Speed analysis of handwriting process models

Benrejeb, Mohamed; Abed-Abdelkrim, A. El

doi:10.1109/icsmc.2002.1167986

Cited by 5 publications

(7 citation statements)

References 4 publications

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“…The proposed generalized model of the handwriting process of Figure can be reorganized according to Figure . From this reorganization of the elements constituting the process, a first artificial neuro‐fuzzy model is proposed, Figure , . This model contains a fuzzy regulator representing the biological system of perception and control and a neural network denoted RND representing the generalized actuator.…”

Section: To a Global Model Designmentioning

confidence: 99%

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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Section: To a Global Model Designmentioning

confidence: 99%

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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“…In Fig. 2, are presented the recorded data for the Arabic letter, ‫,ح‬ "HA", from [10]. It is difficult to get the useful information from muscles activities.…”

Section:  Bipolar Surface Electrodes (Medicotest Bluementioning

confidence: 99%

“…For this, a variety of signal processing techniques are used to make EMG waveforms easier to interpret. Indeed, the fluctuation of EMG"s magnitudes can be filtered, [10] Scripters were of different gender and ages. The data base is a set of some Arabic letters, and eight basic geometric shapes, presented in table I of [9].…”

Section:  Bipolar Surface Electrodes (Medicotest Bluementioning

confidence: 99%

“…Firstly, for the current writing instant t i , where , equation 9 According to these simulation results, there is a considerable deviation between the experimental data and the model outputs, expressed in " (10) are the coordinates of experimental recorded data and d=x,y are outputs generated by the Yasuhara model. Fig.4 illustrates errors signals according to x movement presented in continuous line, and y movement in dashed line.…”

Section: A Controller For the Handwriting Systemmentioning

confidence: 99%

“…Other researches such as [9][10][11][12] show that the complex movement of hand writing can be modelled on the plane (x,y), from two most active forearm muscles, named "Abductor Pollicis Longus" and "Extensor Carpi Ulnaris". They assure vertical and horizontal displacement, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Biological Feedback Controller Design for Handwriting Model

Badri¹,

Chihi²,

Abdelkrim³

2018

ijacsa

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This paper deals with a feedback controller of PD (proportional, derivative) type applied to the process of handwriting. The considered model for this study describes the behavior of the system "hand and pen" to forearm muscles forces, applied for the production of handwriting. The applied approach considers memory recall of error signal between model outputs and experimental data to reach a desired trajectory position, a rapid dynamic and stable model response. The control technique is applied in order to expand the handwriting model response to a larger database of graphic traces. The obtained results illustrated the reliability of closed loop control to command the handwriting system, and to ensure its robustness against unknown inputs such as muscles forces that could vary from an individual to another and increase model complexity.

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Neural network modeling and implementation of a handwriting system

Dhouibi

Abdelkrim

2018

2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET)

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Speed analysis of handwriting process models

Cited by 5 publications

References 4 publications

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

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

Biological Feedback Controller Design for Handwriting Model

Neural network modeling and implementation of a handwriting system

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