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.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hosna Ghandeharion; Abbas Erfanian Omidvar
Volume 3, Issue 3 , June 2009, , Pages 199-211
Abstract
Contamination of Electroencephalographic (EEG) recordings with different kinds of artifacts is the main obstacle to the analysis of EEG data. Independent Component Analysis (ICA) is now a widely accepted tool for detection of artifact in EEG data. This component-based method segregates artifactual activities ...
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Contamination of Electroencephalographic (EEG) recordings with different kinds of artifacts is the main obstacle to the analysis of EEG data. Independent Component Analysis (ICA) is now a widely accepted tool for detection of artifact in EEG data. This component-based method segregates artifactual activities in separate sources hence, the reconstruction of EEG recordings without these sources leads to artifact reduction. Identification of the artifactual components is a major challenge to artifact removal using ICA is the. Although, during past several years, it has been proposed for automatic detecting the artifactual component, there is still little consensus on criteria for automatic rejection of undesired components. In this paper we present a new identification procedure based on statistics and time-frequency properties of independent components for fully automatic ocular artifact suppression. By comparing the statistics and time-frequency properties of independent components, the artifactual components were identified and removed. The results on 2000 4-s EEG epochs indicate that the artifact components can be identified with an accuracy of 92.8%. Moreover, statistical test indicates that the statistics and time-frequency properties of artifactual components are significantly different from that of non-artifactual components.
Neuro-Muscular Engineering
Hamid Reza Kobravi; Abbas Erfanian Omidvar
Volume 2, Issue 4 , June 2008, , Pages 335-349
Abstract
In this paper an adaptive robust fuzzy controller based on sliding mode control (SMC) approach is proposed to control the knee joint position using quadriceps electrical stimulation and it has been tested on three subjects. The proposed method is based on SMC. The main advantage of SMC derives from the ...
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In this paper an adaptive robust fuzzy controller based on sliding mode control (SMC) approach is proposed to control the knee joint position using quadriceps electrical stimulation and it has been tested on three subjects. The proposed method is based on SMC. The main advantage of SMC derives from the property of robustness to system uncertainties and external disturbances. However, a large value has to be applied to the control gain when the boundary of uncertainties is unknown. Unfortunately, this large control gain may cause chattering on the sliding surface and therefore deteriorate the system performance. In this paper a robust control strategy proposed which is based on the combination of sliding mode, fuzzy logic systems, and an adaptive compensator to reduce the system uncertainties while alleviating the effects of chattering. The fuzzy logic system is used to identify the muscle-joint dynamics. The parameters of this fuzzy system were estimated using another fuzzy system. The controller is evaluated through the simulation studies on a virtual patient and experimental studies on intact subjects. The results show that the adaptive robust controller provides an accurate tracking of desired knee-joint angle for different subjects and different days and can generate control signals to compensate the muscle fatigue and reject the external disturbance.
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.
