Éditeur(s) : HAL CCSD
Résumé : International audience
This paper deals with the study of the kinematic dimension of scribbling activities executed by kindergarten children aged from 3 to 6 years old from three grades. For this purpose, three sigma-lognormal features, six classical ones and one hybrid feature related to visuo-motor skills are extracted from scribbles realized using five different Type Grib. The statistical analysis of these data illustrates that sigma-lognormal modeling can satisfactorily reconstruct kindergarten children’s scribbles. Moreover, this preliminary study confirms that there are significant differences between grades with respect to six of the features we studied, regardless of the nature of the scribbling movement made by children. These features are related to rapidity, fluidity and precision of linear and curvilinear movements used for scribbling tasks.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165910
https://hal.univ-antilles.fr/hal-01165910
https://hal.univ-antilles.fr/hal-01165910/document
https://hal.univ-antilles.fr/hal-01165910/file/IGS_2015_submission_28.pdf

Éditeur(s) : HAL CCSD Frontiers
Résumé : International audience
The generation of handwriting is a complex neuromotor skill requiring the interaction of many cognitive processes. It aims at producing a message to be imprinted as an ink trace left on a writing medium. The generated trajectory of the pen tip is made up of strokes superimposed over time. The Kinematic Theory of rapid human movements and its family of lognormal models provide analytical representations of these strokes, often considered as the basic unit of handwriting. This paradigm has not only been experimentally confirmed in numerous predictive and physiologically significant tests but it has also been shown to be the ideal mathematical description for the impulse response of a neuromuscular system. This latter demonstration suggests that the lognormality of the velocity patterns can be interpreted as reflecting the behavior of subjects who are in perfect control of their movements. To illustrate this interpretation, we present a short overview of the main concepts behind the Kinematic Theory and briefly describe how its models can be exploited, using various software tools, to investigate these ideal lognormal behaviors. We emphasize that the parameters extracted during various tasks can be used to analyze some underlying processes associated with their realization. To investigate the operational convergence hypothesis, we report on two original studies. First, we focus on the early steps of the motor learning process as seen as a converging behavior toward the production of more precise lognormal patterns as young children practicing handwriting start to become more fluent writers. Second, we illustrate how aging affects handwriting by pointing out the increasing departure from the ideal lognormal behavior as the control of the fine motricity begins to decline. Overall, the paper highlights this developmental process of merging toward a lognormal behavior with learning, mastering this behavior to succeed in performing a given task, and then gradually deviating from it with aging.
ISSN: 1664-1078

hal-01098124
https://hal.archives-ouvertes.fr/hal-01098124
DOI : 10.3389/fpsyg.2013.0094

Éditeur(s) : HAL CCSD
Résumé : International audience
The Sigma-Lognormal model of the Kinematic Theory of rapid human movements allows us to represent on-line signatures with an analytical neuromuscular model. It has been successfully used in the past to generate synthetic signatures in order to improve the performance of an automatic verification system. In this paper, we attempt for the first time to build a verification system based on the model parameters themselves. For describing individual lognormal strokes, we propose eighteen features which capture cognitive psychomotor characteristics of the signer. They are matched by means of dynamic time warping to derive a dissimilarity measure for signature verification. Promising initial results are reported for an experimental evaluation on the SUSIG visual sub-corpus, which contains some of the most skilled forgeries currently available for research.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165871
https://hal.univ-antilles.fr/hal-01165871
https://hal.univ-antilles.fr/hal-01165871/document
https://hal.univ-antilles.fr/hal-01165871/file/IGS_2015_submission_11.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
The development of predictive tools has been commonly utilized as the most effective manner to prevent illnesses thatstrike suddenly. Within this context, investigations linking fine human motor control with brain stroke risk factors areconsidered to have a high potential but they are still in an early stage of research. The present paper analysesneuromuscular features of oscillatory movements based on the Omega-Lognormal model of the Kinematic Theory. On adatabase of oscillatory movements from 120 subjects, we demonstrate that the proposed features differ significantlybetween subjects with and without brain stroke risk factors. This promising result motivates the development ofpredictive tools based on the Omega-Lognormal model.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165773
https://hal.univ-antilles.fr/hal-01165773
https://hal.univ-antilles.fr/hal-01165773/document
https://hal.univ-antilles.fr/hal-01165773/file/IGS_2015_submission_9.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
In this paper, we introduce a framework for on-line learning of handwritten symbols from scratch. As such, learning suffers from missing data at the beginning of the learning process, in this paper we propose the use of Sigma-lognormal model to generate synthetic data. Our framework deals with a real-time use of the system, where the recognition of a single symbol cannot be postponed by the generation of synthetic data. We evaluate the use of our framework and Sigma-lognormal model by comparison of the recognition rate to a block-learning and learning without any synthetic data. Experimental results show that both of these contributions represent an enhancement to the on-line handwriting recognition, especially when starting from scratch.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165923
https://hal.univ-antilles.fr/hal-01165923
https://hal.univ-antilles.fr/hal-01165923/document
https://hal.univ-antilles.fr/hal-01165923/file/IGS_2015_submission_34.pdf

Éditeur(s) : HAL CCSD
Résumé : International audience
Graphomotor control constitutes a condition to the mastery of literacy and more widely to successful school apprenticeship. The Guadeloupian context is marked by significant rates of early school leaving and illiteracy. In such context, our research is concerned with two questions: “Does graphomotor training can help since kindergarten to strengthen graphomotor performances and the progression of literacy?” and “Which methodological approach can help to characterize the impact of graphomotor training?” By using online acquisition devices, we have tried to characterize the graphomotor skills for 132 pupils. These 3 to 5 years old children were submitted to 3 methods of graphomotor training. One of the methods that suppose family involvement, includes activities inspired by kid’s early scribbles. We considered the statistical analysis of the blind feedbacks of the teachers, of the features provided by the visuo-spatial processing of handwritten productions and of features provided by the sigma-lognormal modeling of the pupils’ movements. The results of this multimodal study show an increase in graphomotor control and in interest for drawing and handwriting tasks. The implication of these preliminary results on the pedagogical and co-educative choices is discussed.
Yves Mazabraud, Thomas Forissier, Alain Pietrus, Olivier Gros, Claire Anjou. CARIBBEAN ACADEMY OF SCIENCES 20 th GENERAL ASSEMBLY AND BIENNAL MEETING: Planning and Abstracts book. Nov 2016, Dehaies, Guadeloupe. 2016.
Deshaies, Guadeloupe

hal-01516420
https://hal.archives-ouvertes.fr/hal-01516420

Éditeur(s) : HAL CCSD
Résumé : International audience
The goal of our experiment is to develop a useful and accessible tool that can be used to evaluate a patient's health by analyzing handwritten strokes. We use a cloud computing approach to analyze stroke data sampled on a commercial tablet working on the Android platform and a distant server to perform complex calculations using the Delta and Sigma lognormal algorithms. A Google Drive account is used to store the data and to ease the development of the project. The communication between the tablet, the cloud and the server is encrypted to ensure biomedical information confidentiality. Highly parameterized biomedical tests are implemented on the tablet as well as a free drawing test to evaluate the validity of the data acquired by the first test compared to the second one. A blurred shape model descriptor pattern recognition algorithm is used to classify the data obtained by the free drawing test. The functions presented in this paper are still currently under development and other improvements are needed before launching the application in the public domain.
17th Biennial Conference of the International Graphonomics Society
Pointe-à-Pitre, Guadeloupe

hal-01165890
https://hal.univ-antilles.fr/hal-01165890
https://hal.univ-antilles.fr/hal-01165890/document
https://hal.univ-antilles.fr/hal-01165890/file/IGS_2015_submission_20.pdf

Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
In this paper, we have presented a spectral and a multifractal analysis performed on 412 time series of wind speed data each of duration of 350 s and sampled at 20 Hz. The average spectrum for the wind speed displays a scaling behavior, in the inertial range, over two decades, with b ¼ 1:68 close to the Kolmogorov value À 5/3. A multifractal analysis has been motivated by the presence of scaling invariance in data set. Then we have considered their scaling properties in the framework of fully developed turbulence and multifractal cascades. The results obtained for wind speed confirm that the exponent scaling function z V ðqÞ is nonlinear and concave. This exponent characterizes the scaling functions in the inertial range indicating that the wind speed is intermittent and multifractal. Moreover the theoretical quadratic relation for lognormal multifractals is well fitted. We investigate the consequence for wind energy production: we generate stochastic simulations of a multifractal random walk, and using a power curve derived from experimental data, we generate the associated power time series. We show that, due to the saturation of the power curve for large speed values, when the input time series (turbulent wind speed) is multifractal, the output can be almost monofractal.
ISSN: 0167-6105

hal-01527886
https://hal.archives-ouvertes.fr/hal-01527886
DOI : 10.1016/j.jweia.2012.06.002