Neuro-Muscular Engineering
Sahar Akbari; Vahid Shalchyan; Mohammad Reza Daliri
Volume 12, Issue 4 , January 2019, , Pages 277-286
Abstract
Neural spike detection is the first step in the analysis of neural action potentials in extracellular recordings. The background noise which mainly originates from a large number of far neuronal units, usually confront with detection of low-amplitude spikes. So far, many scholars have devoted their works ...
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Neural spike detection is the first step in the analysis of neural action potentials in extracellular recordings. The background noise which mainly originates from a large number of far neuronal units, usually confront with detection of low-amplitude spikes. So far, many scholars have devoted their works to this subject and many algorithms have been proposed. In this paper we present an automatic spike detection algorithm for the noise-contaminated extracellular signal. This algorithm consists of four steps: 1- A bandpass filtering and using a differential filter; 2- applying Shannon's energy nonlinear filter; 3- Hilbert transform; and 4- Thresholding of the signal. The proposed method has been compared with five known methods in spike detection. This comparison is done on two simulated datasets and one real data set. The results indicate the superiority of the proposed method for simulated data compared to other methods, which indicates the robustness of the proposed algorithm to the noise. Meanwhile, for real data, it reaches the second place among all six methods. Using Shannon's non-linear energy filter can be an effective way to detect spikes in extracellular signal recordings. The comparison indicates that this method is superior to the commonly known methods for spike detection.
Neuro-Muscular Engineering
Tahmineh Sadati; Mohammad Reza Daliri
Volume 12, Issue 1 , June 2018, , Pages 1-10
Abstract
A brain-computer interface is a system which works based on the neural activity created by the brain and it has attracted the attention of many researchers in recent years. These interfaces are independent of the usual pathway of peripheral and muscular nerves and are very important because of their ...
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A brain-computer interface is a system which works based on the neural activity created by the brain and it has attracted the attention of many researchers in recent years. These interfaces are independent of the usual pathway of peripheral and muscular nerves and are very important because of their ability to provide a new dimension in communication or control of a device for the disabled persons. The neural activity used in the brain-computer interface can be recorded by various invasive and non-invasive methods and can be converted to the desired signal by different decoding algorithms. In this study, 3 rats were used to perform a movement task which was pressing a key and receiving a drop of water by a mechanical arm for corrected trials. By implanting a 16-channel microelectrode array in the rat's motor cortex during an invasive process, the brain signals are recorded during the task, and simultaneously the signal received by the force sensor is also stored. By performing the necessary preprocessing on spikes and extracting the firing rates of signal as a feature vector by convolving a Gaussian window with the spike trains, the necessary inputs for the decoding algorithm, which is linear regression here, are obtained. Two patterns have been used for cross validation. The first pattern considers 60% of the data from the beginning of the signal as a train set and the remaining 40% of the signal as a test set and the second pattern is the opposite of the first one. Several methods have been used to evaluate the decoding algorithm used in the studies. In this paper, the correlation coefficient and coefficient of determination have been used. The correlation coefficient and coefficient of determination between the desired force and predicted force in linear resgression method, in average of three sessions for three rats, are equal to r=0.56 and =0.20 for the first pattern and r=0.55 and =0.30 for the second pattern respectively. These results show that firing rates of neurons are proper features to predict continous variables such as force. Besides, it can be concluded that linear regression is a suitable method for decoding a motor variable like force and follows the desired signal properly.
Neuro-Muscular Engineering
Amir Masoud Ahmadi; Sepideh Farakhor Seghinsara; Mohamad Reza Daliri; Vahid Shalchyan
Volume 11, Issue 1 , May 2017, , Pages 83-100
Abstract
The brain stimulation and its widespread use is one of the most important subjects in studies of neurophysiology. In brain electrical stimulation methods, following the surgery and electrode implantation, electrodes send electrical impulses to the specific targets in the brain. The use of this stimulation ...
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The brain stimulation and its widespread use is one of the most important subjects in studies of neurophysiology. In brain electrical stimulation methods, following the surgery and electrode implantation, electrodes send electrical impulses to the specific targets in the brain. The use of this stimulation method is provided therapeutic benefits for treatment chronic pain, essential tremor, Parkinson’s disease, major depression, and neurological movement disorder syndrome (dystonia). One area in which advancements have been recently made is in controlling the movement and navigation of animals in a specific pathway. It is important to identify brain targets in order to stimulate appropriate brain regions for all the applications listed above. An animal navigation system based on brain electrical stimulation is used to develop new behavioral models for the aim of creating a platform for interacting with the animal nervous system in the spatial learning task. In the context of animal navigation the electrical stimulation has been used either as creating virtual sensation for movement guidance or virtual reward for movement motivation. In this paper, different approaches and techniques of brain electrical stimulation for this application has been reviewed.
Neuro-Muscular Engineering
Hesam Moradkhani; Vahid Shalchyan
Volume 10, Issue 4 , January 2017, , Pages 325-337
Abstract
P300 Speller as a most commonly used brain–computer interface (BCI) has been able to provide simple communication capabilities for people with severe motor or speech disabilities in order to have a better interaction with the outer world over the past years. Checker-board paradigm introduced by ...
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P300 Speller as a most commonly used brain–computer interface (BCI) has been able to provide simple communication capabilities for people with severe motor or speech disabilities in order to have a better interaction with the outer world over the past years. Checker-board paradigm introduced by Townsend et al. [1] is one of the most practical alternatives for row-column paradigm, enhancing the performance of the speller by preventing row-column induced errors. In this study, we investigated the effect of substituting presentation of an emoji stimulus instead of flashing the characters in the performance of a checker-board P-300 speller. The performance of the proposed paradigm was evaluated and compared to the traditional stimuli in checker-board paradigm in an online experiment over ten healthy subjects. For each paradigm, the recorded data from an offline session was used to calibrate the speller classifier; and consequently, the classification accuracy was calculated over online sessions. The proposed paradigm, showed 14% enhancement in classification accuracy with respect to the checker-board paradigm. The results of this study obviously showed that the stimuli obtained by presenting emoji instead of character flashing, effectively improved the speller classification accuracy.
Neuro-Muscular Engineering
Mohsen Abedi; Majid Mohammadi Moghaddam; Mohammad Firoozabadi
Volume 9, Issue 1 , April 2015, , Pages 33-48
Abstract
In this paper the simulation of pathological behavior in gait locomotion of central nervous system (CNS) diseases and effects of rehabilitation techniques are investigated. These simulations noticeably deepen the knowledge of researches in neurorehabilitation realm about the neuroscientific basis of ...
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In this paper the simulation of pathological behavior in gait locomotion of central nervous system (CNS) diseases and effects of rehabilitation techniques are investigated. These simulations noticeably deepen the knowledge of researches in neurorehabilitation realm about the neuroscientific basis of CNS treatment after a neural disorder. However, only a limited number of these simulations have been proposed in the previous works that issued some aspects of CNS diseases. Due to this limitation, in this paper, a more efficient simulation has been done on pathological behavior of neural disorders with including the brain signal disruption in the models. To do this, combinations of neural reflexes and central pattern generator (CPG) has been incorporated in the neuromuscular system and examined on two different msculoskeletal system containing two leg-one segment and one leg-two segment systems. Then, the locomotion of hemiplegia and paraplegia patients are simulated by inserting a malfunction in the supraspinal signal coming into the CPG. Moreover, the effects of rehabilitation effects on paraplegia patients have been investigated and qualititatively compared to the experimental data.
Neuro-Muscular Engineering
Reza Hajian; Farzad Towhidkhah
Volume 8, Issue 1 , March 2014, , Pages 19-29
Abstract
Tremor is one of the most frequent movement disorders which is involuntary and approximately sinusoidal. It affects various body joints such as elbow. Tremor on an elbow is considered as extension, flection, and rotation of the forearm. There are miscellaneous types of treatments for tremor one of which ...
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Tremor is one of the most frequent movement disorders which is involuntary and approximately sinusoidal. It affects various body joints such as elbow. Tremor on an elbow is considered as extension, flection, and rotation of the forearm. There are miscellaneous types of treatments for tremor one of which is electrical stimulation. In this research, we study existing stimulation methods in order to reduce tremor and control stimulation pulses. It should be notified that studying these methods requires 1- an electrical stimulator so that one can run natural experiments and estimate the validity of the model, 2- a skeletal- neuromuscular model in order to study the tremor dynamics and the system simulation, and 3- determining an appropriate stimulation scheme and control method in order that one can control the stimulation parameters to reduce tremor. The antagonist muscle stimulation technique for reducing tremor is in the form of either muscle co-contraction or anti-phase stimulation. In the former method, considering the fact that the time-dependent system has time-delay, disturbance, and non-linearities, a robust controller is needed. Hence, in this study, we take advantage of MPC controller because of its features. The results show that MPC controller is more satisfactory than the PID and fuzzy ones used in previous works and also demonstrate that one can theoretically reduce tremor by applying appropriate electrical stimulation.
Neuro-Muscular Engineering
Sahar Babaei; Ali Maleki
Volume 8, Issue 1 , March 2014, , Pages 57-68
Abstract
Nowadays real time motion tracking have been receiving considerable attention in many applications and research fields such as rehabilitation, medicine and treatment. Recently MEMS accelerometers play an important role to attend desired result for these applications. This paper presents a new design ...
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Nowadays real time motion tracking have been receiving considerable attention in many applications and research fields such as rehabilitation, medicine and treatment. Recently MEMS accelerometers play an important role to attend desired result for these applications. This paper presents a new design for angle measurement device based on accelerometer sensor and Bluetooth module. Using Bluetooth module in addition to providing minimally obtrusive recording, allows you to connect to your personal computer and mobile quicker and easier. This system has made up of 2 complete 3 axis accelerometer ADXL330, which by giving sufficient data in 3D space allows us to investigate joint angle with DCMR method. The mentioned method in dynamic recording remarkably has less error in comparison to CMR method. As one application for this system, determination of elbow joint angle is studied. Eventually experimental recording of elbow joint angle in static and dynamic condition was done by applying CMR method. With reference to electrogoniometer output the maximum static and dynamic error were obtained respectively 3 and 6.1 degrees.
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
Ali Falaki; Farzad Towhidkhah
Volume 5, Issue 2 , June 2011, , Pages 127-141
Abstract
Based on previous studies, human motor control system may apply two control strategies, impedance control and model based control, for learning motor skills and counteracting environmental instabilities. Since interaction among these controllers is not fully studied, the investigation of impedance and ...
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Based on previous studies, human motor control system may apply two control strategies, impedance control and model based control, for learning motor skills and counteracting environmental instabilities. Since interaction among these controllers is not fully studied, the investigation of impedance and model based controllers function during learning period seems desirable. In this study a supervisory controller was used to coordinate the model based and impedance controllers. Coordinating model based controller and impedance controller by using supervisory unit will result in simultaneously adjustment of forward motor command and joint stiffness. In order to evaluate performance of the suggested model, it was applied to arm reaching movements in the presence of external force fields. Results showed that both suitable impedance values and a proper internal model are required to fulfill movements similar to those of humans under different circumstances. Research has shown that central nervous system is able to purposefully modulate arm impedance to counteract environmental disturbances. This study showed that beside this modulation, the maximum motor learning may occur in direction with the least impedance and the most kinematic error. It also concluded that confronting abrupt changes in disturbance, the system managed to decrease error without learning the new dynamic using previous knowledge by supervisory system. A part of this compensation is due to stiffness variations and another part is due to decreasing the influence of model based controller.
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.
Neuro-Muscular Engineering
Amir Hosein Eskandari; Ehsan Sedaghat Nejad; Seyed Javad Mousavi; Mohsen Asghari; Mohammad Parnianpour
Volume 5, Issue 3 , June 2011, , Pages 257-273
Abstract
Selection of muscle activation pattern to reach a specific goal by considering the complexities of neuromuscular system and the way it overcomes these complications, is of researchersinterest in motor control. One proposed solutionfor resolving thesecomplexities is the concept of simple module (synergies) ...
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Selection of muscle activation pattern to reach a specific goal by considering the complexities of neuromuscular system and the way it overcomes these complications, is of researchersinterest in motor control. One proposed solutionfor resolving thesecomplexities is the concept of simple module (synergies) that the combination of them leads to more complex activities. In the present work, the existence and arrangement of synergies in the lumbar spine are proved. For this purpose, a model with 18-muscles in level L4-L5 is utilized in the static condition. In order to obtaina muscular and stability synergies, muscle activation, which are obtained by exerting moments in 2D and 3D spaces and angular stiffness to the model,are used. The results show that six muscular synergies suffice to be able to reach any point in the moment space. Also, three stability synergies can reconstruct a part of joint angular stiffness space. In addition, the obtained muscular synergies are robust against changes in the amplitude of exerted moment. In this study, it is shown that one can generates any task involves producing determined moment and angular stiffness in the joint, by combining muscular and stability synergies together.
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
Davoud Naderi; Mohsen Sadeghi Mehr; Nader Farahpour; Behnam Miripour-Fard
Volume 2, Issue 2 , June 2008, , Pages 85-93
Abstract
Cognition of human postural responses can provide valuable insight on the control of stability. Researchers can use this finding to design rehabilitation exercises to improve the patients, balance. This study was done with the aim of conducting theoretical and experimental investigations on human response ...
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Cognition of human postural responses can provide valuable insight on the control of stability. Researchers can use this finding to design rehabilitation exercises to improve the patients, balance. This study was done with the aim of conducting theoretical and experimental investigations on human response to tilting base plate in the sagittal plane. A four-segment model with three degrees of freedom was used as a biomechanical model of human body and its motion was studied in the sagittal plane. The postures of model were found by optimization technique such that the stability of model to be optimum. Zero moment point stability criterion was applied to find the optimum posture against the tilting base plate. To verify the theoretical results experimentally, the stability measure device was designed and manufactured. In several trials, the responses of ten male healthy persons standing on a tilting platform under perturbations were recorded by using the motion analysis system. Through data analysis, the response of each subject was surveyed and the experimental and theoretical results were compared. Both the experimental and theoretical results showed that the human central nervous system evokes the ankle strategy to keep its balance under tilting base plate conditions. A good coincident between the experimental results and theoretical predictions was observed, indicating that the model basis optimization method can be well relied upon to predict the human joints angle trajectories in response to base plate tilting.
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
Mehrak Mahmoudi; Mohammad Jafar Abd Khodaei; Saeide Khatibirad
Volume -2, Issue 1 , July 2005, , Pages 9-16
Abstract
A mathematical model is presented for simulation of neurotransmitter release in the synaptic cleft of a neuromuscular junction. Chaudhuri's model is improved by adding calcium diffusion effect on the neurotransmitter release. When an action potential occurs, the calcium channels on presynaptic membrane ...
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A mathematical model is presented for simulation of neurotransmitter release in the synaptic cleft of a neuromuscular junction. Chaudhuri's model is improved by adding calcium diffusion effect on the neurotransmitter release. When an action potential occurs, the calcium channels on presynaptic membrane will open and allow calcium ions to enter in presynaptic terminal. Then, these ions diffuse between calcium channels and release sites, where clearance mechanisms remove some of them. The model is defined by some partial differential equations which are solved by numerical methods. Solving these equations, the temporal changes of calcium concentration in the release sites and the amount of neurotransmitter release at each time are obtained. Finally, the effect of two consecutive action potential pulses on the above mechanisms is studied.
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
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.
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.
