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Lognormality: An Open Window on Neuromotor Control

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Graphonomics in Human Body Movement. Bridging Research and Practice from Motor Control to Handwriting Analysis and Recognition (IGS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14285))

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Abstract

This invited special session of IGS 2023 presents the works carried out at Laboratoire Scribens and some of its collaborating laboratories. It summarises the 17 talks presented in the colloquium #611 entitled « La lognormalité: une fenêtre ouverte sur le contrôle neuromoteur» (Lognormality: a window opened on neuromotor control), at the 2023 conference of the Association Francophone pour le Savoir (ACFAS) on May 10, 2023. These talks covered a wide range of subjects related to the Kinematic Theory, including key elements of the theory, some gesture analysis algorithms that have emerged from it, and its application to various fields, particularly in biomedical engineering and human-machine interaction.

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References

  1. Adler, C.H., et al.: Low clinical diagnostic accuracy of early vs advanced Parkinson disease: clinicopathologic study. Neurology 83, 406–412 (2014)

    Article  Google Scholar 

  2. Alamargot, D., Morin, M.-F.: Does handwriting on a tablet screen affect students’ graphomotor execution? A comparison between grades two and Nine. Hum. Mov. Sci. 44, 32–41 (2015). https://doi.org/10.1016/j.humov.2015.08.011

    Article  Google Scholar 

  3. Alamargot, D., Morin, M.-F.: Approche cognitive de la production écrite: Principaux résultats et apports pour l’apprentissage et l’enseignement à l’école. Approche Neuropsychologique des Apprentissages chez l’Enfant (ANAE) 163, 713721 (2019)

    Google Scholar 

  4. Alamargot, D., Chesnet, D., Dansac, C., Ros, C.: Eye and pen: a new device for studying reading during writing. Behav. Res. Methods 38(2), 287–299 (2006). https://doi.org/10.3758/BF03192780

  5. Alamargot, D., Morin, M.-F., Simard-Dupuis, É.: Handwriting in signing deaf middle-school students and relationship with text composition and spelling. Read. Writ. 31, 10171038 (2018). https://doi.org/10.1007/s11145-018-9824-y

    Article  Google Scholar 

  6. Alamargot, D., Morin, M.-F., Simard-Dupuis, É.: Handwriting delay in dyslexia: children at the end of primary school still make numerous short pauses when producing letters. J. Lean. Disabil. 53(3), 163–175 (2020). https://doi.org/10.1177/0022219420903705

    Article  Google Scholar 

  7. Arpi, E., Ferrari, F.: Preterm birth and behaviour problems in infants and preschool-age children: a review of the recent literature. Dev. Med. Child Neurol. 55, 788–796 (2013). https://doi.org/10.1111/dmcn.12142

    Article  Google Scholar 

  8. Asif, H., et al.: Objective structured assessment of technical skill (OSATS) in the surgical skills and technology elective program (SSTEP): comparison of peer and expert raters. Am. J. Surg. 223(2), 276–279 (2022). https://doi.org/10.1016/j.amjsurg.2021.03.064

    Article  Google Scholar 

  9. Awal, M., et al.: Design and optimization of ECG modeling for generating different cardiac dysrhythmias. Sensors 21(5), 1638 (2021). https://doi.org/10.3390/s21051638

    Article  Google Scholar 

  10. Barlow, K.M.: Postconcussion syndrome: a review. J. Child Neurol. 31, 57–67 (2016)

    Article  Google Scholar 

  11. Beery, K.E., Buktenica, N.A., Beery, N.A.: The Beery-Buktenica Developmental Test of Visual-Motor Integration: Administration, Scoring, and Teaching Manual, 6th edn. NCS Pearson, Minneapolis (2010)

    Google Scholar 

  12. Bensalah, A., Chen, J., Fornés, A., Carmona-Duarte, C., Lladós, J., Ferrer, M.Á.: Towards stroke patients’ upper-limb automatic motor assessment using smartwatches. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021, LNCS, vol. 12661, pp. 476–489. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68763-2_36

  13. Berio, D., Fol Leymarie, F., Asente, P., Echevarria, J.: StrokeStyles: stroke-based segmentation and stylization of fonts. ACM Trans. Graph. 41(3), 1–21 (2022). Article #28, https://doi.org/10.1145/3505246

  14. Berio, D., Akten, M., Fol Leymarie, F., Grierson, M., Plamondon, R.: Calligraphic stylisation learning with a physiologically plausible model of movement and recurrent neural networks. In: ACM Proceedings of 4th International Conference on Movement Computing (MOCO), London, UK (2017). https://doi.org/10.1145/3077981.3078049

  15. Berio, D., Calinon, S., Fol Leymarie, F.: Learning dynamic graffiti strokes with a compliant robot. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, South Korea, pp. 3981–3986 (2016).

    Google Scholar 

  16. Berio, D., Fol Leymarie, F., Plamondon, R.: Expressive curve editing with the sigma lognormal model. In: Diamanti, O., Vaxman. A. (eds.), Proceedings of Eurographics, Delft, The Netherlands, pp. 33–36 (2018)

    Google Scholar 

  17. Berio, D., Fol Leymarie, F., Plamondon, R.: Kinematics reconstruction of static calligraphic traces from curvilinear shape features. In: Plamondon, R., Marcelli, A., Ferrer, M.A. (eds.) The Lognormality Principle and its Applications in e-Security, e-Learning and e-Health. World Scientific Publishing, Series in Machine Perception and Artificial Intelligence (88), Ch. 11, pp. 237–268 (2021)

    Google Scholar 

  18. Bernard, C.O., Ponsford, J.A., McKinlay, A., McKenzie, D., Krieser, D.: Predictors of post-concussive symptoms in young children: injury versus non-injury related factors. J. Int. Neuropsychol. Soc. 22(8), 793–803 (2016)

    Article  Google Scholar 

  19. Bidias, S.P., David, J.-P., Savaria, Y., Plamondon, R.: On the use of interval arithmetic to bound delta-lognormal rapid human movements. In: Proceedings International Conference on pattern Recognition and Artificial Intelligence: Workshop on the Lognormality Principle and its Applications, Montréal, Canada, pp. 738–742 (2018)

    Google Scholar 

  20. Bhattacharya, U., Plamondon, R., Dutta Chowdhury, S., Goyal, P., Parui, S.K.: A sigma-lognormal model-based approach to generating large synthetic online handwriting samples databases. Int. J. Doc. Anal. Recogn. (IJDAR). 20(71), 1–17 (2017)

    Google Scholar 

  21. Boyle, E.M., et al.: Neonatal outcomes and delivery of care for infants born late preterm or moderately preterm: a prospective population-based study. Arch Dis Child Fetal Neonatal Ed 100, F479-485 (2015). https://doi.org/10.1136/archdischild-2014-307347

    Article  Google Scholar 

  22. Boyoguéno-Bidias, S.P.: Extracteur de paramètres Delta-Lognormaux globalement optimaux par séparation et évaluation exploitant l’arithmétique par intervalles : thèse doctorat, Polytechnique Montréal (2022)

    Google Scholar 

  23. Cancer, A., Minoliti, R., Crepaldi, M., Antonietti, A.: Identifying developmental motor difficulties: a review of tests to assess motor coordination in children. J. Funct. Morphol. Kinesiol. 5(1), 16 (2020). https://doi.org/10.3390/jfmk5010016

    Article  Google Scholar 

  24. Cardarilli, G.C., Di Nunzio, L., Fazzolari, R., Re, M., Silvestri, F.: Improvement of the cardiac oscillator based model for the simulation of bundle branch blocks. Appl. Sci. 9(18), 3653 (2019). Article 18. https://doi.org/10.3390/app9183653

  25. Carmona-Duarte, C., Ferrer, M.A., Plamondon, R., Gómez-Rodellar, A., Gómez-Vilda, P.: Sigma-lognormal Modeling of Speech. Cogn. Comput. 13(2), 488–503 (2021)

    Article  Google Scholar 

  26. Carmona-Duarte, C., Ferrer, M.A., Gómez-Vilda, P., Van Gemmert, A.W.A., Plamondon, R.: Evaluating Parkinson’s disease in voice and handwriting using the same methodology. In: The Lognormality Principle and its Applications in e-Security, e-Learning and e-Health, pp. 161–175 (2020). https://doi.org/10.1142/9789811226830_0007

  27. Carmona-Duarte, C., Gomez, P., Ferrer, M.A., Plamondon, R., Londral, A.: Study of several parameters for the detection of amyotrophic lateral sclerosis from articulatory movement. Loquens. 4(2), 038 (2017)

    Article  Google Scholar 

  28. Charles, M., Soppelsa, R., Albaret, J.M.: BHK échelle d’évaluation rapide de l’écriture chez l’enfant [BHK Concise Evaluation Scale for Children’s Handwriting]. ECPA (2004)

    Google Scholar 

  29. Chen, M., AlRegib, G., Juang, B.-H.: Air-writing recognition – part ii: detection and recognition of writing activity in continuous stream of motion data. IEEE Trans. Hum.-Mach. Syst. 46(3), 436–444 (2016)

    Article  Google Scholar 

  30. Cinnera, A.M., Morone, G.: Motor recovery in stroke rehabilitation supported by robot-assisted therapy. In: Assistive Technologies for Assessment and Recovery of Neurological Impairments, pp. 304–321 (2022). https://doi.org/10.4018/978-1-7998-7430-0.ch015

  31. Cioni, G., Inguaggiato, E., Sgandurra, G.: Early intervention in neurodevelopmental disorders: underlying neural mechanisms. Dev. Med. Child Neurol. 58(Suppl 4), 61–66 (2016). https://doi.org/10.1111/dmcn.13050

    Article  Google Scholar 

  32. Côté, J.N.: Adaptations to neck/shoulder fatigue and injuries. Adv. Exp. Med. Biol. 826, 205–228 (2014)

    Article  Google Scholar 

  33. Cserjesi, R., et al.: Functioning of 7-year-old children born at 32 to 35 weeks’ gestational age. Pediatrics Pediatrics 130(4), e838–846 (2012). https://doi.org/10.1542/peds.2011-2079

  34. Dankovičová, Z., Drotár, P., Gazda, J., Vokorokos, L.: Overview of the handwriting processing for clinical decision support system. In: IEEE 14th International Scientific Conference on Informatics, p. 6367 (2017). https://doi.org/10.1109/INFORMATICS.2017.8327223

  35. Danna, J., Longcamp, M., Nalborczyk, L. Velay, J-L., Commengé, C., Jover, M.: Interaction between orthographic and graphomotor constraints in learning to write. Learn. Instr. 80, 101622 (2022)

    Google Scholar 

  36. Delenclos, M., Jones, D.R., McLean, P.J., Uitti, R.J.: Biomarkers in Parkinson’s disease: advances and strategies. Parkinsonism Rel. Disord 22, S106–S110 (2016)

    Article  Google Scholar 

  37. Diaz, M., Fischer*, A., Ferrer, M.A., Plamondon, R.: Dynamic signature verification system based on one real signature. IEEE Trans. Cybern. 48(1), 228–239 (2018)

    Google Scholar 

  38. Diaz, M., Ferrer, M.A., Impedovo, D., Malik, M.I., Pirlo, G., Plamondon, R.: A perspective analysis of handwritten signature technology ACM Comput. Surv. 51(6), 39 (2019)

    Google Scholar 

  39. Diaz, M., Quintana, J.J., Ferrer, M.A., Carmona-Duarte, C., Wolniakowski A., Miatliuk, K.: Lognormality of velocity profiles in rapid robotic arm movements. In: 19th International Graphonomics Conference (IGS2019), Cancun, México, June 9–12 (2019)

    Google Scholar 

  40. Diaz, M., et al.: Studying the principle of lognormality in livestocks. In: Plamondon, R., Marcelli, A., Ferrer, M.A. (eds.) The Lognormality Principle and its applications in e-security, e-learning and e-health, World Scientific, Series in Machine Perception Artificial Intelligence, ISBN: 978-081-122-682-2, vol. 88, pp. 397–398 ( 2021). https://doi.org/10.1142/9789811226830

  41. Djioua, M., Plamondon, R.: A new algorithm and system for the characterization of handwriting strokes with delta-lognormal parameters. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 2060–2072 (2009)

    Google Scholar 

  42. Djioua, M., Plamondon, R.: A new algorithm and system for the extraction of delta-lognormal parameters. Technical Report, EPM-RT-2008-04, École Polytechnique de Montréal (2008)

    Google Scholar 

  43. Djioua, M., & Plamondon, R. (2008). A new methodology to improve myoelectric signal processing using handwriting. In: Proseedings International Conference on Frontiers in Handwriting Recognition, Montreal, ICFHR’2008, pp. 112–117

    Google Scholar 

  44. Douret, L., Auzias, M.: Le développement de l’organisation motrice et temporo-spatiale de l’écriture chez l’enfant. Approche Neuropsychologique des Apprentissages chez l’Enfant (ANAE) 5, 2935 (1993)

    Google Scholar 

  45. Duval, T., Rémi, C., Plamondon, R., Vaillant, J., O’Reilly, C.: Combining sigma-lognormal modeling and classical features for analyzing graphomotor performances in kindergarten children. Hum. Mov. Sci. 43, 183–200 (2015). https://doi.org/10.1016/j.humov.2015.04.005

    Article  Google Scholar 

  46. Duval, T, Rémi, C., Plamondon, R., O’Reilly, C.: On the use of the sigma-lognormal model to study children handwriting. In: 16th Biennial Conference of the Graphonomics Society (IGS 2013), Japan, pp. 26–29 (2013)

    Google Scholar 

  47. Eliasson, A.-C., Ro€sblad, B., Forssberg, H.: Disturbances in programming goal-directed arm movements in children with ADHD. Dev. Med. Child Neurol. 46(1), 19–27 (2004). https://doi.org/10.1111/j.1469-8749.2004.tb00429.x

  48. Faci, N., Désiré, N., Beauchamp, M.H., Gagnon, I., Plamondon, R.: Lognormality in children with mild traumatic brain injury: a preliminary pilot study. In: Proceedings of ICPRAI 2018. ICPRAI 2018 First International Workshop on the Lognormality Principle and its Applications, Montréal, Canada, pp. 790–794 (2018)

    Google Scholar 

  49. Faci, N., Boyogueno Bidias, S.P., Plamondon, R., Bergeron, N.: An interactive tablet-based system to run neuromuscular tests. In: Plamondon, R., Marcelli, A., Ferrer, M.A. (eds.) The Lognormality Principle and Its Applications in E-Security, e-Learning and e-Health, World Scientific Publishing, Series in Machine Perception and Artificial Intelligence, vol.88, pp 269–288 (2021). https://doi.org/10.1142/9789811226830_0012

  50. Faci, N., Désiré, N., Beauchamp, M.H., Gagnon, I., Plamondon, R.: Lognormality in children with mild traumatic brain injury: a preliminary pilot study. In Plamondon, R., Marcelli, A., Ferrer, M.A. (eds.) The Lognormality Principle and its Applications in e-Security, e-Learning and e-Health. World Scientific Publishing, Series in Machine Perception and Artificial Intelligence, vol. 88, pp. 143–160 (2021)

    Google Scholar 

  51. 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, 940–953 (2020)

    Article  Google Scholar 

  52. Fawaz, H.I., Forestier, G., Weber, J., Idoumghar, L., Muller, P.-A.: Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks. Int. J. Comput. Assist. Radiol. Surg. 14(9), 1611–1617 (2019). https://doi.org/10.1007/s11548-019-02039-4

    Article  Google Scholar 

  53. Feldman, J., Singh, M.: Information along contours and object boundaries. Psychol. Rev. 112(1), 243–252 (2005)

    Article  Google Scholar 

  54. 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. (PAMI). 42(1): 114–125 (2020)

    Google Scholar 

  55. Ferrer, M., et al.: From operculum and body tail movements to different coupling of physical activity and respiratory frequency in farmed gilthead sea bream and European sea bass. Insights on aquaculture biosensing. Comput. Electron. Agric. 175(105531), 1–9 (2020). ISSN 0168–1699

    Google Scholar 

  56. Ferrer, M., Diaz, M., Quintana-Hernandez, J., Carmona-Duarte, C., Plamondon, R.: A multilognormal analysis of 3D human movements. IEEE Trans. Pattern Anal. Mach. Intell. 13, March 2023

    Google Scholar 

  57. Fahr, A., Keller, J.W., Balzer, J., Lieber, J., van Hedel, H.J.A.: Quantifying age-related differences in selective voluntary motor control in children and adolescents with three assessments. Hum. Mov. Sci. 77, 102790 (2021). https://doi.org/10.1016/j.humov.2021.102790

    Article  Google Scholar 

  58. Fischer, A., Plamondon, R.: Signature verification based on the kinematic theory of rapid human movements. IEEE Trans. Hum. Mach. Syst. 47(2), 169–180 (2017)

    Article  Google Scholar 

  59. Fischer, A., Schindler, R., Bouillon, M., Plamondon, R.: Modeling 3d movements with the kinematic theory of rapid human movements. In: Plamondon, R., Marcelli, A., Ferrer, M. (eds.), The Lognormality Principle and its Applications in e-Security, e-Learning and e-Health, chapter 15, pp. 327–342. World Scientific (2021)

    Google Scholar 

  60. Fong, W.C.: Why Chinese painting is history. Art Bull. 85(2), 258–280 (2003)

    Article  Google Scholar 

  61. Freedberg, D., Gallese, V.: Motion, emotion and empathy in esthetic experience. Trends Cogn. Sci. 11(5), 197–203 (2007)

    Article  Google Scholar 

  62. Freyd, J.J.: Representing the dynamics of a static form. Mem. Cognit. 11(4), 342–346 (1983)

    Article  Google Scholar 

  63. Funke, I., Mees, S.T., Weitz, J., Speidel, S.: Video-based surgical skill assessment using 3D convolutional neural networks. Int. J. Comput. Assist. Radiol. Surg. 14(7), 1217–1225 (2019). https://doi.org/10.1007/s11548-019-01995-1

    Article  Google Scholar 

  64. Goldbraikh, A., D’Angelo, A.-L., Pugh, C.M., Laufer, S.: Video-based fully automatic assessment of open surgery suturing skills. Int. J. Comput. Assist. Radiol. Surg. 17(3), 437–448 (2022). https://doi.org/10.1007/s11548-022-02559-6

    Article  Google Scholar 

  65. Goubault, E., Martinez, R., Bouffard, J., Dowling-Medley, J., Begon, M., Dal Maso, F.: Shoulder electromyography-based indicators to assess manifestation of muscle fatigue during laboratory-simulated manual handling task. Ergonomics 65(1), 118–133 (2022)

    Article  Google Scholar 

  66. Goubault, E., Verdugo, F., Pelletier, J., Traube, C., Begon, M., Dal Maso, F.: Exhausting repetitive piano tasks lead to local forearm manifestation of muscle fatigue and negatively affect musical parameters. Sci. Rep. 11(1), 8117 (2021)

    Article  Google Scholar 

  67. Guerfali, W., Plamondon, R.: Signal processing for the parameter extraction of the delta lognormal model. In: Research in Computer and Robot Vision, pp. 217–232, World Scientific 1995

    Google Scholar 

  68. Hajian-Tilaki, K.: Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J. Intern. Med. 4(2), 627–635 (2013)

    Google Scholar 

  69. Hamstra-Bletz, L., Blöte, A.W.: Development of handwriting in primary school: a longitudinal study. Percept. Mot. Skills 70(3), 759770 (1990)

    Article  Google Scholar 

  70. Hansen, E., Walster, G.W.: Global Optimization Using Interval Analysis: Revised and Expanded. CRC Press, Boca Raton (2003)

    Google Scholar 

  71. Heaton, J.: An empirical analysis of feature engineering for predictive modeling. SoutheastCon 2016, 1–6 (2016). https://doi.org/10.1109/SECON.2016.7506650

    Article  Google Scholar 

  72. Hira, S., et al.: Video-based assessment of intraoperative surgical skill. Int. J. Comput. Assist. Radiol. Surg. 17(10), 1801–1811 (2022). https://doi.org/10.1007/s11548-022-02681-5

    Article  Google Scholar 

  73. Hong, M., Perlmutter, J.S., Earhart, G.M.: A kinematic and electromyographic analysis of turning in people with parkinson disease. Neurorehabil. Neural Repair 23(2), 166–176 (2009). https://doi.org/10.1177/1545968308320639

    Article  Google Scholar 

  74. Hughes, A.J., Redsell, S.A., Glazebrook, C.: Motor development interventions for preterm infants: a systematic review and meta-analysis. Pediatrics 138, (2016). https://doi.org/10.1542/peds.2016-0147

  75. Kaiser, M.L., Schoemaker, M.M., Albaret, J.M., Geuze, R.H.: What is the evidence of impaired motor skills and motor control among children with attention deficit hyperactivity disorder (ADHD)? systematic review of the literature. Res. Dev. Disabil. 36C, 338–357 (2015). https://doi.org/10.1016/j.ridd.2014.09.023

    Article  Google Scholar 

  76. Kerstjens, J.M., et al.: Developmental delay in moderately preterm-born children at school entry. J. Pediatr. 159, 92–98 (2011). https://doi.org/10.1016/j.jpeds.2010.12.041

    Article  Google Scholar 

  77. Kieves, N.R.: Objective gait analysis: review and clinical applications. Vet. Clin. North Am. Small Anim. Pract. 52(4), 857–867 (2022). https://doi.org/10.1016/j.cvsm.2022.03.009

    Article  Google Scholar 

  78. Korkman, M., Kirk, U., Kemp, S.: NEPSY-II : Bilan neuropsychologique de l’enfant. Seconde édition. Pearson Canada Assessment, Toronto (2012)

    Google Scholar 

  79. Laniel, P., Faci, N., Plamondon, R., Beauchamp, M.H., Gauthier, B.: Kinematic analysis of fast pen strokes in children with ADHD. Appl. Neuropsychol. Child 9, 125–140 (2020). https://doi.org/10.1080/21622965.2018.1550402

    Article  Google Scholar 

  80. Laurent, A., Plamondon, R., Begon, M.: Central and peripheral shoulder fatigue pre-screening using the sigma-lognormal model: a proof of concept. Front. Hum. Neurosci. 14, 171 (2020)

    Article  Google Scholar 

  81. Laurent, A., Plamondon, R., Begon, M.: Reliability of the kinematic theory parameters during handwriting tasks on a vertical setup. Biomed. Sig. Process. Control 71, 103157 (2022)

    Article  Google Scholar 

  82. Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: Eegnet: A compact convolutional network for EEG-based brain-computer interfaces (2016). CoRR, abs/1611.08024. http://arxiv.org/abs/1611.08024. arXiv:1611.08024

  83. Lebel, K., Nguyen, H., Duval, C., Plamondon, R., Boissy, P.: Capturing the cranio-caudal signature of a turn with inertial measurement systems: methods, parameters robustness and reliability. Front. Bioeng. Biotech. 5, 51 (2017). https://doi.org/10.3389/fbioe.2017.00051

    Article  Google Scholar 

  84. Lebel, K., Duval, C., Nguyen, H., Plamondon, R., Boissy, P.: Turn cranio-caudal signature assessment from inertial systems for mobility deficit identification in Parkinson’s disease patients. Parkinsonism Relat. Disord. 46, E24–E25 (2018)

    Google Scholar 

  85. Lebel, K., Duval, C., Nguyen, H.P., Plamondon, R., Boissy, P.: Cranio-caudal kinematic turn signature assessed with inertial systems as a marker of mobility deficits in Parkinson’s disease. Front. Neurol. 9, 22 (2018). https://doi.org/10.3389/fneur.2018.00022

    Article  Google Scholar 

  86. Lebel, K., Duval, C., Plamondon, R., Faci, N., Boissy, P.: Sigma-Lognormal modelling to assess mobility deficits: the case of turn signature assessed with inertial measurement units (IMUs). In: Plamondon, R., Marcelli, A., Ferrer, M.Á., (eds.) The Lognormality Principle and its Applications in e-Security, e-Learning and e-Health. World Scientific Publishing Series in Machine Perception and Artificial Intelligence, vol. 88, pp. 177–193 (2020). https://doi.org/10.1142/9789811226830_0008

  87. Leiva, L.A., Martín-Albo, D., Plamondon, R., Vatavu, R.-D.: KeyTime: super-accurate prediction of stroke gesture production times. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, vol. 2018 (2018)

    Google Scholar 

  88. Leiva, L., Diaz, M., Ferrer, M.A., Plamondon, R.: Human or machine? it is not what you write, but how you write It. In: Proceedings of ICPR 2020. 25th International Conference on Pattern Recognition, ICPR 2020, Milan, Italy, pp. 2612–2619 (2021)

    Google Scholar 

  89. Leiva, L., Vatavu, R.D., Martin Albo, D., Plamondon, R.: Omnis praedictio: estimating the full spectrum of human performance with stroke gestures. Int. J. Hum. Comput. Stud. 142, 102466 (2020)

    Article  Google Scholar 

  90. Leiva, L., Martin Albo, D, Plamondon, R., Vidal, E.: Gestures à go go: authoring synthetic human-like gestures using the kinematic theory of rapid movements. In: Proceedings of CHI 2015. CHI 2015, Seoul, Korea, pp.1–10 (2015)

    Google Scholar 

  91. Levien, R.: From spiral to spline: optimal techniques in interactive curve design, Ph.D. thesis, EECS Department, University of California, Berkeley (2009)

    Google Scholar 

  92. Leyton, M.: Symmetry-curvature duality. Comput. Vis. Graph. Image Process. 38(3), 327–341 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  93. Longcamp, M., Anton, J.L., Roth, M., Velay, J.L.: Visual presentation of single letters activates a pre-motor area involved in writing. Neuroimage 19(4), 1492–1500 (2003)

    Article  Google Scholar 

  94. Louis, D., et al.: School readiness among children born preterm in Manitoba. Canada. JAMA Pediatr 176, 1010–1019 (2022). https://doi.org/10.1001/jamapediatrics.2022.2758

    Article  Google Scholar 

  95. Mancini, M., et al.: Continuous monitoring of turning in Parkinson’s disease: rehabilitation potential. NeuroRehabilitation 37(1), 3 (2015). https://doi.org/10.3233/NRE-151236

    Article  Google Scholar 

  96. Martin-Albo, D., Leiva, L.A., Huang, J., Plamondon, R.: Strokes of insight: user intent detection and kinematic compression of mouse cursor trails. Inf. Process. Manage. 52(6), 989–1003 (2016)

    Article  Google Scholar 

  97. Miatliuk, K., Wolniakowski, A., Diaz, M., Ferrer, M.A.: Universal robot employment to mimic human writing. In: 20th International Carpathian Control Conference, ISBN: 978-1-7281-0702-8, pp. 1–5, 26–29 May 2019. https://doi.org/10.1109/CarpathianCC.2019.8766027, Kraków-Wieliczka, Poland

  98. Nadeau, A., et al.: A 12-week cycling training regimen improves upper limb functions in people with Parkinson’s disease. Front. Hum. Neurosci. 12(351), 1–10 (2018). https://doi.org/10.3389/fnhum.2018.00351

  99. Mediavilla, C.: Calligraphy: From Calligraphy to Abstract Painting. Scirpus Publications, Wommelgem (1996)

    Google Scholar 

  100. Michaud, B., Bailly, F., Charbonneau, E., Ceglia, A., Sanchez, L., Begon, M.: Bioptim, a python framework for musculoskeletal optimal control in biomechanics. IEEE Trans. Syst. Man Cybern. 53(1), 321–332 (2023)

    Article  Google Scholar 

  101. Moore, R.E.: Interval analysis. Prentice-Hall, Englewood Cliffs (1966)

    MATH  Google Scholar 

  102. Moran, L.M., et al.: Quality of life in pediatric mild traumatic brain injury and its relationship to postconcussive symptoms. J. Pediatr. Psychol. 37(7), 736–744 (2011)

    Article  Google Scholar 

  103. Morin, M.-F., Bara, F., Alamargot, D.: Apprentissage de la graphomotricité à l’école : Quelles acquisitions? Quelles pratiques? Quels outils? Scientia Paedagogica Experimentalis, LIV 1–2, 4782 (2017)

    Google Scholar 

  104. Müller, M., Röder, T., Clausen, M., Eberhardt, B., Krüger, B., Weber, A.: Documentation mocap database HDM05, Technical Report, CG-2007-2, Universität Bonn (2007)

    Google Scholar 

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

    Article  MATH  Google Scholar 

  106. O’Reilly, C., Plamondon, R.: Prototype-based methodology for the statistical analysis of local features in stereotypical handwriting tasks. In: International Conference on Pattern Recognition, IEEE, Istanbul, Turkey, pp.1864–1867 (2010)

    Google Scholar 

  107. O’Reilly, C., Plamondon, R., Landou, M.K., Stemmer, B.: Using kinematic analysis of movement to predict the time occurrence of an evoked potential associated with a motor command. Eur. J. Neurosci. 37(2), 173–180 (2013). https://doi.org/10.1111/ejn.12039

    Article  Google Scholar 

  108. O’Reilly, C., Plamondon, R.: A globally optimal estimator for the delta-lognormal modeling of fast reaching movements. IEEE Trans. Syst. Man Cybern. Part B (Cybernetics) 42, 1428–1442 (2012). https://doi.org/10.1109/TSMCB.2012.2192109

  109. O’Reilly, C., Oruganti, S.D.R., Tilwani, D., Bradshaw, J.: Model-driven analysis of ECG using reinforcement learning. Bioengineering 10(6), 696 (2023)

    Article  Google Scholar 

  110. Oğul, B.B., Gilgien, M., Özdemir, S.: Ranking surgical skills using an attention-enhanced siamese network with piecewise aggregated kinematic data. Int. J. Comput. Assist. Radiol. Surg. 17(6), 1039–1048 (2022). https://doi.org/10.1007/s11548-022-02581-8

    Article  Google Scholar 

  111. Pan, Z., Talwar, S., Plamondon, R., Van Gemmert, A.W.: Characteristics of bi-directional unimanual and bimanual drawing movements: The application of the delta-lognormal models and sigma-lognormal model. Pattern Recogn. Lett. 121, 97–103 (2019)

    Article  Google Scholar 

  112. Paz-Villagrán, V., Danna, J., Velay, J.-L.: Lifts and stops in proficient and dysgraphic handwriting. Hum. Movement Sci. 33, 381–394 (2014). https://doi.org/10.1016/j.humov.2013.11.005

    Article  Google Scholar 

  113. Pfeiffer, B., Moskowitz, B., Paoletti, A., Brusilovskiy, E., Zylstra, S.E., Murray, T.: Developmental test of visual-motor integration (VMI): an effective outcome measure for handwriting interventions for kindergarten, first-grade, and second-grade students? Am. J. Occup. Ther. 69(4), 1–7 (2015). https://doi.org/10.5014/ajot.2015.015826

    Article  Google Scholar 

  114. Pignocchi, A.: How the intentions of the draftsman shape perception of a drawing. Conscious. Cogn. 19(4), 887–898 (2010)

    Article  Google Scholar 

  115. Plamondon, R., Djioua, M., O’Reilly, C.: La Théorie cinématique des mouvements humains rapides : développements récents. Traitement du Signal, Numéro Spécial : Le Document Écrit. 26(5), 377–394 (2009)

    Google Scholar 

  116. Plamondon, R., O’Reilly, C., Rémi, C., Duval, T.: The lognormal handwriter: learning, performing and declining. Front. Psychol. Cogn. Sci. 2013, 1–14 (2013)

    Google Scholar 

  117. 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 

  118. Plamondon, R.: A kinematic theory of rapid human movements. Part I. movement representation and generation. Biol. Cybern. 72(4), 295–307 (1995a). https://doi.org/10.1007/BF00202785

  119. Plamondon, R.: A kinematic theory of rapid human movements: part II: movement time and control. Biol. Cybern. 72(4), 309–320 (1995). https://doi.org/10.1007/BF00202786

    Article  MATH  Google Scholar 

  120. Plamondon, R.: A kinematic theory of rapid human movements: Part III: kinetic outcomes. Biol. Cybern. 78, 133–145 (1998)

    Article  MATH  Google Scholar 

  121. Plamondon, R.: The lognormality principle: a personalized survey. In: Plamondon, R., Marcelli, A., Ferrer, M.A. (eds.), The Lognormality Principle and its applications in e-security, e-learning and e-health, World Scientific Publishing, Series in Machine Perception and Artificial Intelligence, vol. 88, pp 1–39 (2021)

    Google Scholar 

  122. Plamondon, R., Guerfali, W.: The generation of handwriting with delta-lognormal synergies. Biol. Cybern. 78(2), 119–132 (1998). https://doi.org/10.1007/s004220050419

    Article  MATH  Google Scholar 

  123. Plamondon, R., Feng, C., Djioua, M.: The convergence of a neuromuscular impulse response towards a lognormal, from theory to practice. (Technical Report EPM-RT-2008–08) (2008)

    Google Scholar 

  124. Plamondon, R.: A kinematic theory of rapid human movements part V. Movement representation and generation. Biol. Cybern. 72, 295–307 (1995)

    Article  MATH  Google Scholar 

  125. Plamondon, R., Feng, C., Woch, A.: A kinematic theory of rapid human movements: part IV: a formal mathematical proof and new insights. Biol. Cybern. 89, 126–138 (2003)

    Article  MATH  Google Scholar 

  126. Plamondon, R., Li, X., Djioua, M.: Extraction of delta-lognormal parameters from handwriting strokes. J. Front. Comput. Sci. China 1(1), 106–113 (2007)

    Article  Google Scholar 

  127. Plamondon, R., Marcelli, A., Ferrer, M.A. (eds.) The lognormality principle and its applications in e-security, e-learning and e-health. World Sci. Publ. Ser. Mach. Percept. Artif. Intell. 88, 415 (2021)

    Google Scholar 

  128. Plamondon, R., O’Reilly, C., Galbally, J., Almaksour, A., Anquetil, E.: Recent developments in the study of rapid human movements with the Kinematic theory: applications to handwriting and signature synthesis. Pattern Recogn. Lett. 35(1), 225–235 (2014). https://doi.org/10.1016/j.patrec.2012.06.004

    Article  Google Scholar 

  129. Plamondon, R., O’Reilly, C., Ouellet-Plamondon, C.: Strokes against stroke—strokes for strides. Pattern Recogn. 47(3), 929–944 (2014). https://doi.org/10.1016/j.patcog.2013.05.004

    Article  Google Scholar 

  130. Plamondon, R., Pirlo, G., Anquetil, E., Rémi, C., Teuling, H-L., Nakagawa, M.: Personal digital bodyguards for e-security, e-learning and e-health: a prospective survey. Pattern Recogn. 81, 633–659 (2018). https://doi.org/10.1016/j.patcog.2018.04.012

  131. Pontart, V., Bidet-Ildei, C., Lambert, E., Morisset, P., Flouret, L., Alamargot, D.: Influence of handwriting skills during spelling in primary and lower secondary grades. Front. Psychol. 4, 19 (2013). https://doi.org/10.3389/fpsyg.2013.00818

    Article  Google Scholar 

  132. Provincial council for maternal and child health. Final report of the neonatal follow-up clinics. vol. 47 (2015)

    Google Scholar 

  133. Quintana, J.J., Ferrer, M.A., Diaz, M., Feo, J.J., Wolniakowski, A., Miatliuk, K.: Uniform vs. lognormal kinematics in robots: perceptual preferences for robotic movements. Appl. Sci. 12(23), 12045 (2022). ISSN: 2523–3963

    Google Scholar 

  134. Quiroz-Juárez, M.A., Jiménez-Ramírez, O., Vázquez-Medina, R., Breña-Medina, V., Aragón, J.L., Barrio, R.A.: Generation of ECG signals from a reaction-diffusion model spatially discretized. Sci. Rep. 9(1), 19000 (2019). https://doi.org/10.1038/s41598-019-55448-5

    Article  Google Scholar 

  135. Raju, T.N.: Developmental physiology of late and moderate prematurity. Semin. Fetal Neonatal. Med. 17, 126–131 (2012). https://doi.org/10.1016/j.siny.2012.01.010

    Article  Google Scholar 

  136. Rémi, C., Nagau, J., Vaillant, J., Dorville, A., Plamondon, R.: Multimodal acquisition and analysis of children handwriting for the study of the efficiency of their handwriting movements: the @MaGma challenge. In: 16th International Conference on Frontiers in Handwriting Recognition, pp.459–464. Niagara Falls, United States (2018)

    Google Scholar 

  137. Rémi, C., Vaillant, J., Plamondon, R., Prévost, L., Duval, T.: Exploring the kinematic dimensions of kindergarten children’s scribbles. In: 17th Biennial Conference of the International Graphonomics Society, 79–82. Guadeloupe (2015)

    Google Scholar 

  138. Rémi, C., Nagau, J., Vaillant, J., Plamondon, R.: Preliminary study of t0, a sigma-lognormal parameter extracted from young childrens controlled scribbles. In: Proceedings of the 18th International Graphonomics Society Conference, Italy, pp.93–97 (2017)

    Google Scholar 

  139. Rémi, C., Nagau, J., Vaillant, J., Plamondon, R.: Could sigma-lognormal modeling help teachers to characterize the kinematic efficiency of pupils’ cursive procedures of handwriting? In: Plamondon, R., Marcelli, A., Ferrer, M.A. (eds.) The Lognormality Principle and its Applications in e-Security, e-Learning, and e-Health, pp. 87–116. World Scientific Publishing Company, Series in Machine Perception and Artificial Intelligence, vol. 88 (2021). https://doi.org/10.1142/9789811226830_0004

  140. Rémi, C., Nagau, J., Copilotrace: a platform to process graphomotor tasks for education and graphonomics research. In: Carmona-Duarte, C., Diaz, M., Angel Ferrer, M., Morales, A. (eds.) 20th International Conference of the International Graphonomics Society 2022, LNCS, vol. 13424, pp. 129–143. Springer, Heidelberg (2022), https://doi.org/10.1007/978-3-031-19745-1_10

  141. Rocca, W.A.: The burden of Parkinson’s disease: a worldwide perspective [Comment]. Lancet Neurol. 17, 928–929 (2018)

    Article  Google Scholar 

  142. Rosell-Moll, E., et al.: Use of accelerometer technology for individual tracking of activity patterns, metabolic rates and welfare in farmed gilthead sea bream (Sparus aurata) facing a wide range of stressors. In: Aquaculture, ISSN 0044–8486, vol. 539, pp. 1–10, 30 (2021)

    Google Scholar 

  143. Rosenblum, S., Epsztein, L., Josman, N.: Handwriting performance of children with attention deficit hyperactive disorders: a pilot study. Phys. Occup. Ther. Pediatr. 28(3), 219–234 (2008). https://doi.org/10.1080/01942630802224934

    Article  Google Scholar 

  144. Schatz P. (2011) Finger Tapping Test. In: Kreutzer J.S., DeLuca J., Caplan B. (eds) Encyclopedia of Clinical Neuropsychology. Springer, New York, NY, 1050–1051

    Google Scholar 

  145. Schindler, R., Bouillon, M., Plamondon, R., Fischer*, A.: Extending the sigma-lognormal model of the kinematic theory to three dimensions. In: Proceedings of ICPRAI 2018. ICPRAI 2018 First International Workshop on the Lognormality Principle and its Applications, Montréal, Canada, 748–752 (2018)

    Google Scholar 

  146. Shah, P., Kaciroti, N., Richards, B., Oh, W., Lumeng, J.C.: Developmental outcomes of late preterm infants from infancy to kindergarten. Pediatrics 138 (2016). https://doi.org/10.1542/peds.2015-3496

  147. Sica, M., et al.: Continuous home monitoring of Parkinson’s disease using inertial sensors: a systematic review. PLoS ONE 16(2), e0246528 (2021). https://doi.org/10.1371/journal.pone.0246528

    Article  Google Scholar 

  148. Smits, E.J., et al.: Standardized handwriting to assess bradykinesia, micrographia and tremor in Parkinson’s disease. PLoS ONE 9(5), e97614 (2014)

    Article  Google Scholar 

  149. Soangra, R., Sivakumar, R., Anirudh, E.R., Reddy Y.S.V., John, E.B.: Evaluation of surgical skill using machine learning with optimal wearable sensor locations. PLOS ONE 17(6) (2022). https://doi.org/10.1371/journal.pone.0267936

  150. Steinhart, S., Weiss, P.L., Friedman, J.: Proximal and distal movement patterns during a graphomotor task in typically developing children and children with handwriting problems. J. Neuroeng. Rehabil. 18, 178 (2021). https://doi.org/10.1186/s12984-021-00970-9

    Article  Google Scholar 

  151. Tiffin, J.: Purdue Pegboard Examiner Manual. Science Research Associates, Chicago (1968)

    Google Scholar 

  152. Todorov, E.: Optimality principles in sensorimotor control. Nat. Neurosci. 7(9), 907–915 (2004)

    Article  Google Scholar 

  153. Ungurean O.-C., Vatavu R.-D., Leiva L.A., Plamondon, R.: Gesture input for users with motor impairments on touchscreens: empirical results based on the kinematic theory. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (2018)

    Google Scholar 

  154. Vaivre-Douret, L., Paquet, A.: L’évaluation standardisée développementale des fonctions neuro-psychomotrices: présentation de la batterie normée française NP-MOT. Neurophysiol. Clin. 49(6), 421 (2019)

    Article  Google Scholar 

  155. Vohr, B.: Long-term outcomes of moderately preterm, late preterm, and early term infants. Clin. Perinatol. 40, 739–751 (2013). https://doi.org/10.1016/j.clp.2013.07.006

    Article  Google Scholar 

  156. Walha, R., et al.: The accuracy and precision of gait spatio-temporal parameters extracted from an instrumented sock during treadmill and overground walking in healthy subjects and patients with a foot impairment secondary to psoriatic arthritis. Sensors 21(18), 6179 (2021). https://doi.org/10.3390/s21186179

    Article  Google Scholar 

  157. Wechsler, D.: WISC-IV: Échelle d’intelligence de Wechsler pour enfants [Wechsler Intelligence Scale for Children, 4th ed. (WISC-IV)]. Toronto, ON: PsychCorp (2004)

    Google Scholar 

  158. Woch, A., Plamondon, R., O’Reilly, C.: Kinematic characteristics of bidirectional delta-lognormal primitives in young and older subjects. Hum. Mov. Sci. 30(1), 1–17 (2011). https://doi.org/10.1016/j.humov.2009.10.006

    Article  Google Scholar 

  159. Wolniakowski, A., Quintana, J.J., Diaz, M., Miatliuk, K., Ferrer, M.A.: Towards human-like kinematics in industrial robotic arms: a case study on a UR3 robot. In: Procceding of the 54th International Carnahan Conference on Security Technology (ICCST), University of Hertfordshire, Hatfield, UK, pp. 1–5, 1–3, September 2022. https://doi.org/10.1109/ICCST49569.2021.9717393

  160. World Health Organization. Neurological disorders: public health challenges: World Health Organization (2006)

    Google Scholar 

  161. Xia, L., Chen, C. Aggarwal, J.: View invariant human action recognition using histograms of 3d joints. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 20–27 (2012)

    Google Scholar 

  162. Yan, J.H., Thomas, J.R.: Arm movement control: differences between children with and without attention deficit hyperactivity disorder. Res. Q. Exerc. Sport 73(1), 10–18 (2002). https://doi.org/10.1080/02701367.2002.10608987

    Article  Google Scholar 

  163. Zemek, R., Osmond, M.H., Barrowman, N., on behalf of PERC Concussion Team.: In: Predicting and preventing postconcussive problems in paediatrics (5P) study: protocol for a prospective multicentre clinical prediction rule derivation study in children with concussion. BMJ Open 2013(3), e003550 (2013). https://doi.org/10.1136/bmjopen-2013-003550

  164. Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., Eickhoff, C.: A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2114–2124 (2021). https://doi.org/10.1145/3447548.3467401

  165. Zhou, H., Hu, H.: Human motion tracking for rehabilitation—a survey. Biomed. Sig. Proc. Control 3(1), 1–18 (2008). Article 1. WorldCat.org. https://doi.org/10.1016/j.bspc.2007.09.001

  166. Zia, A., Sharma, Y., Bettadapura, V., Sarin, E.L., Essa, I.: Video and accelerometer-based motion analysis for automated surgical skills assessment. Int. J. Comput. Assist. Radiol. Surg. 13(3), 443–455 (2018). https://doi.org/10.1007/s11548-018-1704-z

    Article  Google Scholar 

  167. Sady, M., Vaughan, C.G., Gioia, G.A.: Psychometric characteristics of the postconcussion symptom inventory in children and adolescents. Arch Clin. Neuropsychol. 29(4), 348–63 (2014). https://doi.org/10.1093/arclin/acu014. Epub 2014 Apr 15

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Acknowledgements

The ACFAS colloquium was financially supported by the Département de Génie Électrique, Polytechnique Montréal and Institut TransMedTech. The authors thank the team members involved in their specific project as reported in each section:

Section 3.1: Alicia Fornès (Universitat Autònoma de Barcelona, UAB), Asma Bensalah (UAB), Maria Cristina Carmona Duarte (Universidad de Las Palmas de Gran Canaria, ULPGC), Jialuo Chen Tormos (UAB), Miguel Angel Ferrer Ballester (ULPGC), Andreas Fischer (University of Applied Sciences and Arts, Western Switzerland, HES-SO-UASAW), Josep Lladóos (UAB), Cristina Martín (Guttman Institute, Neurorehabilitation Institute, Badalona, Spain, GINI), Eloy Opisso (GINI), Anna Scius-Bertrand ( HES-SO UASAW), Réjean Plamondon (Polytechnique Montréal), Josep Maria Tormos (GINI).

Section 3.2: Karina Lebel (UdeS – Université de Sherbrooke), Thierry Daviault (UdeS), Patrick Boissy (UdeS), Roua Walha (UdeS), Nathaly Gaudreault (UdeS), Christian Duval (Université du Québec à Montréal), Pierre Blanchet (CHUM, Université de Montréal), Réjean Plamondon (PM).

Section 3.3: Romeo Salameh (UdeM- Université de Montréal), Guillaume Seguin de Broin (PM), Maria Cristina Carmona Duarte (Las Palmas University, Gran Canarie, Spain), Karina Lebel (UdeS - Université de Sherbrooke), Vanessa Bachir (UdeM), Réjean Plamondon (PM), Pierre Blanchet (Centre Hospitalier de l’Université de Montréal).

Section 4.1 : Christian O’ Reilly (University of South Carolina,USA, USC), Dee-pa Tilwani (USC.), Jessica Bradshaw (USC).

Section 4.2: Mickael Begon (UdeM - Université de Montréal), Anaïs Laurent (PM), Ben Braithwaite (PM), Réjean Plamondon (PM).

Section 4.3: Youssef Beloufa (PM), Aymeric Guy (Life Engine Technologies Inc, LET.), Catherine Forest-Nault (LET.), Louis Marceau (LET.), Olivier Desbiens (PM) Réjean Plamondon, (PM).

Section 4.4: Frédéric Fol Leymarie (University of London -Goldsmiths), Daniel Berio (Goldsmiths, University of London, UK.), Réjean Plamondon (PM).

Section 5.1: Simon Pierre Boyogueno-Bidias (PM) , Jean-Pierre David (PM), Yvon Savaria (PM), Réjean Plamondon (PM).

Section 5.2: Andreas Fischer (University of Applied Sciences and Arts Western Switzerland, HES-SOS), Roman Schindler (HES-SOS, University of Fribourg, Swit-zerland.), Manuel Bouillon (HES-SOS University of Fribourg, Switzerland.), Réjean Plamondon (PM).

Section 5.3: Zigeng Zhang (PM), Christian O’ Reilly (University of South Carolina), Réjean Plamondon (PM).

Section 6.1: Denis Alamargot (Université Poitiers, France), Marie-France Morin (UdeS - Université de Sherbrooke), Nadir Faci (PM), Réjean Plamondon (PM).

Section 6.2: Céline Rémi (Université des Antilles, Guadeloupe, UA), Jimmy Na-geau (UA), Jean Vaillant (UA), Réjean Plamondon (PM), Emma-nuel Biabiany (UA).

Section 6.3 : Nadir Faci (PM), Naddley Désiré (SickKids Hospital Toronto), Miriam Beauchamp (UdeM - Université de Montréal), Isabelle Gagnon (Université McGill), Réjean Plamondon (PM).

Section 6.4: Raphaëlle Fortin (UdeM - Université de Montréal), Patricia Laniel (UdeM), Nadir Faci (PM), Miriam Beauchamp (UdeM), Réjean Plamondon (PM) Bruno Gauthier (UdeM).

Section 6.5: Marie-Noëlle Simard (UdeM - Université de Montréal), Thuy Mai Luu (CHU Ste-Justine, UdeM), Mathieu Dehaes (UdeM), Anne Gallagher (Université de Montréal), Anik Cloutier (CHU Sainte-Justine), Réjean Plamondon (PM).

Section 6.6: Caroline Bazinet (AleoVr), Alexis Maher (AleoVr), Charles Tétreault (AleoVr), Catherine Bazinet (AleoVr) , Andreas Fischer (University of Applied Sciences and Arts Western Switzerland, HES-SO) , Réjean Plamondon (PM).

Study 4.1 was supported by a pilot grant from the Carolina Autism & Neurodevelopment Center at the University of South Carolina (PI: C.O’R.). The ECG was collected under a National Institute of Mental Health grant (PI: J.B.; No: K23MH120476) and a National Institute on Deafness and Other Communication Disorders grant (PI: J.B., No: DC017252). The study presented in section 6.5 was funded by the Fondation des Étoiles. Its authors would like to thank all participants and their families.

Author information

Authors and Affiliations

  1. Laboratoire Scribens, Département de Génie Électrique, Polytechnique Montréal, Montréal, Canada

    Réjean Plamondon, Guillaume Séguin de Broin, Olivier Desbiens, Youssef Beloufa, Simon-Pierre Boyoguéno-Bidias, Zigeng Zhang & Nadir Faci

  2. Computer Vision Center, Computer Science Department, Universitat Autònoma de Barcelona, Bellaterra, Spain

    Asma Bensalah

  3. Département de Génie Électrique et Informatique, Université de Sherbrooke, Sherbrooke, Canada

    Karina Lebel

  4. Faculté de Médecine, Université de Montréal, Montréal, Canada

    Romeo Salameh

  5. Department of Computer Science and Engineering, University of South Carolina, Columbia, USA

    Christian O’Reilly

  6. LifeEngine Technologies Inc., Montréal, Canada

    Olivier Desbiens, Youssef Beloufa & Aymeric Guy

  7. Computing Department, Goldsmiths, University of London, London, UK

    Daniel Berio & Frederic Fol Leymarie

  8. University of Applied Sciences and Arts, Fribourg, Switzerland

    Andreas Fischer

  9. LAMIA, Université Des Antilles, Guadeloupe, France

    Céline Rémi

  10. AleoVR Inc., Montréal, Canada

    Caroline Bazinet

  11. Faculté de Médecine, École de Kinésiologie, Université de Montréal, Montréal, Canada

    Mickael Begon

  12. Laboratoire GRISE - Groupe de Recherche et d’Intervention sur les Adaptations Sociales de l’Enfance, Université de Sherbrooke, Sherbrooke, Canada

    Marie-France Morin

  13. Laboratoire CeRCA-CNRS - Centre de Recherche sur la Cognition et l’Apprentissage , Université de Poitiers, Poitiers, France

    Denis Alamargot

  14. Département de Psychologie, Université de Montréal, Montréal, Canada

    Raphaëlle Fortin

  15. Faculté de Médecine, École de Réhabilitation, Université de Montréal, Montréal, Canada

    Marie-Noëlle Simard

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  22. Caroline Bazinet
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Correspondence to Réjean Plamondon .

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  1. University of Salerno, Fisciano, Italy

    Antonio Parziale

  2. Universidad de las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Moises Diaz

  3. University of Lisbon, Lisbon, Portugal

    Filipe Melo

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Plamondon, R. et al. (2023). Lognormality: An Open Window on Neuromotor Control. In: Parziale, A., Diaz, M., Melo, F. (eds) Graphonomics in Human Body Movement. Bridging Research and Practice from Motor Control to Handwriting Analysis and Recognition. IGS 2023. Lecture Notes in Computer Science, vol 14285. Springer, Cham. https://doi.org/10.1007/978-3-031-45461-5_15

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  • DOI: https://doi.org/10.1007/978-3-031-45461-5_15

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  • Print ISBN: 978-3-031-45460-8

  • Online ISBN: 978-3-031-45461-5

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