Multi-model approach to characterize human handwriting motion

This paper deals with characterization and modelling of human handwriting motion from two forearm muscle activity signals, called electromyography signals (EMG). In this work, an experimental approach was used to record the coordinates of a pen tip moving on the (x, y) plane and EMG signals during the handwriting act. The main purpose is to design a new mathematical model which characterizes this biological process. Based on a multi-model approach, this system was originally developed to generate letters and geometric forms written by different writers. A Recursive Least Squares algorithm is used to estimate the parameters of each sub-model of the multi-model basis. Simulations show good agreement between predicted results and the recorded data.

[1]  M. Lisak.  Color Atlas of Human Anatomy. Volume 1: Locomotor System , 2004 .

[2]  Jie Xiao,et al.  Fuzzy Predictive Control for a Tubular Heat Exchanger System Based Multiple Models Strategy , 2013, 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[3]  Tarek Tutunji,et al.  Mechatronic systems identification using an impulse response recursive algorithm , 2007, Simul. Model. Pract. Theory.

[4]  Victor I. Djigan,et al.  Multichannel parallelizable sliding window RLS and fast RLS algorithms with linear constraints , 2006, Signal Process..

[5]  Ruud G. J. Meulenbroek,et al.  Stroke-direction preferences in drawing and handwriting , 1991 .

[6]  Mohamed Benrejeb,et al.  A new approach for multimodel identification of complex systems based on both neural and fuzzy clustering algorithms , 2010, Eng. Appl. Artif. Intell..

[7]  Kais Bouzrara,et al.  Expansion of MIMO ARX model on Laguerre orthonormal bases , 2013, 2013 International Conference on Electrical Engineering and Software Applications.

[8]  S. Ms.  Human Anatomy: A Clinically-Orientated Approach , 2007 .

[9]  Ding Feng.  System identification.Part F:Multi-innovation identification theory and methods , 2012 .

[10]  M Yasuhara,et al.  [Experimental studies of handwriting process]. , 1974, Iyo denshi to seitai kogaku. Japanese journal of medical electronics and biological engineering.

[11]  J. Strackee,et al.  A handwriting simulator. , 1962, Physics in medicine and biology.

[12]  Mohamed Benrejeb,et al.  Analysis of Handwriting Velocity to Identify Handwriting Process from Electromyographic Signals , 2012 .

[13]  Emil Levi,et al.  Identification of complex systems based on neural and Takagi-Sugeno fuzzy model , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  H. Natsuyama.  System identification: methods and applications , 1974 .

[15]  Réjean Plamondon,et al.  A kinematic theory of rapid human movements , 1995, Biological Cybernetics.

[16]  R. Plamondon.  Errata: A kinematic theory of rapid human movements, Part II. , 1995 .

[17]  Pierre Borne,et al.  A Neural Approach of Multimodel Representation of Complex Processes , 2008, Int. J. Comput. Commun. Control.

[18]  Kumpati S. Narendra,et al.  Adaptive control using multiple models , 1997, IEEE Trans. Autom. Control..

[19]  F. Ding,et al.  Least‐squares parameter estimation for systems with irregularly missing data , 2009 .

[20]  Jane C. Holland,et al.  Human anatomy: A clinically-orientated approach , 2010 .

[21]  Feng Ding,et al.  Auxiliary model identification method for multirate multi-input systems based on least squares , 2009, Math. Comput. Model..

[22]  Feng Ding,et al.  Auxiliary model-based least-squares identification methods for Hammerstein output-error systems , 2007, Syst. Control. Lett..

[23]  John S. MacDonald,et al.  Experimental studies of handwriting signals. , 1966 .