Neuro-Muscular Engineering
Sohrab Barimani; Ali Maleki; Ali Fallah
Volume 8, Issue 1 , March 2014, , Pages 101-111
Abstract
FES based method used for rehabilitation of patients with spinal cord injury (SCI). One of these methods is FES cycling. FES cycling exercise has to be useful among SCI patients because of creating a periodic activity in the muscles of the lower extremities and stability of seating position. The major ...
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FES based method used for rehabilitation of patients with spinal cord injury (SCI). One of these methods is FES cycling. FES cycling exercise has to be useful among SCI patients because of creating a periodic activity in the muscles of the lower extremities and stability of seating position. The major challenge for application of FES in rehabilitation is early fatigue occurrence in electrically stimulated muscles. Motor control system selects a low-cost path among the infinite possible route to the body's movements. High efficiency and the minimum rate of muscle fatigue are main characteristics of the motor control system. This type of control system is called muscle synergy. In this study, the quantification of muscle synergy between the core muscles in cycling has been done by non-negative matrix factorization (NMF) method and considering the kinesiology basis. Four synergies were determined as appropriate and optimal synergies to describe the cycling in different mechanical terms. VAF criteria with regard to the four synergies to describe cycling in speeds of 40, 50 and 60 rpm are 92±4, 92±3 and 91±4% respectively and torques, 5, 7 and 9 Nm are 91±3, 92±5 and 92±4% respectively. Correlation between Synergies extracted at different mechanical terms is 98.4 percent in average.
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.
Neuro-Muscular Engineering
Abed Khorasani; Abbas Erfanian Omidvar
Volume 5, Issue 3 , June 2011, , Pages 245-255
Abstract
During the last decade, functional neuromuscular stimulation (FNS) has been proposed as a potential technique for restoring motor function in paralyzed limbs. A major challenge to restoring a desired functional limb movement through the use of intramuscular stimulation is the development of a robust ...
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During the last decade, functional neuromuscular stimulation (FNS) has been proposed as a potential technique for restoring motor function in paralyzed limbs. A major challenge to restoring a desired functional limb movement through the use of intramuscular stimulation is the development of a robust control strategy for determining the stimulation patterns. A major impediment to stimulating the paralyzed limbs and determining the stimulation pattern has been the highly non-linear, time-varying properties of electrically stimulated muscle, muscle fatigue, large latency and time constant which limit the utility of pre-specified stimulation pattern and open-loop FES control system. In this paper we present a robust strategy for multi-joint control through intramuscular stimulation in which the system parameters are adapted online and the controller requires no offline training phase. The method is based on the combination of sliding mode control with fuzzy logic and neural control. Extensive experiments on three rats are provided to demonstrate the robustness, stability, and tracking accuracy of the proposed method. The results show that the proposed strategy can provide accurate tracking control with fast convergence.
Rehabilitation Engineering
Vahab Nekoukar; Abbas Erfanian Omidvar
Volume 4, Issue 4 , June 2010, , Pages 327-336
Abstract
One major limitation of walker-supported walking using functional electrical stimulation (FES) in paraplegic subjects is the high energy expenditure and the high upper body effort. Paraplegics should exert high amount of hand force to stabilize the body posture and to compensate lack of the sufficient ...
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One major limitation of walker-supported walking using functional electrical stimulation (FES) in paraplegic subjects is the high energy expenditure and the high upper body effort. Paraplegics should exert high amount of hand force to stabilize the body posture and to compensate lack of the sufficient torques at the lower extremity joints. In this paper, we introduce a 2-D musculoskeletal model of walker-assisted FES-supported walking of paraplegics. Using the developed model and an optimal controller, the stimulation patterns are determined such that the tracking errors of lower joint reference trajectories are minimized and the muscle activations and the handle reaction force (HRF) are reduced. Outputs of the optimal controller are stimulation patterns of the lower body muscles and torque acting on the upper body joints. The results show that the HRF and ground reaction force (GRF) generated by simulation are in agreement with the measured HRF and GRF. Moreover, the results indicate that the simulation-generated stimulation patterns of lower body muscles are in consist with the stimulation patterns reported in the literatures.
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.
Neuro-Muscular Engineering
Abbas Erfanian Omidvar
Volume -2, Issue 1 , July 2005, , Pages 81-92
Abstract
This paper is concerned with developing a force-generating model of electrically stimulated muscle under non-isometric condition. Hill-based muscle models have been the most popular structure. This type of muscle model was constructed as a combination of different independent blocks (i.e., activation ...
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This paper is concerned with developing a force-generating model of electrically stimulated muscle under non-isometric condition. Hill-based muscle models have been the most popular structure. This type of muscle model was constructed as a combination of different independent blocks (i.e., activation dynamics, force-length and force-velocity relations, and series elastic element). The model assumes that the force-length and the force-velocity relations are uncoupled from the activation dynamics. However, some studies suggest that the shapes of the active force-length and the active force-velocity curves change with the level of the activation. Moreover, the "active state" block of the Hill-type model has no physical interpretation. To overcome the limitation of the Hill-type model, we used the multilayer perceptron (MLP) with back-propagation learning algorithm and Radial Basis Function (RBF) network with stochastic gradient learning rule for muscle modeling, where the stimulation signal, muscle length, velocity of length perturbation, and past measured or predicted force constitute the input of the neural model, and the predicted force is the output. Two modes of network operation are of interest: a time-varying network which allows updating the parameters of network to continue after convergence, and a time-invariant neural network with parameters fixed after convergence. The results show that time-varying and time-invariant neural networks would be able to track the muscle force with accuracy up to 99.5% and 95%, respectively. In addition, the results show that the accuracy of muscle force prediction depends on the structure of neural network. The prediction accuracy of RBF network after 1000 training epochs is higher than that of MLP network after 5000 training epochs.
Neuro-Muscular Engineering
Ali Esteki
Volume -1, Issue 1 , June 2004, , Pages 15-23
Abstract
Computer simulation of a three dimensional model of the thumb and index finger was used to perform a sensitivity analysis of each joint position to individual muscle activation level. The results were used to study the effect of each muscle on hand posture and select specific muscles to get a desired ...
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Computer simulation of a three dimensional model of the thumb and index finger was used to perform a sensitivity analysis of each joint position to individual muscle activation level. The results were used to study the effect of each muscle on hand posture and select specific muscles to get a desired posture of the hand to assist the implementation of FNS systems. The hand was treated as a multi-body system including rigid segments connected by joints. Each joint was subjected to a total moment including muscle active and joint passive components. The forward approach, in which the equilibrium equations are solved for joint positions as a function of muscle moments, was used. The results showed that at the base joint of the index finger, flexion effect of the extrinsic flexor muscles was about two times of that of the intrinsic muscles. It was also shown that each muscle of the extensor system is individually more effective than the extrinsic flexor muscles. At the more distal joints, intrinsic muscles acted as feeble extensors. At the base joint of the thumb, extensor muscles were much more powerful than the flexor and flexor effect of adductor muscles. Also, abductor muscles were much more effective than the adductors. It was revealed that flexor muscles of the more distal joints are as strong as the extensor muscles. The conclusions are that: the minimum required muscles for appropriate positioning of the hand and for grasp and applying force to objects are limited.
