@article {10.3844/ajassp.2012.1742.1756,
article_type = {journal},
title = {Analysis of Handwriting Velocity to Identify Handwriting Process from Electromyographic Signals},
author = {Chihi, Ines and Abdelkrim, Afef and Benrejeb, Mohamed},
volume = {9},
year = {2012},
month = {Aug},
pages = {1742-1756},
doi = {10.3844/ajassp.2012.1742.1756},
url = {https://thescipub.com/abstract/ajassp.2012.1742.1756},
abstract = {Problem statement: Handwriting movement is one of the most complex activities of human motions. It’s a blend of kinesthetic, cognitive, perceptual and motor components. The study of this biological process shows that a bell-shaped velocity profiles are generally observed in the handwriting motion. We, therefore, assume that the handwriting speed has an important role in control and generation of this human process and that the control nervous system might take this information into account to reconstruct the pen-tip trace. The Electromyography (EMG) signals measured on the skin surface of a writing forearm contain the adequate information to present the motor commands of the handwriting process. Approach: In this study, an identification technique, based on Recursive Least Square algorithm (RLS), is proposed to identify the pen-tip movement in human handwriting process, by using input and output data which present EMG signals and velocities according to x and y coordinates.  Results: The proposed approach of handwriting identification indicates that the pen-tip movement can be reconstruct from EMG signals of forearm muscles. The obtained results show better concordance with the experimental data than results obtained from other approach elaborated in the literature. Conclusion: The proposed handwriting model shows generally, good agreement with the real pen-tip movement. This method should be refined in monowriter and multiwriter case and independently of the size or the direction of the writing shape.},
journal = {American Journal of Applied Sciences},
publisher = {Science Publications}
}