Neural network modeling and implementation of a handwriting system

Dhouibi, Meriam; Abdelkrim, Hedi; Abdelkrim, Afef

doi:10.1109/aset.2018.8379849

Cited by 2 publications

(2 citation statements)

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“…As for kinematic models, they generate curvilinear speed profiles reflecting the effect of neuromuscular impulses involved in the generation of motions. Many models have been developed under this approach such as the deltalognormal [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], the double gaussian [30], the sigma-lognormal [31], the double beta [32]. The problem of kinematic models is the lack of information on the spatial aspect of the movement.…”

Section: Overview Of Some Handwriting Modelsmentioning

confidence: 99%

“…For the characterization of the global model, we proposed different conventional and unconventional modeling approaches. Indeed, starting from the high order and complex handwriting process, we initiated work exploiting conventional identification approaches such as the recursive least squares algorithm in order to determine a coherent structure of the handwriting system and reduce the order of the model of this process, . Multimodel concept has also been proposed in order to integrate intra and inter writers variability, , Regarding unconventional approaches, we first proposed different types of neural models using EMG signals and neural network approaches, to model the control system of the handwriting process.…”

Section: Introductionmentioning

confidence: 99%

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Conventional and non conventional body motions modelling and control. Application to the handwriting process

Abdelkrim

Benrejeb

2019

Asian Journal of Control

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The handwriting process is one of the most important and complex activities of the human body. In recent years, modelling and analyzing and controlling it have received great attention leading to large important motion studies results. This paper attempts to present an overview of recent works about our proposed approaches and other ones to model and control this process. Hence, an overall model of the handwriting process has been proposed and developed by the coauthors, showing the interest of using the soft computing concepts, namely neural networks and fuzzy logic. The design of this model requires several undertaken steps. Starting from an experimental approach to acquire electromyographic signals and writing positions, different conventional and non conventional models have been proposed. Besides some control strategies have been considered in particular neural and fuzzy approaches in addition to non conventional neural and neuro-fuzzy Internal Model Control strategy. The proposed global model, inspired from the biological structure system, has been designed and validated through simulation results.

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