Research Article Open Access

Analysis of Handwriting Velocity to Identify Handwriting Process from Electromyographic Signals

Mohamed Benrejeb1, Afef Abdelkrim1 and Ines Chihi1
  • 1 Departement of Electrical Engineering, Laboratory of Automatic Researchs (LA.R.A), National Engineering School of Tunis, Le Belvédère 1002 Tunis, Tunisia


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.

American Journal of Applied Sciences
Volume 9 No. 10, 2012, 1742-1756


Submitted On: 9 February 2012 Published On: 27 August 2012

How to Cite: Benrejeb, M., Abdelkrim, A. & Chihi, I. (2012). Analysis of Handwriting Velocity to Identify Handwriting Process from Electromyographic Signals. American Journal of Applied Sciences, 9(10), 1742-1756.

  • 20 Citations



  • Traces present movements
  • global generalized handwriting
  • Electromyography (EMG)
  • Least Square algorithm (RLS)
  • simultaneous flexion-extension