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.
Rehabilitation Engineering
Ali Maleki; Ali Fallah
Volume 2, Issue 2 , June 2008, , Pages 131-140
Abstract
Patients with spinal cord injury in C5/C6 levels are capable of controlling the voluntary movements of the shoulder joints, but some muscles involved in the movement of the elbow joint are paralyzed in these patients. By using FES as well as an appropriate stimulation of the paralyzed muscles, the patients ...
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Patients with spinal cord injury in C5/C6 levels are capable of controlling the voluntary movements of the shoulder joints, but some muscles involved in the movement of the elbow joint are paralyzed in these patients. By using FES as well as an appropriate stimulation of the paralyzed muscles, the patients can be assisted with their essential daily living activities. One of the major problems of using FES for reanimation of the paralyzed arm is to provide voluntary commands for FES control. Kinematic synergy and muscle synergy are two main options in this regard. In this paper, these two command sources were evaluated and compared. Furthermore, a mixed method was proposed, which improves performance. Thus, the EMG and kinematical data during a set of activities of daily living (AOL) were recorded and processed. Precise investigations were carried out in order to determine the appropriate values for high-level neural network controller parameters. Next, six different neural network controller structures were trained by the EMG and/or kinematical data. Using this method, cross correlation between the estimation and measurement for all records was obtained as 94.76% for kinematic synergy and 98.08%, for muscle synergy. In the mixed method, these values were improved to 94.82% and 98.84% respectively. Furthermore, mixed method paved the way to improve the performance of low-level controller with estimating the desired kinematics for the distal joint and desired activity for the paralyzed muscle.
