Combining sigma-lognormal modeling and classical features for analyzing graphomotor performances in kindergarten children

Duval, Thérésa; Rémi, Céline; Plamondon, Réjean; Vaillant, Jean; O’Reilly, Christian

doi:10.1016/j.humov.2015.04.005

Cited by 28 publications

(27 citation statements)

References 44 publications

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“…An alternative approach to modeling the handwritten patterns is to use the Kinematic Theory of Rapid Human Movements [47,48] and, in particular, the so-called sigma-lognormal (ΣΛ) model [49]. This model has been used with successful results in many practical applications, for example, for developing an online signature verification system [50] and for analyzing graphomotor performance in kindergarten children [51]. The main advantage of this approach is that it is based on a physiological model of the human movement production which can lead to an improved characterization of the hidden specificity of the writers.…”

Section: Feature Extractionmentioning

confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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Studying the effects of neurodegeneration on handwriting has emerged as an interdisciplinary research topic and has attracted considerable interest from psychologists to neuroscientists and from physicians to computer scientists. The complexity of handwriting, in fact, appears to be sensitive to age-related impairments in cognitive functioning; thus, analyzing handwriting in elderly people may facilitate the diagnosis and monitoring of these impairments. A large body of knowledge has been collected in the last thirty years thanks to the advent of new technologies which allow researchers to investigate not only the static characteristics of handwriting but also especially the dynamic aspects of the handwriting process. The present paper aims at providing an overview of the most relevant literature investigating the application of dynamic handwriting analysis in neurodegenerative disease assessment. The focus, in particular, is on Parkinon’s disease (PD) and Alzheimer’s disease (AD), as the two most widespread neurodegenerative disorders. More specifically, the studies taken into account are grouped in accordance with three main research questions: disease insight, disease monitoring, and disease diagnosis. The net result is that dynamic handwriting analysis is a powerful, noninvasive, and low-cost tool for real-time diagnosis and follow-up of PD and AD. In conclusion of the paper, open issues still demanding further research are highlighted.

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Section: Feature Extractionmentioning

confidence: 99%

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Vessio

2019

Applied Sciences

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“…In addition, it was found that in right‐handers, the non‐dominant limb links were more tightly frozen than was the case with the dominant limb, while this was not a case in left‐handers, suggesting the influence of writing direction on the acquisition of coordination patterns (Newell & van Emmerik, 1989). More recently, Duval and co‐workers (Duval, Rémi, Plamondon, Vaillant, & O'Reilly, 2015) studied the graphomotor skills of kindergarten children who produced pre‐calligraphic trajectories, using different indexes of rapidity, fluidity, and regularity of their pen tip movements.…”

Section: Introductionmentioning

confidence: 99%

“…One of the curious facts about the previous studies on the development of handwriting is that the tasks performed by participants were often not the actual task of writing meaningful letters, but that of drawing or tracing of nonsense graphic forms such as straight lines and ellipses (Danna, Enderli, Athènes, & Zanone, 2012;Greer & Lockman, 1998), continuous lines of loop patterns (Bosga-Stork, Bosga, & Meulenbroek, 2011), or other abstract letter-like patterns (Duval et al, 2015;Jongbloed-Pereboom, Peeters, Overvelde, Nijhuis-van der Sanden, & Steenbergen, 2015). In these studies, the task of handwriting was viewed as the task of forming and producing a desired spatial trajectory of the tip of a tool held in hand.…”

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confidence: 99%

Cultural entrainment of motor skill development: Learning to write hiragana in Japanese primary school

Nonaka

2017

Developmental Psychobiology

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The aim of the present study was to examine how the social norms shared in a classroom environment influence the development of movement dynamics of handwriting of children who participate in the environment. To look into this issue, the following aspects of the entire period of classroom learning of hiragana letters in Japanese 1st graders who had just entered primary school were studied: First, the structure of classroom events and the specific types of interaction and learning within such environment were described. Second, in the experiment involving 6‐year‐old children who participated in the class, writing movements of children and their changes over the period of hiragana education were analyzed for each stroke composing letters. It was found that writing movement of children became differentiated in a manner specific to the different types of stroke endings, to which children were systematically encouraged to attend in the classroom. The results provide a detailed description of the process of how dynamics of fine motor movement of children is modulated by the social norms of a populated, classroom environment in a non‐Latin alphabet writing system.

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“…The Sigma-Lognormal model decomposes the complex signals that describe the speed of muscular movements into simpler ones that can be explained by a few parameters. These parameters contain information about the activity itself and about the neuromotor skills of the person [14]. Studies like [14] have proved that the Sigma-Lognormal model can be used to characterize children handwriting.…”

Section: Introductionmentioning

confidence: 99%

“…These parameters contain information about the activity itself and about the neuromotor skills of the person [14]. Studies like [14] have proved that the Sigma-Lognormal model can be used to characterize children handwriting. They conclude that there are two main groups of children separable by looking at their learning stage.…”

Section: Introductionmentioning

confidence: 99%

Predicting Age Groups from Touch Patterns based on Neuromotor Models

Hernandez-Ortega

Morales

Fiérrez

et al. 2017

8th International Conference of Pattern Recognition Systems (ICPRS 2017)

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This paper proposes a new method for user classification into children and adults according to their interaction with touchscreen devices. The method is based on the Sigma-Lognormal theory of rapid human movements. The experimentation is conducted on a publicly available database with samples obtained from 89 children between 3 and 6 years old and 30 adults. The neuromotor skills of the users are first described using Sigma-Lognormal parameters from a single drag and drop task. We have then used Support Vector Machines to classify the resulting features into age groups. The experiments include single-sensor and cross-sensor scenarios using data from various smartphones and tablets. The results, with correct classification rates over 96%, show the discriminative ability of the proposed neuromotor-inspired features to classify age groups according to the interaction with touch devices.

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Combining sigma-lognormal modeling and classical features for analyzing graphomotor performances in kindergarten children

Cited by 28 publications

References 44 publications

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Dynamic Handwriting Analysis for Neurodegenerative Disease Assessment: A Literary Review

Cultural entrainment of motor skill development: Learning to write hiragana in Japanese primary school

Predicting Age Groups from Touch Patterns based on Neuromotor Models

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