Identifying the Dynamics of Leg Muscle Activation During Human Gait Using Neural Oscillator and Fuzzy Compensator

Introduction: The goal of this study is to design a model in order to predict the muscle activation pattern because the muscle activation patterns contain valuable information about the muscle dynamics and movement patterns. Therefore, the goal of the presentation of this neural model is to identify the desired muscle activation patterns by Hopf chaotic oscillator during walking. Since the knee muscles activation has a significant effect on the movement pattern during walking, the main concentration of this study is to identify the knee muscles activation dynamics using a modeling technique.Material and Method: The EMG recording obtained from 5 healthy subjects that electrodes positioned on the Tibialis- Anterior and Rectus- Femoris muscles on every two feet. In the proposed model, along with the chaotic oscillator, a fuzzy compensator was designed to face the unmolded dynamics. In fact, on the condition, the observed difference between the desired and actual activation patterns violate some specific quantitative ranges, the fuzzy compensator based on predefined rules modify the activity pattern produced by the Hopf oscillator.Results: To evaluate the results, some quantitative measures used. According to the achieved results, the proposed model could generate the trajectories, dynamics of which are similar to the muscle activation dynamics of the studied muscles. In this model, the generated activity pattern by the proposed model cannot follow the desired activity of the Tibialis- Anterior muscle as well as Rectus- Femoris muscle.  Conclusion: The similarity between the generated activity pattern by the model and the activation dynamics of Rectus- Femoris muscle was more considerable. In other words, based on the recorded human data, the activation pattern of the Rectus- Femoris is more similar to a rhythmic pattern

Movement Stabilizing Using Afferent Control of Spinal Locomotor CPG Using Chaotic Takagi-Sugeno Fuzzy Logic Systems: A Simulation Study

 Background: External control of the function of the central pattern generators (CPGs), exist in the spinal cord, is possible by electrical or chemical stimulation of some of the spinal Afferents. After restarting the activity of spinal cord CPG, the dynamics of movements such as gait can be changed through the time by controlling the rhythm of the CPG.Methods: The purpose of this study was provision of closed loop control algorithm based on the Takagi-Sugeno fuzzy controller in order to adjust the weight of the spinal cord afferents in a neuro-mechanical model with the aim of controlling the rhythm of the CPGs. Rhythm control of CPGs has been done with the aim of implementing the process of resetting the phase during gait in order to stabilize the movement against external disturbances. In this paper, the efficiency of a continuous Takagi-Sugeno fuzzy controller with the efficiency of 2 fuzzy Takagi-Sugeno chaotic controllers has been compared.Results: It was shown by the results obtained from the simulation that the process of resetting the motion phase of the skeletal angle in face of applying disturbance in method of chaotic Takagi-Sugeno fuzzy controller is done with good features in the presence of delayed feedback in decreasing overshoot and undershoot to the amount of 1.982 and 0.17 radians, respectively so that the best amount of afferent to reset the phase and return to the desired angle is provided at the shortest possible time.Conclusion: In this paper, the efficiency of a continuous Takagi-Sugeno fuzzy controller with the efficiency of 2 fuzzy Takagi-Sugeno chaotic controllers has been compared for movement stabilization using spinal cord afferent control. According to the results, the best performance was observed when the chaotic fuzzy Takagi-Sugenocontroller in the presence of delayed feedback was used

A Robust Nonlinear Control Strategy for Unsupported Paraplegic Standing Using Functional Electrical Stimulation: Controller Synthesis and Simulation

Background: Functional electrical stimulation (FES) is known as a promising technique for movement generation in the paralyzed limbs through electrical stimulation of the muscle nerves. This paper focuses on the FES based control of upright standing in paraplegic patients. In this study a new approach for controlling the upright posture has been proposed. The posture control strategies proposed in the previous works were based on controlling the angular joint position, and none of them were focused on controlling the CoP dynamics directly. Since the CoP is representative of posture balance dynamics, in this study the adopted FES based control strategy was designed to control the CoP dynamics directly.Method: In the proposed strategy, the controller has determined the stimulation intensity of ankle muscles in a manner to restrict the center of pressure (CoP) in a specific zone to guarantee the posture balance during unsupported standing. The proposed approach is based on a new cooperative based combination between two different controllers. Utilizing this strategy, until the CoP is confined within the stable zone, an adaptive controller is active and tries to preserve the posture stability. When the CoP goes out the stable zone, sliding mode control, as a nonlinear control technique presenting remarkable properties of robustness, is activated and tries to back the CoP within the preference zone. In this manner, not only the posture balance can be guaranteed but also the balance dynamics can be similar to the elicited dynamic postural behavior in the normal subjects.Results: Extended evaluations carried out through the simulation studies on a musculoskeletal model. According to the achieved results, the proposed control strategy is not only robust against the external disturbances but also insensitive to the initial postural conditions.Conclusion: The achieved results prove the acceptable performance of the proposed control strategy

A Hidden Markov Model Based Detecting Solution for Detecting the Situation of Balance During Unsupported Standing Using the Electromyography of Ankle Muscles

Quiet Standing; Hidden Markov Model; Electromyography; Dynamic Balance.: In this study, three detecting approaches have been proposed and evaluated for online detection of balance situations during quiet standing. The applied methods were based on electromyography of the gastrocnemius muscles adopting the hidden Markov models.Methods: The levels of postural stability during quiet standing were regarded as the hidden states of the Markov models while the zones in which the center of pressure lies within determines the level of stability. The Markov models were trained by using the well-known Baum-Welch algorithm. The performance of a single hidden Markov model, the multiple hidden Markov model, and the multiple hidden Markov model alongside an adaptive neuro-fuzzy inference system (ANFIS), were compared as three different detecting methods.Results: The obtained results show the better and more promising performance of the method designed based on a combination of the hidden Markov models and optimized neuro-fuzzy system.Conclusion: According to the results, using the combined detecting method yielded promising results

The Effect of Intraspinal Micro Stimulation With Variable Stimulating Pattern in Adult Rat With Induction of Spinal Cord Injury in the Treatment of Spinal Cord Injuries

Background: Spinal cord injury is one of the diseases that, no specific treatment has yet found despite the variety of works that have done in this field. Different approaches to treat such injuries have investigated today. One of them is invasive intra-spinal interventions such as electrical stimulation. Therefore, in this study, the effect of the protocol for intra-spinal variable and fixed electrical stimulation has been investigated in order to recover from spinal cord injury. Methods: In the study, 18 Wistar male rats randomly divided into Three groups, including intra-spinal electrical stimulation (IES), IES with variable pattern of stimulation (VP IES) and a sham group. Animals initially subjected to induced spinal cord injury. After one week, the animal movement was recorded on the treadmill during practice using a camera and angles of the ankle joint were measured using the Tracker software. Then, the obtained data were analyzed by nonlinear evaluations in the phase space. Results: The motion analyses and kinematic analyses were carried out on all groups. According to the achieved results, the gait dynamics of the VP IES group has the most conformity to the gait dynamics of the healthy group. Also, the best quality of the balance preservation observed in the VP IES group. Conclusion: It can be concluded that the IES with variable pattern of stimulation along with exercise therapy has significant gait restorative effects and increases the range of motion in rats with induced spinal cord injury

A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model

Background: Animal models of absence epilepsy are widely used in childhood absence epilepsy studies. Absence seizures appear in the brain’s electrical activity as a specific spike wave discharge (SWD) pattern. Reviewing long-term brain electrical activity is time-consuming and automatic methods are necessary. On the other hand, nonlinear techniques such as phase space are effective in brain electrical activity analysis. In this study, we present a novel SWD-detection framework based on the geometrical characteristics of the phase space.Methods: The method consists of the following steps: (1) Rat stereotaxic surgery and cortical electrode implantation, (2) Long-term brain electrical activity recording, (3) Phase space reconstruction, (4) Extracting geometrical features such as volume, occupied space, and curvature of brain signal trajectories, and (5) Detecting SDWs based on the thresholding method. We evaluated the approach with the accuracy of the SWDs detection method.Results: It has been demonstrated that the features change significantly in transition from a normal state to epileptic seizures. The proposed approach detected SWDs with 98% accuracy.Conclusion: The result supports that nonlinear approaches can identify the dynamics of brain electrical activity signals

Prediction of the wrist joint position during a postural tremor using neural oscillators and an adaptive controller

The prediction of the joint angle position, especially during tremor bursts, can be useful for detecting, tracking, and forecasting tremors. Thus, this research proposes a new model for predicting the wrist joint position during rhythmic bursts and inter-burst intervals. Since a tremor is an approximately rhythmic and roughly sinusoidal movement, neural oscillators have been selected to underlie the proposed model. Two neural oscillators were adopted. Electromyogram (EMG) signals were recorded from the extensor carpi radialis and flexor carpi radialis muscles concurrent with the joint angle signals of a stroke subject in an arm constant-posture. The output frequency of each oscillator was equal to the frequency corresponding to the maximum value of power spectrum related to the rhythmic wrist joint angle signals which had been recorded during a postural tremor. The phase shift between the outputs of the two oscillators was equal to the phase shift between the muscle activation of the wrist flexor and extensor muscles. The difference between the two oscillators′ output signals was considered the main pattern. Along with a proportional compensator, an adaptive neural controller has adjusted the amplitude of the main pattern in such a way so as to minimize the wrist joint prediction error during a stroke patient′s tremor burst and a healthy subject′s generated artificial tremor. In regard to the range of wrist joint movement during the observed rhythmic motions, a calculated prediction error is deemed acceptable

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively