google Scholar 

  18. Parziale, A., Diaz, M., Ferrer, M.A., Marcelli, A.: SM-DTW: stability modulated dynamic time warping for signature verification. Pattern Recogn. Lett. 121, 113–122 (2019)

    Article  Google Scholar 

  19. Parziale, A., Parisi, R., Marcelli, A.: Extracting the motor program of handwriting from its lognormal representation. In: The Lognormality Principle and its Applications in E-security, E-learning And E-health, pp. 289–308. World Scientific (2021)

    Google Scholar 

  20. Parziale, A., Senatore, R., Della Cioppa, A., Marcelli, A.: Cartesian genetic programming for diagnosis of Parkinson disease through handwriting analysis: performance vs. interpretability issues. Artif. Intell. Med. 111, 101984 (2021)

    Article  Google Scholar 

  21. Parziale, A., Senatore, R., Marcelli, A.: Exploring speed-accuracy tradeoff in reaching movements: a neurocomputational model. Neural Comput. Appl. 32(17), 13377–13403 (2020). https://doi.org/10.1007/s00521-019-04690-z

    Article  Google Scholar 

  22. Plamondon, R.: A kinematic theory of rapid human movements: part i. movement representation and generation. Biol. Cybern. 72(4), 295–307 (1995)

    Article  MATH  Google Scholar 

  23. Plamondon, R., Djioua, M.: A multi-level representation paradigm for handwriting stroke generation. Hum. Mov. Sci. 25(4–5), 586–607 (2006)

    Article  Google Scholar 

  24. Prakash, C., Kumar, R., Mittal, N.: Recent developments in human gait research: parameters, approaches, applications, machine learning techniques, datasets and challenges. Artif. Intell. Rev. 49(1), 1–40 (2018). https://doi.org/10.1007/s10462-016-9514-6

    Article  Google Scholar 

  25. Raibert, M.H.: Motor control and learning by the state space model. Ph.D. thesis, Massachusetts Institute of Technology (1977)

    Google Scholar 

  26. Reschechtko, S., Pruszynski, J.A.: Stretch reflexes. Curr. Biol. 30(18), R1025–R1030 (2020)

    Article  Google Scholar 

  27. Senatore, R., Marcelli, A.: A neural scheme for procedural motor learning of handwriting. In: 2012 International Conference on Frontiers in Handwriting Recognition, pp. 659–664. IEEE (2012)

    Google Scholar 

  28. Summers, J.J., Anson, J.G.: Current status of the motor program: revisited. Hum. Mov. Sci. 28(5), 566–577 (2009)

    Article  Google Scholar 

  29. Tucha, O., et al.: Kinematic analysis of dopaminergic effects on skilled handwriting movements in Parkinson’s disease. J. Neural Transm. 113(5), 609–623 (2006). https://doi.org/10.1007/s00702-005-0346-9

    Article  Google Scholar 

  30. Ünlü, A., Brause, R., Krakow, K.: Handwriting analysis for diagnosis and prognosis of Parkinson’s disease. In: Maglaveras, N., Chouvarda, I., Koutkias, V., Brause, R. (eds.) ISBMDA 2006. LNCS, vol. 4345, pp. 441–450. Springer, Heidelberg (2006). https://doi.org/10.1007/11946465_40

    Chapter  Google Scholar 

  31. Vahdat, S., Lungu, O., Cohen-Adad, J., Marchand-Pauvert, V., Benali, H., Doyon, J.: Simultaneous brain-cervical cord FMRI reveals intrinsic spinal cord plasticity during motor sequence learning. PLoS Biol. 13(6), e1002186 (2015)

    Article  Google Scholar 

  32. Van Gemmert, A.W., Teulings, H.L., Stelmach, G.E.: Parkinsonian patients reduce their stroke size with increased processing demands. Brain Cogn. 47(3), 504–512 (2001)

    Article  Google Scholar 

  33. Wang, C., Xiao, Y., Burdet, E., Gordon, J., Schweighofer, N.: The duration of reaching movement is longer than predicted by minimum variance. J. Neurophysiol. 116(5), 2342–2345 (2016)

    Article  Google Scholar 

  34. Weiler, J., Gribble, P.L., Pruszynski, J.A.: Spinal stretch reflexes support efficient hand control. Nat. Neurosci. 22(4), 529–533 (2019)

    Article  Google Scholar 

  35. Wing, A.M.: Motor control: mechanisms of motor equivalence in handwriting. Curr. Biol. 10(6), R245–R248 (2000)

    Article  Google Scholar 

  36. Wolpaw, J.R.: The education and re-education of the spinal cord. Prog. Brain Res. 157, 261–399 (2006)

    Article  Google Scholar 

  37. Wolpaw, J.R.: The negotiated equilibrium model of spinal cord function. J. Physiol. 596(16), 3469–3491 (2018)

    Article  Google Scholar 

  38. Wolpaw, J.R., Tennissen, A.M.: Activity-dependent spinal cord plasticity in health and disease. Annu. Rev. Neurosci. 24(1), 807–843 (2001)

    Article  Google Scholar 

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Parziale, A., Marcelli, A. (2022). Should We Look at Curvature or Velocity to Extract a Motor Program?. In: Carmona-Duarte, C., Diaz, M., Ferrer, M.A., Morales, A. (eds) Intertwining Graphonomics with Human Movements. IGS 2022. Lecture Notes in Computer Science, vol 13424. Springer, Cham. https://doi.org/10.1007/978-3-031-19745-1_15

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  • DOI: https://doi.org/10.1007/978-3-031-19745-1_15

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