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Neuromuscular Modelling and Analysis of Handwriting: from Automatic Generation to Biomedical and Neurocognitive applicationsold_uid | 12441 |
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title | Neuromuscular Modelling and Analysis of Handwriting: from Automatic Generation to Biomedical and Neurocognitive applications |
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start_date | 2013/05/15 |
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schedule | 10h |
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online | no |
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summary | Many models have been proposed over the years to study human movements in general and
handwriting in particular: models relying on neural networks, dynamics models, psychophysical
models, kinematic models and models exploiting minimization principles. Among the models
that can be used to provide analytical representations of a pen stroke, the Kinematic Theory of
rapid human movements and its family of lognormal models has often served as a guide in the
design of pattern recognition systems relying on the exploitation of the fine neuromotricity, like
on-line handwriting segmentation, signature verification as well as in the design of intelligent
systems involving in a way or another, the global processing of human movements. Among
other things, this lecture aims at elaborating a theoretical background for many handwriting
applications as well as providing some basic knowledge that could be integrated or taking care
of in the development of new automatic pattern recognition systems to be exploited in
biomedical engineering and cognitive neurosciences.
More specifically, we will overview the basic neuromotor properties of single strokes and will
explain how they can be superimposed vectorially to generate complex pen tip trajectories.
Doing so, we will report on various projects conducted by our team and our collaborators. First,
we will present a brief comparative survey of the different lognormal models. Then, from a
practical perspective, we will describe some parameter extraction algorithms suitable for the
reverse engineering of individual strokes as well as of complex handwriting signals. We will show
how the resulting representation could be employed to characterize signers and writers and
how the corresponding feature sets could be exploited to study the effects of various factors,
like aging and health problems, on handwriting variability. We will also describe some
methodologies to generate automatically huge on-line handwriting databases for either writer
dependent or writer independent applications as well as for the production of synthetic
signature databases. From a theoretical perspective, we will explain how, using an original
psychophysical set up, we have been able to validate the basic hypothesis of the Kinematic
Theory and to test its most distinctive predictions. We will complete this survey by explaining
how the Kinematic Theory could be utilized to improve some signal processing techniques,
opening a window on novel potential applications for on-line handwriting processing,
particularly to provide some benchmarks to analyze children handwriting learning, to study
aging effects on neuromotor control as well as developing diagnostic systems for
neuromuscular disorders. To illustrate this latter point, we will report typical results obtained so
far for the assessment of brain stroke most important modifiable risk factors (diabetes,
hypertension, hypercholesterolemia, obesity, cardiac problems, cigarette smoking). |
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responsibles | Revault d'Allonnes |
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