Neuro-Muscular Engineering
Mehdi Borjkhani; Farzad Towhidkhah
Volume 4, Issue 2 , June 2010, , Pages 109-122
Abstract
Writing is one of the high practiced and complex movement skills of human. Most of the proposed models for writing are bottom-up models, and therefore they could not reflect the biological aspects of movements in this process. Also there is not any model for illustrating the role of different parts of ...
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Writing is one of the high practiced and complex movement skills of human. Most of the proposed models for writing are bottom-up models, and therefore they could not reflect the biological aspects of movements in this process. Also there is not any model for illustrating the role of different parts of the brain in this task. In this paper we are going to describe some neurological and physiological aspects of the brain operation in the writing task. Then some evidence of prediction in writing and existence of internal models for limbs such as hand are presented. According to these, modeling of writing using model predictive control (MPC) is possible. Based on the presented simulations and experimental results it seems that the modeling of writing by MPC is very similar to the real skill, The proposed model has some advantages such as being consistent with the biological evidence, modeling prediction in writing and high correlation of the statical and dynamical features of the generated letters with those written by human.
Neuro-Muscular Engineering
Amir Homayoun Jafari; Seyed Mohammad Reza Hashemi Golpayegani; Farzad Towhidkhah; Ali Fallah
Volume -2, Issue 1 , July 2005, , Pages 57-70
Abstract
A hierarchical structure model with three levels is presented for modeling motor control in skill movements. At each level, based on accuracy and quality of control, a specific controller is activated. At first level, control concepts are qualitative. The duty of the first level is to provide stability ...
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A hierarchical structure model with three levels is presented for modeling motor control in skill movements. At each level, based on accuracy and quality of control, a specific controller is activated. At first level, control concepts are qualitative. The duty of the first level is to provide stability of system, based on the received qualitative information from second level such as the decrement or increment of error. A self-organized controller at first level is used to generate qualitative control commands, and it plays an encouragement-punishment role to keep the stability of system by sending discrete commands to the second level. This controller only contributes at control action when the controller of second level can not preserve stability individually. At second level, control concepts are quantitative. The duty of the second level is adaptation and control of system accurately. The received information at this level generally comes from sensory and visual feedbacks, and it includes more accurate concepts of control action - like the amount of movement error. A model based on the predictive controller at second level generates quantitative control commands and indeed, determines trajectory of movement accurately. A fuzzy switch combines the control commands of first and second levels, based on the sliding mode strategy, to provide a robust control. At third level, this command is interpreted and then is applied to the involved muscles in movement. The received information at this level is generally the contribution of muscles in performing movement and the effects of environment on the movement, which comes from sensory feedbacks. The presented model with this hierarchical structure has a proper ability to control and keep the stability of system. The simulation results confirm this subject.
