Analysis Of Electromyographic Signals Research Articles

Recent Advances of Stretchable Nanomaterial-Based Hydrogels for Wearable Sensors and Electrophysiological Signals Monitoring.

Electrophysiological monitoring is a commonly used medical procedure designed to capture the electrical signals generated by the body and promptly identify any abnormal health conditions. Wearable sensors are of great significance in signal acquisition for electrophysiological monitoring. Traditional electrophysiological monitoring devices are often bulky and have many complex accessories and thus, are only suitable for limited application scenarios. Hydrogels optimized based on nanomaterials are lightweight with excellent stretchable and electrical properties, solving the problem of high-quality signal acquisition for wearable sensors. Therefore, the development of hydrogels based on nanomaterials brings tremendous potential for wearable physiological signal monitoring sensors. This review first introduces the latest advancement of hydrogels made from different nanomaterials, such as nanocarbon materials, nanometal materials, and two-dimensional transition metal compounds, in physiological signal monitoring sensors. Second, the versatile properties of these stretchable composite hydrogel sensors are reviewed. Then, their applications in various electrophysiological signal monitoring, such as electrocardiogram monitoring, electromyographic signal analysis, and electroencephalogram monitoring, are discussed. Finally, the current application status and future development prospects of nanomaterial-optimized hydrogels in wearable physiological signal monitoring sensors are summarized. We hope this review will inspire future development of wearable electrophysiological signal monitoring sensors using nanomaterial-based hydrogels.

Optimized k-nearest neighbors for classification of prosthetic hand movements using electromyography signal

Electromyography (EMG) signals are essential, as they are used to measure muscular activity in different parts of the human body. The measurement and analysis of EMG signal lead to various applications of muscle disorders such as muscular dystrophy, myopathy, hand movements, etc. In this paper, an improved and effective hand movement classification model is developed for amputee subjects. It includes: (1) EMG feature extraction using Discrete Wavelet Transform (DWT), (2) EMG feature selection using binary Global Best Guided Gaussian Artificial Bee Colony (BGGABC), (3) Hand movements classification using Optimized k-nearest neighbors (OKNN) classifier. The EMG signal is taken from the DB3 of NinaPro dataset comprising 17 different prosthetic hand movements recorded from 11 amputee subjects. Thereafter, DWT is applied to decompose the EMG signal for extracting features. An improved wrapper-based feature selection technique (BGGABC) is used to select the optimal feature subset for effective classification. The two variants of KNN, i.e. Smallest Modified KNN and Largest Modified KNN are taken in which item strength to a class is optimized for efficient classification. The strength of an item to a class depends on distance and weight of an item to a class. Therefore, a multi-objective Non-dominated sorting genetic algorithm-II (NSGA-II) is used for optimizing these two contradictory parameters (distance and weight) simultaneously to have optimized variants, namely: Optimized Smallest KNN (OSKNN) and Optimized Largest KNN (OLKNN). Extensive results show that the proposed method OKNN achieved the highest classification accuracy of 93.07% (OLKNN) and 89.43% (OSKNN) compared with KNN variants and competitors.

Research Progress in Data Acquisition and Intelligent Sensing Methods for Lumbar Electromyographic Signals

Population aging trend is taking place in our country, and low back pain is a symptom of neuromuscular diseases of concern in the elderly. Accurately analyzing the disease of low back pain is important for both timely intervention and rehabilitation of patients. As a kind of bioelectrical signal, the acquisition and analysis of lumbar electromyography (EMG) signal is an important direction for the study of low back pain. The study reviews the acquisition of lumbar EMG by different types of sensors, introduces the signal characteristics of needle electrodes, surface electromyography electrodes and array electrodes, describes the use of signal algorithms, points out that wireless sensors and the use of deep learning algorithms are the direction of development, and puts forward prospects for its further development.

Low Complex CORDIC-based Hand Movement Recognition Design Methodology for Rehabilitation and Prosthetic Applications.

Hand movement recognition using Electromyography (EMG) signals have gained much significance lately and is extensively used for rehabilitation and prosthetic applications including stroke-driven disability and other neuromuscular disorders. Herein, quantitative analysis of EMG signals is very crucial. However, such applications are constrained by power consumption limitations due to the battery backup necessitating low-complex system design and the on-chip area requirement. Existing hand movement recognition methodologies using single-channel EMG signal involve computationally intensive stages, including Ensemble Empirical Mode Decomposition (EEMD), Fast Independent Component Analysis (FastICA), feature extraction, and Linear Discriminant Analysis (LDA) classification, which can not be mapped onto the low-complex architecture directly from the algorithmic level. The high computational complexity of LDA classification makes it difficult to be used for low-complex applications. In this paper, we introduce a low-complex CORDIC-based hand movement recognition design methodology targeting resource-constrained rehabilitation applications. This work explores replacing LDA classification with K-Means clustering due to its reduced complexity and efficient clustering algorithm. CORDIC-based K-Means clustering is used to further reduce the overall computational complexity of the system. The proposed low complex, K-Means clustering-based hand movement recognition for classifying seven hand movements using single-channel EMG data is found to be 99.77 % less complex and 1.28% more accurate than the conventional LDA-based classification.

A Proposal to Analyze Muscle Dynamics Under Fatiguing Contractions Using Surface Electromyography Signals and Fuzzy Recurrence Network Features

The analysis of surface electromyography (sEMG) signals is significant in the detection of muscle fatigue. These signals exhibit a great degree of complexity, nonlinearity, and chaos. Also, presence of high degree of fluctuations in the signal makes its analysis a difficult task. This study aims to analyze the nonlinear dynamics of muscle fatigue conditions using Fuzzy recurrence networks (FRN). Dynamic sEMG signals are measured from biceps brachii muscle of 45 normal subjects referenced to 50% of maximal voluntary contractions (MVC) for this. Recorded signals are then pre-processed and divided into ten equal parts. FRNs are transformed from the signals. The network features, namely average weighted degree (AWD) and Closeness centrality (CC) are extracted to analyze the muscle dynamics during fatiguing conditions. The decrease in these features during fatigue indicates a reduction in signal complexity and an increase in complex network stiffness. Both AWD and CC features are statistically significant with [Formula: see text]. Further, these features are classified using Naïve Bayes (NB), k nearest neighbor (kNN) and random forest (RF) algorithms. Maximum accuracy of 96.90% is achieved using kNN classifier for combined FRN features. Thus, the proposed features provide high-quality inputs to the neural networks that may be helpful in analyzing the complexity and stiffness of neuromuscular system under various myoneural conditions.

Long-Term Bilateral Neuromuscular Function and Knee Osteoarthritis after Anterior Cruciate Ligament Reconstruction.

Neuromuscular function is thought to contribute to posttraumatic osteoarthritis (PTOA) risk in anterior cruciate ligament (ACL)-reconstructed (ACLR) patients, but sensitive and easy-to-use tools are needed to discern whether complex muscle activation strategies are beneficial or maladaptive. Using an electromyography (EMG) signal analysis technique coupled with a machine learning approach, we sought to: (1) identify whether ACLR muscle activity patterns differed from those of healthy controls, and (2) explore which combination of patient outcome measures (thigh muscle girth, knee laxity, hop distance, and activity level) predicted the extent of osteoarthritic changes via magnetic resonance imaging (MRI) in ACLR patients. Eleven ACLR patients 10-15 years post-surgery and 12 healthy controls performed a hop activity while lower limb muscle EMG was recorded bilaterally. Osteoarthritis was evaluated based on MRI. ACLR muscle activity patterns were bilaterally symmetrical and differed from those of healthy controls, suggesting the presence of a global adaptation strategy. Smaller ipsilateral thigh muscle girth was the strongest predictor of inferior MRI scores. The ability of our EMG analysis approach to detect meaningful neuromuscular differences that could ultimately be related to thigh muscle girth provides the foundation to further investigate a direct link between muscle activation patterns and PTOA risk.

P28: Analysis of Surface Electromyography (sEMG) Signals for Motion Signatures in Strength Training Exercises via Stretchable Wearable Electronic Sensors

Background: The characterization of commonly used strength training exercises has a range of implications for analysis of techniques displaying proper form and injury prevention in both sports and clinical rehabilitation settings. This study examines the ability of wearable sensors to effectively determine the degree of activation of different muscle groups using surface electromyography (sEMG) signals. The interpretation of these signals across muscle groups within an exercise allow for the characterization of their motion signatures. Wearable sensors with embedded electrodes (BioStamps RC, MC10 Inc.) were applied to two defined muscle groups for three exercises: a triceps push-up (PU), a biceps curl (BC) and an unweighted squat (US). Methods: Seven healthy volunteers performed three consecutive exercises in the following order; PU, BC, and US. Each exercise was performed for five repetitions with BioStamps applied to the distal and proximal ends of the subjects’ dominant side using adhesive bandages. For each of the defined exercises the investigator played a timed voice recording in which the subjects were instructed to perform the series of repetitions with consistent timing. Sensors automatically upload de-identified data to a tablet device during use. The following parameters are reported from the stamp: electromyographic data (of the muscle causing abduction and adduction) and acceleration (of the distal and proximal ends of the limb which is being abducted/adducted). The data was uploaded to Python and analyzed using a rolling average. P-values were then determined using a one-tailed t-test. Results: Each of the three strength exercises had two identifiable motion signatures, one from each muscle group, derived from the sEMG data which shows the peak muscle activation during the 5 repetitions of an exercise (Figure 1, E). Placement on the BioStamps on the TB and BB recorded activation in the PU and BC (Figure 1, A & B). There is a difference between the TB and BB activation in the PU (p=0.358) but no difference was found between their activation in the BC (p=0.06). This finding was limited by the small sample size (n=7), which would be improved in a future study. Placement of the BioStamps on the BF and RF was used in the unweighted squat (Figure 1, C & D). There is a difference in the RF and BF activation in the US (p=0.179). Discussion: By nature of the antagonistic motion of the triceps brachii (TB) and biceps brachii (BB) in a push-up, activation levels measured by sEMG recordings are expected to be similar. The TB acts as one of the primary movers, while the BB acts as an antagonist and stabilizes the movement to allow for a controlled push-up. Similarly, in a squat similar levels of activation were expected in the rectus femoris (RF) and the biceps femoris (BF) as the RF functions as a primary mover and the BF as a stabilizer. Activation of both the primary mover and stabilizing muscles is important for proper form and function.Figure 1. A & B. Placement of BioStamps on the triceps brachii and biceps brachii, muscles used in the biceps curl (p=0.06) and triceps push-up (p=0.358). C & D. Placement of BioStamps on the rectus femoris and the biceps femoris, muscles used in the unweighted squat (p=0.179). E. Unweighted squat sEMG graph showing muscle activation (mV) of rectus femoris over time (s).

Predicting muscle fatigue during dynamic contractions using wavelet analysis of surface electromyography signal

Muscle fatigue is defined as a reduction in the capability of muscle to exert force or power. Although surface electromyography (sEMG) signals during exercise have been used to assess muscle fatigue, analyzing the sEMG signal during dynamic contractions is difficult because of the many signal distorting factors such as electrode movements, and variations in muscle tissue conductivity. Besides the non-deterministic and non-stationary nature of sEMG in dynamic contractions, no fatigue indicator is available to predict the ability of a muscle to apply force based on the sEMG signal properties.In this study, we designed and manufactured a novel wearable sensor system with both sEMG electrodes and motion tracking sensors to monitor the dynamic muscle movements of human subjects. We detected the state of muscle fatigue using a new wavelet analysis method to predict the maximum isometric force the subject can apply during dynamic contraction.Our method of signal processing consists of four main steps. 1- Segmenting sEMG signals using motion tracking signals. 2- Determine the most suitable mother wavelet for discrete wavelet transformation (DWT) based on cross-correlation between wavelets and signals. 3- Deoinsing the sEMG using the DWT method. 4- Calculation of normalized energy in different decomposition levels to predict maximal voluntary isometric contraction force as an indicator of muscle fatigue.The monitoring system was tested on healthy adults doing biceps curl exercises, and the results of the wavelet decomposition method were compared to well-known muscle fatigue indices in the literature.

Effects of Muscle Fatigue and Recovery on the Neuromuscular Network after an Intermittent Handgrip Fatigue Task: Spectral Analysis of Electroencephalography and Electromyography Signals

Mechanisms underlying exercise-induced muscle fatigue and recovery are dependent on peripheral changes at the muscle level and improper control of motoneurons by the central nervous system. In this study, we analyzed the effects of muscle fatigue and recovery on the neuromuscular network through the spectral analysis of electroencephalography (EEG) and electromyography (EMG) signals. A total of 20 healthy right-handed volunteers performed an intermittent handgrip fatigue task. In the prefatigue, postfatigue, and postrecovery states, the participants contracted a handgrip dynamometer with sustained 30% maximal voluntary contractions (MVCs); EEG and EMG data were recorded. A considerable decrease was noted in EMG median frequency in the postfatigue state compared with the findings in other states. Furthermore, the EEG power spectral density of the right primary cortex exhibited a prominent increase in the gamma band. Muscle fatigue led to increases in the beta and gamma bands of contralateral and ipsilateral corticomuscular coherence, respectively. Moreover, a decrease was noted in corticocortical coherence between the bilateral primary motor cortices after muscle fatigue. EMG median frequency may serve as an indicator of muscle fatigue and recovery. Coherence analysis revealed that fatigue reduced the functional synchronization among bilateral motor areas but increased that between the cortex and muscle.

Generation and analysis of synthetic surface electromyography signals under varied muscle fiber type proportions and validation using recorded signals.

The magnitude and duration of muscle force production are influenced by the fiber type proportion. In this work, surface electromyography (sEMG) signals of muscles with varied fiber type proportions, are generated. For this, relevant components of existing models reported in various literature have been adopted. Also, a method to calculate the motor unit size factor is proposed. sEMG signals of adductor pollicis (AP) and triceps brachii (TB) muscles are simulated from the onset of force production to muscle fatigue state at various percentages of maximal voluntary contraction (MVC) values. The model is validated using signals recorded from these muscles using well-defined isometric exercise protocols. Root mean square and mean power spectral density values extracted from the simulated and recorded signals are found to increase for TB and decrease for AP with time. A linear variation of the features with %MVC values is obtained for simulated and experimental results. The Bland-Altman plot is used to analyze the agreement between simulated and experimental feature values. Good agreement is obtained for the feature values at various %MVCs. The mean endurance time calculated using the model is found to be comparable to that of the experimental value. This method can be used to generate sEMG signals of different muscles with varying fiber type ratios under various neuromuscular conditions.

Nonlinear analysis of biceps surface EMG signals for chaotic approaches

In the present study, the chaos is detected and quantified in the electromyographic (EMG) signal of the biceps muscle using nonlinear features. Biological systems are complex and nonlinear systems composed of a large number of interconnected elements. A thorough understanding of the behavior of these systems, which are sometimes chaotic, will lead to an understanding of the behavior of diseases, finding effective treatments, and using appropriate rehabilitation equipment. Therefore, designing a novel stimulation equation based on Rossler nonlinear dynamics causes chaos in the biceps EMG signal. This stimulation, the sitting and hand positioning is designed in such a way to prevent muscle fatigue and saturated stimulability and also to ensure that with minimal stimulation of the musculocutaneous nerve, there is a maximum activity in the biceps muscle. Then, each signal was divided into chaotic and non-chaotic parts with the positive largest Lyapunov exponent (LLE), and its nonlinear and chaotic criteria, including fractal dimension (FD) using Petrosian method, correlation dimension (CD), for both parts of the EMG signal were calculated as first time. Then, after applying the values of these criteria as features to the support vector machine (SVM) classifier, the separation was carried out using a linear kernel. The results showed that if the classification features include the two features of FD and CD, they are separated from non-chaotic EMG signals with 98.97 % accuracy. Finally, for the first time the extracted features of CD, FD are a good representation of the chaotic behavior of the biceps EMG signal.

Fuzzy inference system (FIS) - long short-term memory (LSTM) network for electromyography (EMG) signal analysis

A wide range of application domains,s such as remote robotic control, rehabilitation, and remote surgery, require capturing neuromuscular activities. The reliability of the application is highly dependent on an ability to decode intentions accurately based on captured neuromuscular signals. Physiological signals such as Electromyography (EMG) and Electroencephalography (EEG) generated by neuromuscular activities contain intrinsic patterns for users’ particular actions. Such actions can generally be classified as motor states, such as Forward, Reverse, Hand-Grip, and Hand-Release. To classify these motor states truthfully, the signals must be captured and decoded correctly. This paper proposes a novel classification technique using a Fuzzy Inference System (FIS) and a Long Short-Term Memory (LSTM) network to classify the motor states based on EMG signals. Existing EMG signal classification techniques generally rely on features derived from data captured at a specific time instance. This typical approach does not consider the temporal correlation of the signal in the entire window. This paper proposes an LSTM with a Fuzzy Logic method to classify four major hand movements: forward, reverse, raise, and lower. Features associated with the pattern generated throughout the motor state movement were extracted by exploring published data within a given time window. The classification results can achieve a 91.3% accuracy for the 4-way action (Forward/Reverse/GripUp/RelDown) and 95.1% (Forward/Reverse Action) and 96.7% (GripUp/RelDown action) for 2-way actions. The proposed mechanism demonstrates high-level, human-interpretable results that can be employed in rehabilitation or medical-device industries.

LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals

Currently, research on gesture recognition systems has been on the rise due to the capabilities these systems provide to the field of human–machine interaction, however, gesture recognition in prosthesis and orthesis has been carried out through the use of an extensive amount of channels and electrodes to acquire the EMG (Electromyography) signals, increasing the cost and complexity of these systems. The scientific literature shows different approaches related to gesture recognition based on the analysis of EMG signals using deep learning models, highlighting the recurrent neural networks with deep learning structures. This paper presents the implementation of a Recurrent Neural Network (RNN) model using Long-short Term Memory (LSTM) units and dense layers to develop a gesture classifier for hand prosthesis control, aiming to decrease the number of EMG channels and the overall model complexity, in order to increase its scalability for embedded systems. The proposed model requires the use of only four EMG channels to recognize five hand gestures, greatly reducing the number of electrodes compared to other approaches found in the literature. The proposed model was trained using a dataset for each gesture EMG signals, which were recorded for 20 s using a custom EMG armband. The model reached an accuracy of to 99% for the training and validation stages, and an accuracy of 87 ± 7% during real-time testing. The results obtained by the proposed model establish a general methodology for the reduction of complexity in the recognition of gestures intended for human.machine interaction for different computational devices.

Analysis and Classification of Muscular Paralysis Disease using Electromyography Signal with Machine Learning

Electrical activity of the muscles is characterized by Electromyography (EMG) signals. The EMG signal analysis form the basis for the diagnosis of muscular paralysis. The EMG signals from amyotrophic lateral sclerosis (ALS), and Myopathy are considered to analyze the paralysis. The statistical analysis of EMG signals from ALS, Myopathy, and Normal conditions, aid in the analysis and classification of paralysis. This work intends to analyze and classify the paralysis and normal conditions using EMG features extracted in time and frequency domains. Twelve statistical features are extracted from the EMG signals considered. Machine Learning techniques and deep learning techniques (DLT) are employed to perform the classification. multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), gradient boosting (GB), and nearest neighbor (NN) classifier models are used for the classification. The accuracy of the classifiers is calculated. The accuracy values obtained are 72% for MLP, 73% for SVM, 72% for RF, 71% for GB, and 69% for NN. The performance accuracy is better in SVM compared to other classifiers.

Biomechanical Analysis Suggests Myosuit Reduces Knee Extensor Demand during Level and Incline Gait.

An FDA-approved soft wearable robot, the Myosuit, which was designed to provide hip and knee extension torque has recently been commercialized. While studies have reported reductions in metabolic costs, increased gait speeds, and improvements in clinical test scores, a comprehensive analysis of electromyography (EMG) signals and joint kinematics is warranted because the recruitment of appropriate muscle groups during physiological movement patterns facilitates effective motor learning. Here, we compared the lower limb joint kinematics and EMG patterns while wearing the Myosuit with that of unassisted conditions when performing level overground and incline treadmill gait. The level overground gait sessions (seven healthy subjects) were performed at self-selected speeds and the incline treadmill gait sessions (four healthy subjects) were performed at 2, 3, 4, and 5 km/h. In order to evaluate how the user is assisted, we conducted a biomechanical analysis according to the three major gait tasks: weight acceptance (WA), single-limb support, and limb advancement. The results from the gait sessions suggest that Myosuit not only well preserves the users’ natural patterns, but more importantly reduce knee extensor demand during the WA phase for both level and incline gait.

Muscle Fatigue Analysis by Visualization of Dynamic Surface EMG Signals Using Markov Transition Field.

Muscle fatigue analysis is important in the diagnosis of neuromuscular diseases. Analysis of surface electromyography (sEMG) signals by non-linear probabilistic approach is useful in studying their transitions and thus the neuromuscular system. In this study, a method to visualize sEMG signals using Markov transition field (MTF) under fatigue conditions is proposed. sEMG signals are acquired from 45 healthy participants during biceps curl exercise. They are filtered and divided into ten equal segments. Markov transition matrix is constructed and corresponding MTF image is generated. The average self-transition probability is extracted and compared for both non-fatigue and fatigue segments. It is observed that the extracted feature shows high statistical significance with p value less than 0.001. The increase in average self-transition probability under fatigue condition correlates with the reduction in the degree of signal complexity. Thus, encoding of sEMG signals to images is helpful in analyzing the complexity of the neuromuscular system. Clinical Relevance- This approach may be helpful in analyzing muscle fatigue related with various myoneural conditions.

Electromyography Signal Analysis for the Detection of TMJ Disorder Using Classification Models and Multivariate Analysis

Temporomandibular joint (TMJ) disorder is a wide term that encompasses a variety of disorders with varying etiologies. The purpose of this study was to analyses electromyographic signals with wavelet transform for the diagnostic methods which help in TMJ issues in patients who visited the Dental Centre. Due to the increasing importance of electromyography signals in diagnosing muscular disorders, such as temporomandibular joint disorder, it has been widely used. Through various techniques, such as discrete wavelet transform and power spectral density, it is possible to identify and minimize the noise in the signals, which can be very useful in the diagnosis of the disorder. This paper presents an algorithm that combines the features of discrete wavelet transform and multivariate analysis in order to detect temporomandibular joint disorder. Support vector machine model is giving the better performance in terms of training, testing time and accuracy with 93% compared to other models. Multivariate analysis shows the significant difference in the feature variable chosen.

Analysis of the quadratus lumborum muscle activity on leg length discrepancy: A randomized controlled trial.

Quadratus lumborum (QL) discrete region extensions might change depending on whether leg length discrepancy (LLD) individually has any extra erector spinae action in the lumbar spine, which can result in serious injury to the lower extremities and lumbar vertebrae. This study aims to investigate the effect of QL muscle activity on LLD by using electromyography (EMG) signals. The study employed a randomized controlled design. A total of 100 right-handed volunteers were included in this study. All participants were assessed manually by tape measurement for LLD. EMG signals were recorded during the resting and maximal isometric contraction positions to determine QL muscle activity. The power spectral density (PSD) methods were applied to compute EMG signals. In maximal isometric contraction position, comparing the short right LLD (Right side = 0.00064 ± 0.00001, Left side = 0.00033 ± 0.0006) and short left LLD (Right side = 0.00001 ± 0.00008, Left side = 0.00017 ± 0.0001), it was found that the short right LLD group had significantly increased PSD of EMG values. In resting position, the short right LLD (Right side = 0.0002 ± 0.0073, Left side = 0.00016 ± 0.0065) had significantly increased PSD of EMG compared to the short left LLD (Right side = 0.00004 ± 0.0003, Left side = 0.0001 ± 0.0008) values of the QL muscle activity. The results of both groups were also statistically significant (p< 0.05). The present study showed that it is possible to determine effective experimental interventions for functional LLD using EMG signal analysis of QL muscle activity on an asymptomatic normal population.

A Virtual EMG Signal Control and Analysis for Optimal Hardware Design

Background: A muscle-computer interface is one of the new applications of the human-computer interface technologies and specifically the brain-computer interface. Brain-muscle-computer interface based on the Electromyography (EMG) signal. EMG signal is an electrical activity from a muscle that is used as an input for effecting several tasks.Objective: This work presented an interfacing process between the Graphical User Interface (GUI) and hardware system. Using the implemented system, the researcher shall deals with the raw EMG data easily by analyzing the signal from the muscle sensor detection.Material and Methods: A novel virtual EMG signal control and analysis system design is proposed in this work. The system consists mainly of two parts, hardware and software toolbox. Hardware design is mainly dependent on using a muscle movement sensor as well as the feedback from the virtual toolbox. The virtual software design offers a relatively simple design of a friendly graphical user interface. It consists mainly of the input EMG signal and output signal after using different processing methods. Feedback response from the final EMG signal results after the processing may help the designer to present the optimal hardware design.Results: The output results show the output performance of the proposed virtual EMG data controlling and analysis with the implemented hardware design of the muscle sensor movement detection. The results show promise that these interfaces may provide a new option to benefit the designer in choosing the optimal prosthesis design of severely disabled persons.

Neuromuscular disease detection based on feature extraction from time–frequency images of EMG signals employing robust hyperbolic Stockwell transform

AbstractIn this paper, a novel technique for detection of healthy (H), myopathy, (M) and amyotrophic lateral sclerosis (ALS) electromyography (EMG) signals is proposed employing robust hyperbolic Stockwell transform (HST). HST is an efficient signal processing technique to analyze any nonstationary signal in joint time–frequency (T–F) plane. However, a major issue with HST is the optimum selection of Gaussian window parameters since the resolution in the T–F plane depends on the shape of the window. Considering the aforesaid fact, in this article, a genetic algorithm (GA) based optimized HST is proposed for improved EMG signal analysis in T–F plane. Several novel features were extracted from HST spectrum and features with high statistical significance were selected for classification using several benchmark classifiers. It was observed that optimized HST resulted in better classification accuracy of EMG signals which indicates its potential for clinical applications.