Neuro-Muscular Engineering
Rahele Shafaei; Seyed Mohammad Reza Hashemi Golpayegani
Volume 5, Issue 3 , June 2011, , Pages 214-228
Abstract
One of main the issues in achieving to a successful FES control is using an as much as possible accurate model of the under electrical stimulation system so that it can adequately indicate the system behavior. Classical computational models that are commonly used for this purpose have a reductionism ...
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One of main the issues in achieving to a successful FES control is using an as much as possible accurate model of the under electrical stimulation system so that it can adequately indicate the system behavior. Classical computational models that are commonly used for this purpose have a reductionism nature; so they cannot consider the interaction existed in biological systems. Considering these restrictions, recently behavioral black box models are mostly used. These models focus on input/output dynamic, which is certainly the necessary modeling information for control design; thus the system is dealt with as a whole, which has hidden the interactions between components inside. Such a model has notbeen presented for elbow angle movement so far. Therefore in this study, we have been to present and verify a black box model of elbow joint movement in the transverse plane, forreaching movement control in people with C5/C6 SCI using dynamic neural networks, including time-delayed feedforward and recurrent networks. Extreme flexibility of time-delayed feedforward architectures was obtainedin a 2 layer structure including 5 hidden neurons and using 1.25s of history of input with performance indexes of 89.89% & 4.85% for cross correlation coefficient and normalized mean square error respectively. The best recurrent network with NARX architecture and equal history of input & output was also occurred in a 2 layer structure having 12 neurons in the hidden layer and using 0.1s of history, with performance indexes of 89.89% & 4.85% for cross correlation coefficient and normalized mean square error respectively. Comparison between best results of training using feedforward and recurrent networks, clearly illustrates both qualitative and quantitative excellency of the latter one in identification of the under-study system.
Biomimetics
Saeed Rashidi; Seyed Mohammad Reza Hashemi Golpayegani; Ali Fallah; Farzad Towhidkhah
Volume 4, Issue 1 , June 2010, , Pages 33-44
Abstract
In drawing movements, the constraints imposed on the trajectory geometry properties and kinematics are known with two laws: 2/3 power law and isochrony phenomenon. In this paper experiments have been designed to study the relation between two empirical laws in straight and curved patterns of drawing ...
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In drawing movements, the constraints imposed on the trajectory geometry properties and kinematics are known with two laws: 2/3 power law and isochrony phenomenon. In this paper experiments have been designed to study the relation between two empirical laws in straight and curved patterns of drawing movements in 16-18 years old subjects. Providing two models of power is indicated that in drawing movements, invariant features can be defining. These features are independent of subject, direction and size of trajectory and together they can simplify the role of the upper motor control system and decrease the degrees of freedom and the computational complexity.
Biological Computer Modeling / Biological Computer Simulation
Siamak Haghipour; Seyed Mohammad Reza Hashemi Golpayegani; Seyed Mohammad Firouzabadi; Sirous Momenzadeh
Volume 3, Issue 3 , June 2009, , Pages 227-241
Abstract
The procedure of pain formation embarks on primary sensory neurons and then ends in central nervous system which is the first stage in the dorsal horn of the spinal cord. Nowadays the great challenge of some researchers for pain control has been to elucidate the mechanisms that are able to switch the ...
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The procedure of pain formation embarks on primary sensory neurons and then ends in central nervous system which is the first stage in the dorsal horn of the spinal cord. Nowadays the great challenge of some researchers for pain control has been to elucidate the mechanisms that are able to switch the state of the dorsal horn of the spinal cord from an unwanted state to a favorite one. In order to achieve such an aim, a model of the function of the dorsal horn of the spinal cord is extracted in order to be able to control the created pains with changing the parameters of the aforementioned model. In this study a cybernetic model is presented with the aid of bifurcation methodologies and reconstructing the dynamics linked with the process of pain formation via clinical experiment that can express different states in the dorsal horn of the spinal cord as normal, suppressed, sensitized, the functionality of memory, the effect of other primary afferents and the effect of descending signals. Input signals in this model consist of thermal stimulation degree proportional to action potential firing rate from Ab afferents, inhibitory descending signals from midbrain and inhibitory or excitatory descending signal from thalamus and cortex and the output signal is the action potential firing rate from transmission cells in dorsal horn of the spinal cord proportional to pain level have been sensed. The significant and remarkable characteristic of this model is applying a cybernetical model based on a sequence of input-output data which can obviate the drawbacks of other models in which simplification and reduction of terms reduce the operation of components of a system. On the other hand, unlike previous models which have been modeled based on membrane (slow) potential, this model is based on the action potential firing rate from transmission cells of the dorsal horn of the spinal cord that has the adaptability with cellular recording as well as having a higher accuracy.
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.
Neuro-Muscular Engineering
Amin Mahnam; Seyed Mohammad Firouzabadi; Seyed Mohammad Reza Hashemi Golpayegani
Volume -1, Issue 1 , June 2004, , Pages 65-76
Abstract
In recent years, various methods have been suggested to improve selectivity in electrical stimulation of neural fibers or cells. One of these methods is the use of depolarizing under-threshold prepulse to selectively stimulate fibers far from the electrode, without excitation of nearer fibers. In this ...
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In recent years, various methods have been suggested to improve selectivity in electrical stimulation of neural fibers or cells. One of these methods is the use of depolarizing under-threshold prepulse to selectively stimulate fibers far from the electrode, without excitation of nearer fibers. In this paper, by implementing a nonlinear model of neural fiber and simulating electrical stimulation of the model, the effect of changes in various parameters of rectangular and stepwise prepulses on the range of applicability of this technique in selective stimulation of fibers in different distances from the electrode and with different diameters has been studied. This study has led to suggest a new waveform for the prepulse; ramp prepulse. The applicability of this prepulse has been studied also. The superiority of this prepulse in comparison with previous suggested ones has been shown. Using this prepulse, it is possible to stimulate selectively fibers in broader range of distances and diameters. Therefore in stimulating neural fibers in spinal cord or peripheral fibers or even neural fibers of special senses, the use of this prepulse can improve distinguishability of fibers in their stimulation.
