Wavelet transform (WT) has recently drawn the attention of the researchers due to its potential in electromyography (EMG) recognition system. However, the optimal mother wavelet selection remains a challenge to the application of WT in EMG signal processing. This paper presents a detail study for different mother wavelet function in discrete wavelet transform (DWT) and continuous wavelet transform (CWT). Additionally, the performance of different mother wavelet in DWT and CWT at different decomposition level and scale are also investigated. The mean absolute value (MAV) and wavelength (WL) features are extracted from each CWT and reconstructed DWT wavelet coefficient. A popular machine learning method, support vector machine (SVM) is employed to classify the different types of hand movements. The results showed that the most suitable mother wavelet in CWT are Mexican hat and Symlet 6 at scale 16 and 32, respectively. On the other hand, Symlet 4 and Daubechies 4 at the second decomposition level are found to be the optimal wavelet in DWT. From the analysis, we deduced that Symlet 4 at the second decomposition level in DWT is the most suitable mother wavelet for accurate classification of EMG signals of different hand movements.
A comparative study of wavelet families for electromyography signal classific...journalBEEI
The document presents a study comparing different wavelet families for classifying electromyography (EMG) signals based on discrete wavelet transform (DWT). The proposed method involves decomposing EMG signals into sub-bands using DWT, extracting statistical features from each sub-band, and using support vector machines (SVM) for classification. Results showed that the sym14 wavelet at the 8th decomposition level achieved the best classification performance for detecting neuromuscular disorders. The study demonstrates that the proposed DWT-based approach can effectively classify EMG signals and help diagnose neuromuscular conditions.
Improving Long Term Myoelectric Decoding, Using an Adaptive Classifier with L...Sarthak Jain
This document presents a novel adaptive myoelectric decoding algorithm to improve long-term accuracy of prosthetic limb control. The algorithm relies on unsupervised updates to the training set to adapt to both slow and fast changes in myoelectric signals over time. An able-bodied user performed eight wrist movements over 4.5 hours while EMG data was collected. The proposed algorithm maintained decoding accuracy with a decay rate of 0.2 per hour, compared to 3.3 per hour for a non-adaptive classifier, demonstrating its ability to adapt to changes in signals and improve reliability of myoelectric prostheses.
This document discusses estimating hand muscle power using surface electromyography (EMG). EMG is used to evaluate electrical activity in muscles during activities to grade muscle strength. The research aims to develop an automatic method for grading muscle power. EMG is acquired from hand muscles during different activities and analyzed. Analysis includes root mean square, maximum amplitude, and burst time of EMG signals. Results from fifty young subjects show these metrics increase with greater muscle contraction and resistance, allowing muscle strength grading. The method could improve on manual muscle testing which depends on examiner judgment.
International Journal of Biometrics and Bioinformatics(IJBB) Volume (3) Issue...CSCJournals
This paper proposes a novel approach for measuring electrical impedance tomography (EIT) of local living tissue using an artificial intelligence algorithm. EIT is a non-invasive medical imaging technique that measures impedance distribution in tissue. The paper addresses challenges in estimating inner tissue impedance values and improving electrode structure. It introduces a "divided electrode" arrangement and equivalent circuit model to model local tissue impedance. An artificial intelligence algorithm called Alopex is used initially for parameter estimation, followed by the Newton method for higher accuracy, overcoming limitations of each approach individually. This novel hybrid model improves spatial resolution and accuracy of estimating tissue impedance values.
Correlation Analysis of Electromyogram SignalsIJMTST Journal
An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts
the motion style of an opposite limb. These are the important factors inhibiting the practical application of
myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram
(EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts
the signal from the users and maintains good movement recognition performance. At the initial stage the
myoelectric signal is extracted from the user by using the data acquisition system. A new set of features
describing the movements of user’s is extracted and the user’s features are classifed using SVM
classification. The given signal is then compared with the database signal with the accuracy of 90.910 %
across all the EMG signals.
Reconstruction of electrical impedance tomography images based on the expecta...ISA Interchange
Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing.
IRJET-Electromyogram Signals for Multiuser Interface- A ReviewIRJET Journal
This document reviews various methods for feature extraction and classification of electromyogram (EMG) signals for multi-user myoelectric interfaces. It surveys previous work that used techniques like discrete wavelet transform (DWT) and support vector machines (SVM) for feature extraction and classification of EMG signals. The document concludes that DWT is well-suited for extracting both time and frequency domain features from non-stationary EMG signals. It also finds that SVM performed accurately for classification of features from multi-user EMG signals. The review aims to determine the best methods for a project using DWT for feature extraction and SVM for classification of EMG signals from multiple users.
1) A study explored using an electrode array and signal characteristics to select an optimal electrode pair for surface electromyography (sEMG), aiming to improve on existing electrode placement methods.
2) A 3x4 electrode array was placed over seven muscles on subjects. Nine bipolar electrode pairs were formed from each array.
3) sEMG parameters were calculated for each pair and evaluated based on repeatability across trials and comparison to a traditionally placed electrode pair, to determine if signal characteristics could help select a high quality electrode pair.
A comparative study of wavelet families for electromyography signal classific...journalBEEI
The document presents a study comparing different wavelet families for classifying electromyography (EMG) signals based on discrete wavelet transform (DWT). The proposed method involves decomposing EMG signals into sub-bands using DWT, extracting statistical features from each sub-band, and using support vector machines (SVM) for classification. Results showed that the sym14 wavelet at the 8th decomposition level achieved the best classification performance for detecting neuromuscular disorders. The study demonstrates that the proposed DWT-based approach can effectively classify EMG signals and help diagnose neuromuscular conditions.
Improving Long Term Myoelectric Decoding, Using an Adaptive Classifier with L...Sarthak Jain
This document presents a novel adaptive myoelectric decoding algorithm to improve long-term accuracy of prosthetic limb control. The algorithm relies on unsupervised updates to the training set to adapt to both slow and fast changes in myoelectric signals over time. An able-bodied user performed eight wrist movements over 4.5 hours while EMG data was collected. The proposed algorithm maintained decoding accuracy with a decay rate of 0.2 per hour, compared to 3.3 per hour for a non-adaptive classifier, demonstrating its ability to adapt to changes in signals and improve reliability of myoelectric prostheses.
This document discusses estimating hand muscle power using surface electromyography (EMG). EMG is used to evaluate electrical activity in muscles during activities to grade muscle strength. The research aims to develop an automatic method for grading muscle power. EMG is acquired from hand muscles during different activities and analyzed. Analysis includes root mean square, maximum amplitude, and burst time of EMG signals. Results from fifty young subjects show these metrics increase with greater muscle contraction and resistance, allowing muscle strength grading. The method could improve on manual muscle testing which depends on examiner judgment.
International Journal of Biometrics and Bioinformatics(IJBB) Volume (3) Issue...CSCJournals
This paper proposes a novel approach for measuring electrical impedance tomography (EIT) of local living tissue using an artificial intelligence algorithm. EIT is a non-invasive medical imaging technique that measures impedance distribution in tissue. The paper addresses challenges in estimating inner tissue impedance values and improving electrode structure. It introduces a "divided electrode" arrangement and equivalent circuit model to model local tissue impedance. An artificial intelligence algorithm called Alopex is used initially for parameter estimation, followed by the Newton method for higher accuracy, overcoming limitations of each approach individually. This novel hybrid model improves spatial resolution and accuracy of estimating tissue impedance values.
Correlation Analysis of Electromyogram SignalsIJMTST Journal
An inability to adapt myoelectric interfaces to a user’s unique style of hand motion. The system also adapts
the motion style of an opposite limb. These are the important factors inhibiting the practical application of
myoelectric interfaces. This is mainly attributed to the individual differences in the exhibited electromyogram
(EMG) signals generated by the muscles of different limbs. In this project myoelectric interface easily adapts
the signal from the users and maintains good movement recognition performance. At the initial stage the
myoelectric signal is extracted from the user by using the data acquisition system. A new set of features
describing the movements of user’s is extracted and the user’s features are classifed using SVM
classification. The given signal is then compared with the database signal with the accuracy of 90.910 %
across all the EMG signals.
Reconstruction of electrical impedance tomography images based on the expecta...ISA Interchange
Electrical impedance tomography (EIT) calculates the internal conductivity distribution within a body using electrical contact measurements. The image reconstruction for EIT is an inverse problem, which is both non-linear and ill-posed. The traditional regularization method cannot avoid introducing negative values in the solution. The negativity of the solution produces artifacts in reconstructed images in presence of noise. A statistical method, namely, the expectation maximization (EM) method, is used to solve the inverse problem for EIT in this paper. The mathematical model of EIT is transformed to the non-negatively constrained likelihood minimization problem. The solution is obtained by the gradient projection-reduced Newton (GPRN) iteration method. This paper also discusses the strategies of choosing parameters. Simulation and experimental results indicate that the reconstructed images with higher quality can be obtained by the EM method, compared with the traditional Tikhonov and conjugate gradient (CG) methods, even with non-negative processing.
IRJET-Electromyogram Signals for Multiuser Interface- A ReviewIRJET Journal
This document reviews various methods for feature extraction and classification of electromyogram (EMG) signals for multi-user myoelectric interfaces. It surveys previous work that used techniques like discrete wavelet transform (DWT) and support vector machines (SVM) for feature extraction and classification of EMG signals. The document concludes that DWT is well-suited for extracting both time and frequency domain features from non-stationary EMG signals. It also finds that SVM performed accurately for classification of features from multi-user EMG signals. The review aims to determine the best methods for a project using DWT for feature extraction and SVM for classification of EMG signals from multiple users.
1) A study explored using an electrode array and signal characteristics to select an optimal electrode pair for surface electromyography (sEMG), aiming to improve on existing electrode placement methods.
2) A 3x4 electrode array was placed over seven muscles on subjects. Nine bipolar electrode pairs were formed from each array.
3) sEMG parameters were calculated for each pair and evaluated based on repeatability across trials and comparison to a traditionally placed electrode pair, to determine if signal characteristics could help select a high quality electrode pair.
Implementation of Radon Transformation for Electrical Impedance Tomography (E...ijistjournal
Radon Transformation is generally used to construct optical image (like CT image) from the projection data in biomedical imaging. In this paper, the concept of Radon Transformation is implemented to reconstruct Electrical Impedance Topographic Image (conductivity or resistivity distribution) of a circular subject. A parallel resistance model of a subject is proposed for Electrical Impedance Topography(EIT) or Magnetic Induction Tomography(MIT). A circular subject with embedded circular objects is segmented into equal width slices from different angles. For each angle, Conductance and Conductivity of each slice is calculated and stored in an array. A back projection method is used to generate a two-dimensional image from one-dimensional projections. As a back projection method, Inverse Radon Transformation is applied on the calculated conductance and conductivity to reconstruct two dimensional images. These images are compared to the target image. In the time of image reconstruction, different filters are used and these images are compared with each other and target image.
Dsp lab report- Analysis and classification of EMG signal using MATLAB.Nurhasanah Shafei
This document discusses a study analyzing and classifying electromyogram (EMG) signals. The researchers developed a MATLAB-based system that can differentiate EMG signals coming from different patients. The system analyzes time and frequency domain characteristics of the EMG signals, including median value, average value, root mean square, maximum power, and minimum power. It then uses these characteristics to identify which patient a given EMG signal belongs to through a graphical user interface. The system was able to accurately classify EMG signals from two patients based on their power spectrum signatures.
Bio-medical (EMG) Signal Analysis and Feature Extraction Using Wavelet TransformIJERA Editor
This document summarizes research on analyzing electromyography (EMG) signals using wavelet transforms to extract features for classification of muscle activity. A multi-channel EMG acquisition system was developed using surface electrodes to measure forearm muscle signals. Different wavelet families were used to analyze the EMG signals. Features like root mean square, logarithm of root mean square, centroid frequency, and standard deviation were extracted. Root mean square feature extraction performed best. In the future, this method could be used to control prosthetic or robotic arms for real-time processing based on muscle activity.
Accuracy, Sensitivity and Specificity Measurement of Various Classification T...IOSR Journals
This document compares the accuracy, sensitivity, and specificity of various classification techniques when applied to healthcare data on diabetes. It analyzes several algorithms implemented in Weka (Multilayer Perception, Bayes Network, J48graft, JRip) and other tools (PNN, LVQ, FFN, etc. in MATLAB and GINI in RapidMiner) on a diabetes dataset. The results show that J48graft had the highest accuracy at 81.33% while PNN had the highest sensitivity at 63.33% and DTDN had the highest specificity at 88.8% based on calculations using true/false positive/negative values. Therefore, different algorithms performed best for different evaluation metrics on this healthcare
Hybrid Feature Extraction Model For Emg Classification Used In Exoskeleton Ro...IRJET Journal
This document presents a study on classifying electromyography (EMG) signals from the upper limb for use in controlling exoskeleton robots. EMG signals from the deltoid and forearm muscles of 4 volunteers during 10 aggressive and normal arm motions were collected. Four feature sets were extracted from the signals, including time domain, wavelet, and frequency domain features. A support vector machine classifier using a polynomial kernel achieved an average accuracy of 97.77% and kappa coefficient of 0.953 in classifying the 10 motions based on the combined feature sets. The results suggest EMG signal classification can provide effective control of exoskeleton robots to help physically impaired individuals perform daily activities.
OPTIMIZATION OF NEURAL NETWORK ARCHITECTURE FOR BIOMECHANIC CLASSIFICATION TA...ijaia
Electromyogram signals (EMGs) contain valuable information that can be used in man-machine interfacing between human users and myoelectric prosthetic devices. However, EMG signals are
complicated and prove difficult to analyze due to physiological noise and other issues. Computational
intelligence and machine learning techniques, such as artificial neural networks (ANNs), serve as powerful
tools for analyzing EMG signals and creating optimal myoelectric control schemes for prostheses. This
research examines the performance of four different neural network architectures (feedforward, recurrent,
counter propagation, and self organizing map) that were tasked with classifying walking speed when given
EMG inputs from 14 different leg muscles. Experiments conducted on the data set suggest that self
organizing map neural networks are capable of classifying walking speed with greater than 99% accuracy.
Hand motion pattern recognition analysis of forearm muscle using MMG signalsjournalBEEI
Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier.
AN ELECTRICAL IMPEDANCE TOMOGRAPHY SYSTEM FOR THYROID GLAND WITH A TINY ELECT...ijbesjournal
Electrical impedance Tomography (EIT) is a non-invasive imaging technique based on measuring of the
electrical conductivity and capacitance of abnormal and normal human tissues. The present work aims to
develop an EIT imaging system for imaging thyroid gland. Patients with thyroid nodules were eligible for
the study. The study was conducted on two groups of participants: control group consists of 20 normal
female cases and experimental consists of 20 goiter female patients. The thyroid nodule location, size, and
type measured by ultrasound. Thyroid gland conductivity and permittivity were recorded using EIT. The
impedance measurement is done through the applying of two probes: one probe to the neck region
(scanning probe) and the rest region (reference probe) with electrolytic gel for each probe, then the system
software proceeds to reconstruct the image and calculate the electrical impedance of the thyroid gland on
a personal computer which acts as an output display and storage for case information. The thyroid
scanning probe has 64 electrodes embedded on a small space (30 mm diameter and 50 mm height) inside
of the probe. Multifrequency impedance measurements are typically made by applying an electric current
to a target mass by using of the scanning probe and measuring the developed voltage. The present EIT
system provides real- time visualization of the spatial distribution of the electrical properties of the thyroid
tissue. Images obtained from the bioimpedance (BI) were compared to images obtained from the
ultrasound imaging, results showed great similarity between the two diagnostic images. Tumor tissue has
higher resistance and capacitance value than that of normal thyroid gland.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
Comparison of regression models for estimation of isometric wrist joint torqu...Amir Ziai
The document compares the performance of common regression models for estimating wrist joint torque using surface electromyography (SEMG) signals under different circumstances. It finds that model accuracy decreases significantly with the passage of time, electrode displacement, and changes in limb posture. The ordinary least squares linear regression model provided high accuracy and very short training times compared to other models tested, including physiological, support vector machine, artificial neural network, and locally weighted projection regression models. Regular retraining of models is necessary to maintain accurate torque estimation when factors like time, electrode placement, or limb position change.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This study compared the online effect of using a broad frequency band (8-26Hz) versus a narrow band (centered at 12Hz) for a motor imagery-based brain-computer interface (BCI). Six subjects performed cursor tasks using both bands. There was no significant difference in accuracy or invalid rates between the bands. However, subjects with high mu rhythm power performed better with the narrow band, while subjects with high beta power performed better with the broad band. Therefore, the broad band is preferred to include both mu and beta rhythms and achieve better performance in most subjects. Future work with more subjects is needed to confirm these findings.
The document describes an EEG study investigating cortical activation during motor planning for grasping objects with either constrained or unconstrained contact points. The study had two conditions: blocked trials where the required grasp was certain, and random trials where it was uncertain. Results showed that beta power over sensorimotor areas differentiated blocked and random contexts, but not grasp types. Beta power decreased more after the movement cue for unconstrained grasping in random contexts, suggesting heightened activation in response to uncertainty. Reaction time also predicted changes in beta power for unconstrained grasping.
Natural Vibration Analysis of Femur Bone Using HyperworksIJERA Editor
The main objective of the femur bone analysis is to know the natural frequencies and identify the fracture location of the bone through simulation based on the HYPERWORKS. The femur bone analysis is subjected to free-free and fixed-fixed boundary conditions. The mode shape shows that the natural frequency of free-free boundary condition varies from 0 Hz to 57 Hz and for fixed-fixed boundary condition 11 Hz to 171 Hz. On the bases of these two boundary conditions mode shape is determined and fracture location can be easily notified.
This document discusses a hand gesture recognition system using electromyography (EMG) signals. EMG sensors on the forearm detect muscle signals which are preprocessed, analyzed in the frequency domain, and classified. Features like Fourier transforms, power spectra, and autoregressive coefficients are extracted from the signals. Various classification algorithms are tested and Naive Bayes achieves the highest accuracy of 95% for categorizing five different hand gestures.
TOMOGRAPHY OF HUMAN BODY USING EXACT SIMULTANEOUS ITERATIVE RECONSTRUCTION AL...cscpconf
In this paper an Exact Simultaneous Iterative Reconstruction Algorithm is developed and applied on a large semi human size normal biological model and a diseased model (liver region affected) to verify the efficiency of the algorithm. The algorithm is successfully reconstructed the normal model having 15%-20% perturbation i.e. change in permittivity during disease. In diseased case, reconstructed imaginary part of complex permittivity clearly detects the affected zone and it may help the medical diagnosis. Hence it may be a powerful tool for early detection of cancerous tumors as the interrogating wave is a noninvasive one at the ultra high frequency range. The resolution of this system is increased with the reduction of
wavelength by immersing the antenna system and the model in saline water region. The advantage of this algorithm is that the calculation of cofactor are done offline to save the computational time and cofactors are expressed as a function of distances irrespective of their positions
Personal identity verification based ECG biometric using non-fiducial features IJECEIAES
Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.
Mathematical Modelling and Computer Simulation Assist in Designing Non-tradit...IJECEIAES
This document describes a study that uses mathematical modeling and computer simulation to analyze the processes involved in electrostatic separation and precipitation. The study develops computer models of cylindrical electrostatic separators charged with both direct current and alternating current. The models are based on solving systems of differential equations that describe the motion, forces, and electric fields affecting macroscopic charged particles within the separator space. The computer simulations analyze the kinematics, dynamics and trajectories of particles moving through the separators. They provide insights into critical operating states for AC charged separators and the effects of various factors like gradient force on particle motion. The models and simulations improve understanding of separation processes compared to prior empirical approaches and enable more complex analysis of particle ballistics.
IRJET-A Survey on Effect of Meditation on Attention Level Using EEGIRJET Journal
This document summarizes a proposed study that investigates the effect of meditation on attention level using EEG data analysis. It begins with an introduction on attention and meditation, then reviews previous related studies that analyzed EEG data to measure attention. The proposed work will record EEG data from subjects using the 10-20 electrode placement system before and after an 8-week meditation program. The EEG data will be preprocessed to remove noise, features will be extracted using wavelet transforms, and a random forest classifier will be used to classify attention levels and analyze the effect of meditation. The goal is to objectively measure how meditation impacts attention to help students improve concentration.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
Application of gabor transform in the classification of myoelectric signalTELKOMNIKA JOURNAL
In recent day, Electromyography (EMG) signal are widely applied in myoelectric control. Unfortunately, most of studies focused on the classification of EMG signals based on healthy subjects. Due to the lack of study in amputee subject, this paper aims to investigate the performance of healthy and amputee subjects for the classification of multiple hand movement types. In this work, Gabor transform (GT) is used to transform the EMG signal into time-frequency representation. Five time-frequency features are extracted from GT coefficient. Feature extraction is an effective way to reduce the dimensionality, as well as keeping the valuable information. Two popular classifiers namely k-nearest neighbor (KNN) and support vector machine (SVM) are employed for performance evaluation. The developed system is evaluated using the EMG data acquired from the publicy available NinaPro Database. The results revealed that the extracting GT features can achieve promising performance in the classification of EMG signals.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Implementation of Radon Transformation for Electrical Impedance Tomography (E...ijistjournal
Radon Transformation is generally used to construct optical image (like CT image) from the projection data in biomedical imaging. In this paper, the concept of Radon Transformation is implemented to reconstruct Electrical Impedance Topographic Image (conductivity or resistivity distribution) of a circular subject. A parallel resistance model of a subject is proposed for Electrical Impedance Topography(EIT) or Magnetic Induction Tomography(MIT). A circular subject with embedded circular objects is segmented into equal width slices from different angles. For each angle, Conductance and Conductivity of each slice is calculated and stored in an array. A back projection method is used to generate a two-dimensional image from one-dimensional projections. As a back projection method, Inverse Radon Transformation is applied on the calculated conductance and conductivity to reconstruct two dimensional images. These images are compared to the target image. In the time of image reconstruction, different filters are used and these images are compared with each other and target image.
Dsp lab report- Analysis and classification of EMG signal using MATLAB.Nurhasanah Shafei
This document discusses a study analyzing and classifying electromyogram (EMG) signals. The researchers developed a MATLAB-based system that can differentiate EMG signals coming from different patients. The system analyzes time and frequency domain characteristics of the EMG signals, including median value, average value, root mean square, maximum power, and minimum power. It then uses these characteristics to identify which patient a given EMG signal belongs to through a graphical user interface. The system was able to accurately classify EMG signals from two patients based on their power spectrum signatures.
Bio-medical (EMG) Signal Analysis and Feature Extraction Using Wavelet TransformIJERA Editor
This document summarizes research on analyzing electromyography (EMG) signals using wavelet transforms to extract features for classification of muscle activity. A multi-channel EMG acquisition system was developed using surface electrodes to measure forearm muscle signals. Different wavelet families were used to analyze the EMG signals. Features like root mean square, logarithm of root mean square, centroid frequency, and standard deviation were extracted. Root mean square feature extraction performed best. In the future, this method could be used to control prosthetic or robotic arms for real-time processing based on muscle activity.
Accuracy, Sensitivity and Specificity Measurement of Various Classification T...IOSR Journals
This document compares the accuracy, sensitivity, and specificity of various classification techniques when applied to healthcare data on diabetes. It analyzes several algorithms implemented in Weka (Multilayer Perception, Bayes Network, J48graft, JRip) and other tools (PNN, LVQ, FFN, etc. in MATLAB and GINI in RapidMiner) on a diabetes dataset. The results show that J48graft had the highest accuracy at 81.33% while PNN had the highest sensitivity at 63.33% and DTDN had the highest specificity at 88.8% based on calculations using true/false positive/negative values. Therefore, different algorithms performed best for different evaluation metrics on this healthcare
Hybrid Feature Extraction Model For Emg Classification Used In Exoskeleton Ro...IRJET Journal
This document presents a study on classifying electromyography (EMG) signals from the upper limb for use in controlling exoskeleton robots. EMG signals from the deltoid and forearm muscles of 4 volunteers during 10 aggressive and normal arm motions were collected. Four feature sets were extracted from the signals, including time domain, wavelet, and frequency domain features. A support vector machine classifier using a polynomial kernel achieved an average accuracy of 97.77% and kappa coefficient of 0.953 in classifying the 10 motions based on the combined feature sets. The results suggest EMG signal classification can provide effective control of exoskeleton robots to help physically impaired individuals perform daily activities.
OPTIMIZATION OF NEURAL NETWORK ARCHITECTURE FOR BIOMECHANIC CLASSIFICATION TA...ijaia
Electromyogram signals (EMGs) contain valuable information that can be used in man-machine interfacing between human users and myoelectric prosthetic devices. However, EMG signals are
complicated and prove difficult to analyze due to physiological noise and other issues. Computational
intelligence and machine learning techniques, such as artificial neural networks (ANNs), serve as powerful
tools for analyzing EMG signals and creating optimal myoelectric control schemes for prostheses. This
research examines the performance of four different neural network architectures (feedforward, recurrent,
counter propagation, and self organizing map) that were tasked with classifying walking speed when given
EMG inputs from 14 different leg muscles. Experiments conducted on the data set suggest that self
organizing map neural networks are capable of classifying walking speed with greater than 99% accuracy.
Hand motion pattern recognition analysis of forearm muscle using MMG signalsjournalBEEI
Surface Mechanomyography (MMG) is the recording of mechanical activity of muscle tissue. MMG measures the mechanical signal (vibration of muscle) that generated from the muscles during contraction or relaxation action. It is widely used in various fields such as medical diagnosis, rehabilitation purpose and engineering applications. The main purpose of this research is to identify the hand gesture movement via VMG sensor (TSD250A) and classify them using Linear Discriminant Analysis (LDA). There are four channels MMG signal placed into adjacent muscles which PL-FCU and ED-ECU. The features used to feed the classifier to determine accuracy are mean absolute value, standard deviation, variance and root mean square. Most of subjects gave similar range of MMG signal of extraction values because of the adjacent muscle. The average accuracy of LDA is approximately 87.50% for the eight subjects. The finding of the result shows, MMG signal of adjacent muscle can affect the classification accuracy of the classifier.
AN ELECTRICAL IMPEDANCE TOMOGRAPHY SYSTEM FOR THYROID GLAND WITH A TINY ELECT...ijbesjournal
Electrical impedance Tomography (EIT) is a non-invasive imaging technique based on measuring of the
electrical conductivity and capacitance of abnormal and normal human tissues. The present work aims to
develop an EIT imaging system for imaging thyroid gland. Patients with thyroid nodules were eligible for
the study. The study was conducted on two groups of participants: control group consists of 20 normal
female cases and experimental consists of 20 goiter female patients. The thyroid nodule location, size, and
type measured by ultrasound. Thyroid gland conductivity and permittivity were recorded using EIT. The
impedance measurement is done through the applying of two probes: one probe to the neck region
(scanning probe) and the rest region (reference probe) with electrolytic gel for each probe, then the system
software proceeds to reconstruct the image and calculate the electrical impedance of the thyroid gland on
a personal computer which acts as an output display and storage for case information. The thyroid
scanning probe has 64 electrodes embedded on a small space (30 mm diameter and 50 mm height) inside
of the probe. Multifrequency impedance measurements are typically made by applying an electric current
to a target mass by using of the scanning probe and measuring the developed voltage. The present EIT
system provides real- time visualization of the spatial distribution of the electrical properties of the thyroid
tissue. Images obtained from the bioimpedance (BI) were compared to images obtained from the
ultrasound imaging, results showed great similarity between the two diagnostic images. Tumor tissue has
higher resistance and capacitance value than that of normal thyroid gland.
A New Approach to Denoising EEG Signals - Merger of Translation Invariant Wav...CSCJournals
In this paper we present a new algorithm using a merger of Independent Component Analysis and Translation Invariant Wavelet Transform. The efficacy of this algorithm is evaluated by applying contaminated EEG signals. Its performance was compared to three fixed-point ICA algorithms (FastICA, EFICA and Pearson-ICA) using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Amari Performance Index. Experiments reveal that our new technique is the most accurate separation method.
Comparison of regression models for estimation of isometric wrist joint torqu...Amir Ziai
The document compares the performance of common regression models for estimating wrist joint torque using surface electromyography (SEMG) signals under different circumstances. It finds that model accuracy decreases significantly with the passage of time, electrode displacement, and changes in limb posture. The ordinary least squares linear regression model provided high accuracy and very short training times compared to other models tested, including physiological, support vector machine, artificial neural network, and locally weighted projection regression models. Regular retraining of models is necessary to maintain accurate torque estimation when factors like time, electrode placement, or limb position change.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This study compared the online effect of using a broad frequency band (8-26Hz) versus a narrow band (centered at 12Hz) for a motor imagery-based brain-computer interface (BCI). Six subjects performed cursor tasks using both bands. There was no significant difference in accuracy or invalid rates between the bands. However, subjects with high mu rhythm power performed better with the narrow band, while subjects with high beta power performed better with the broad band. Therefore, the broad band is preferred to include both mu and beta rhythms and achieve better performance in most subjects. Future work with more subjects is needed to confirm these findings.
The document describes an EEG study investigating cortical activation during motor planning for grasping objects with either constrained or unconstrained contact points. The study had two conditions: blocked trials where the required grasp was certain, and random trials where it was uncertain. Results showed that beta power over sensorimotor areas differentiated blocked and random contexts, but not grasp types. Beta power decreased more after the movement cue for unconstrained grasping in random contexts, suggesting heightened activation in response to uncertainty. Reaction time also predicted changes in beta power for unconstrained grasping.
Natural Vibration Analysis of Femur Bone Using HyperworksIJERA Editor
The main objective of the femur bone analysis is to know the natural frequencies and identify the fracture location of the bone through simulation based on the HYPERWORKS. The femur bone analysis is subjected to free-free and fixed-fixed boundary conditions. The mode shape shows that the natural frequency of free-free boundary condition varies from 0 Hz to 57 Hz and for fixed-fixed boundary condition 11 Hz to 171 Hz. On the bases of these two boundary conditions mode shape is determined and fracture location can be easily notified.
This document discusses a hand gesture recognition system using electromyography (EMG) signals. EMG sensors on the forearm detect muscle signals which are preprocessed, analyzed in the frequency domain, and classified. Features like Fourier transforms, power spectra, and autoregressive coefficients are extracted from the signals. Various classification algorithms are tested and Naive Bayes achieves the highest accuracy of 95% for categorizing five different hand gestures.
TOMOGRAPHY OF HUMAN BODY USING EXACT SIMULTANEOUS ITERATIVE RECONSTRUCTION AL...cscpconf
In this paper an Exact Simultaneous Iterative Reconstruction Algorithm is developed and applied on a large semi human size normal biological model and a diseased model (liver region affected) to verify the efficiency of the algorithm. The algorithm is successfully reconstructed the normal model having 15%-20% perturbation i.e. change in permittivity during disease. In diseased case, reconstructed imaginary part of complex permittivity clearly detects the affected zone and it may help the medical diagnosis. Hence it may be a powerful tool for early detection of cancerous tumors as the interrogating wave is a noninvasive one at the ultra high frequency range. The resolution of this system is increased with the reduction of
wavelength by immersing the antenna system and the model in saline water region. The advantage of this algorithm is that the calculation of cofactor are done offline to save the computational time and cofactors are expressed as a function of distances irrespective of their positions
Personal identity verification based ECG biometric using non-fiducial features IJECEIAES
Biometrics was used as an automated and fast acceptable technology for human identification and it may be behavioral or physiological traits. Any biometric system based on identification or verification modes for human identity. The electrocardiogram (ECG) is considered as one of the physiological biometrics which impossible to mimic or stole. ECG feature extraction methods were performed using fiducial or non-fiducial approaches. This research presents an authentication ECG biometric system using non-fiducial features obtained by Discrete Wavelet Decomposition and the Euclidean Distance technique was used to implement the identity verification. From the obtained results, the proposed system accuracy is 96.66% also, using the verification system is preferred for a large number of individuals as it takes less time to get the decision.
Mathematical Modelling and Computer Simulation Assist in Designing Non-tradit...IJECEIAES
This document describes a study that uses mathematical modeling and computer simulation to analyze the processes involved in electrostatic separation and precipitation. The study develops computer models of cylindrical electrostatic separators charged with both direct current and alternating current. The models are based on solving systems of differential equations that describe the motion, forces, and electric fields affecting macroscopic charged particles within the separator space. The computer simulations analyze the kinematics, dynamics and trajectories of particles moving through the separators. They provide insights into critical operating states for AC charged separators and the effects of various factors like gradient force on particle motion. The models and simulations improve understanding of separation processes compared to prior empirical approaches and enable more complex analysis of particle ballistics.
IRJET-A Survey on Effect of Meditation on Attention Level Using EEGIRJET Journal
This document summarizes a proposed study that investigates the effect of meditation on attention level using EEG data analysis. It begins with an introduction on attention and meditation, then reviews previous related studies that analyzed EEG data to measure attention. The proposed work will record EEG data from subjects using the 10-20 electrode placement system before and after an 8-week meditation program. The EEG data will be preprocessed to remove noise, features will be extracted using wavelet transforms, and a random forest classifier will be used to classify attention levels and analyze the effect of meditation. The goal is to objectively measure how meditation impacts attention to help students improve concentration.
METHODS FOR IMPROVING THE CLASSIFICATION ACCURACY OF BIOMEDICAL SIGNALS BASED...IAEME Publication
Biomedical signals are long records of electrical activity within the human body, and they faithfully represent the state of health of a person. Of the many biomedical signals, focus of this work is on Electro-encephalogram (EEG), Electro-cardiogram (ECG) and Electro-myogram (EMG). It is tiresome for physicians to visually examine the long records of biomedical signals to arrive at conclusions. Automated classification of these signals can largely assist the physicians in their diagnostic process. Classifying a biomedical signal is the process of attaching the signal to a disease state or healthy state. Classification Accuracy (CA) depends on the features extracted from the signal and the classification process involved. Certain critical information on the health of a person is usually hidden in the spectral content of the signal. In this paper, effort is made for the improvement in CA when spectral features are included in the classification process.
Application of gabor transform in the classification of myoelectric signalTELKOMNIKA JOURNAL
In recent day, Electromyography (EMG) signal are widely applied in myoelectric control. Unfortunately, most of studies focused on the classification of EMG signals based on healthy subjects. Due to the lack of study in amputee subject, this paper aims to investigate the performance of healthy and amputee subjects for the classification of multiple hand movement types. In this work, Gabor transform (GT) is used to transform the EMG signal into time-frequency representation. Five time-frequency features are extracted from GT coefficient. Feature extraction is an effective way to reduce the dimensionality, as well as keeping the valuable information. Two popular classifiers namely k-nearest neighbor (KNN) and support vector machine (SVM) are employed for performance evaluation. The developed system is evaluated using the EMG data acquired from the publicy available NinaPro Database. The results revealed that the extracting GT features can achieve promising performance in the classification of EMG signals.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
EEG SIGNAL CLASSIFICATION USING LDA AND MLP CLASSIFIERhiij
Electroencephalography (EEG) is the recording of electrical activities along the scalp. EEG measures
voltage fluctuations resulting from ionic current flows within the neurons of the brain. Diagnostic
applications generally focus on the spectral content of EEG, which is the type of neural oscillations that
can be observed in EEG signal. EEG is most often used to diagnose epilepsy, which causes obvious
abnormalities in EEG readings. This powerful property confirms the rich potential for EEG analysis and
motivates the need for advanced signal processing techniques to aid clinicians in their interpretations.
This paper describes the application of Wavelet Transform (WT) for the processing of
Electroencephalogram (EEG) signals. Furthermore, the linear discriminant analysis (LDA) is applied for
feature selection and dimensionality reduction where the informative and discriminative two-dimension
features are used as a benchmark for classification purposes through a Multi-Layers Perceptron (MLP)
neural network. For five classification problems, the proposed model achieves a high sensitivity,
specificity and accuracy of 100%.Finally, the comparison of the results obtained with the proposed
methods and those obtained with previous literature methods shows the superiority of our approach for
EEG signals classification and automated diagnosis
New Method of R-Wave Detection by Continuous Wavelet TransformCSCJournals
In this paper we have employed a new method of R-peaks detection in electrocardiogram (ECG) signals. This method is based on the application of the discretised Continuous Wavelet Transform (CWT) used for the Bionic Wavelet Transform (BWT). The mother wavelet associated to this transform is the Morlet wavelet. For evaluating the proposed method, we have compared it to others methods that are based on Discrete Wavelet Transform (DWT). In this evaluation, the used ECG signals are taken from MIT-BIH database. The obtained results show that the proposed method outperforms some conventional techniques used in our evaluation.
SUITABLE MOTHER WAVELET SELECTION FOR EEG SIGNALS ANALYSIS: FREQUENCY BANDS D...sipij
Wavelet transform (WT) is a powerful modern tool for time-frequency analysis of non-stationary signals such as electroencephalogram (EEG). The aim of this study is to choose the best and suitable mother wavelet function (MWT) for analyzing normal, seizure-free and seizured EEG signals. Several MWTs can be used, but the best MWT is the one that conserves the quasi-totality of information of the original signal on wavelet coefficients and gather more EEG rhythms in terms of frequency. In this study, Daubechies, Symlets and Coiflets orthogonal families were used as bsis mother wavelet functions. The percentage rootmeans square difference (PRD), the signal to noise ratio (SNR) and the simulated frequencies as the selection metrics. Simulation results indicate Daubechies wavelet at level 4 (Db4) as the most suitable MWT for EEG frequency bands decomposition.Furthermore, due to the redundancy of the extracted features, linear discriminant analysis (LDA) is applied for feature selection. Scatter plot showed that the selected feature vector represents the amount of changes in frequency distribution and carries most of the discriminative and representative information about their classes. Then, this study can provide a reference for the selection of a suitable MWT and discriminativefeatures.
Significant variables extraction of post-stroke EEG signal using wavelet and ...TELKOMNIKA JOURNAL
Stroke patients require a long recovery. One success of the treatment given is the evaluation and
monitoring during recovery. One device for monitoring the development of post-stroke patients is
Electroencephalogram (EEG). This research proposed a method for extracting variables of EEG signals for
post-stroke patient analysis using Wavelet and Self-Organizing Map Kohonen clustering. EEG signal was
extracted by Wavelet to obtain Alpha, beta, theta, gamma, and Mu waves. These waves, the amplitude
and asymmetric of the symmetric channel pairs are features in Self Organizing Map Kohonen Clustering.
Clustering results were compared with actual clusters of post-stroke and no-stroke subjects to extract
significant variable. These results showed that the configuration of Alpha, Beta, and Mu waves, amplitude
together with the difference between the variable of symmetric channel pairs are significant in the analysis
of post-stroke patients. The results gave using symmetric channel pairs provided 54-74% accuracy.
MHEALTH APPLICATIONS DEVELOPED BY THE MINISTRY OF HEALTH FOR PUBLIC USERS INK...hiij
mHealth applications have shown promise in supporting the delivery of health services in peoples’ daily life. Recently, the Ministry of Health in the Kingdom of Saudi Arabia (MOH) has launched several mHealth applications to develop work mechanisms. Our study aimed to identify and understand the design of mHealth apps by classifying their persuasive features using the Persuasive Systems Design (PSD) model and expert evaluation method. This paper presents the distinct persuasive features applied in recent applications launched by MOH for public users called “Sehha & Mawid” Apps. The results revealed the extensive use of persuasive features; particularly features related to credibility support, dialogue support and primary task support respectively. The implementation and design of social support features were found to be poor; this could be due to the nature of the apps or lack of knowledge from the developers’
perspectives. The findings suggest some features that may improve the persuasion for the evaluated apps.
5. detection and separation of eeg artifacts using wavelet transform nov 11, ...IAESIJEECS
This document summarizes a study on using wavelet transforms to detect and separate artifacts in EEG signals. The study aimed to minimize artifacts and noise in EEG signals without affecting the original signal. Wavelet transforms were found to be effective for analyzing non-stationary EEG signals. The results showed that wavelet transforms significantly reduced input size without compromising performance. Decomposing EEG signals using wavelet transforms extracted different frequency bands and resolved signals at different resolutions. This allowed artifacts and noise to be detected and the original signal to be recovered. Simulation results demonstrated the wavelet transform's ability to denoise EEG signals and extract key frequency components.
Recognition of new gestures using myo armband for myoelectric prosthetic appl...IJECEIAES
Myoelectric prostheses are a viable solution for people with amputations. The chal- lenge in implementing a usable myoelectric prosthesis lies in accurately recognizing different hand gestures. The current myoelectric devices usually implement very few hand gestures. In order to approximate a real hand functionality, a myoelectric prosthesis should implement a large number of hand and finger gestures. However, increasing number of gestures can lead to a decrease in recognition accuracy. In this work a Myo armband device is used to recognize fourteen gestures (five build in gestures of Myo armband in addition to nine new gestures). The data in this research is collected from three body-able subjects for a period of 7 seconds per gesture. The proposed method uses a pattern recognition technique based on Multi-Layer Perceptron Neural Network (MLPNN). The results show an average accuracy of 90.5% in recognizing the proposed fourteen gestures.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
EEG based Brain Computer Interface (BCI) establishes a new channel between human brain and the
surrounding environment in order to disseminate instructions to the outside world. It is based on the
recording of temporary EEG changes during different types of motor imagery such as imagination of
different hand movements. The spatial pattern of activated cortical areas during motor imagery is similar
to that of real time executed movement. Time domain features and frequency domain features are extracted
with emphasis on recognizing discriminative features representing EEG trials recorded during imagination
of different hand movements. Then, classification into different hand movements is carried out.
Using deep neural networks in classifying electromyography signals for hand g...IAESIJAI
Electromyography (EMG) signals are used for various applications, especially in smart prostheses. Recognizing various gestures (hand movements) in EMG systems introduces challenges. These challenges include the noise effect on EMG signals and the difficulty in identifying the exact movement from the collected EMG data amongst others. In this paper, three neural network models are trained using an open EMG dataset to classify and recognize seven different gestures based on the collected EMG data. The three implemented models are: a four-layer deep neural network (DNN), an eight-layer DNN, and a five-layer convolutional neural network (CNN). In addition, five optimizers are tested for each model, namely Adam, Adamax, Nadam, Adagrad, and AdaDelta. It has been found that four layers achieve respectable recognition accuracy of 95% in the proposed model.
The brain dominance is referred to right brain and left brain. The brain dominance can be observed with an Electroencephalogram (EEG) signal to identify different types of electrical pattern in the brain and will form the foundation of one‟s personality. The objective of this project is to analyze brain dominance by using Wavelet analysis. The Wavelet analysis is done in 2-D Gabor Wavelet and the result of 2-D Gabor Wavelet is validated with an establish brain dominance questionnaire. Twenty-one samples from University Malaysia Pahang (UMP) student are required to answer the establish brain dominance questionnaire has been collected in this experiment. Then, brainwave signal will record using Emotiv device. The threshold value is used to remove the artifact and noise from data collected to acquire a smoother signal. Next, the Band-pass filter is applied to the signal to extract the sub-band frequency components from Delta, Theta, Alpha, and Beta. After that, it will extract the energy of the signal from image feature extraction process. Next the features were classified by using K-Nearest Neighbor (K-NN) in two ratios which 70:30 and 80:20 that are training set and testing set (training: testing). The ratio of 70:30 gave the highest percentage of 83% accuracy while a ratio of 80:20 gave 100% accuracy. The result shows that 2-D Gabor Wavelet was able to classify brain dominance with accuracy 83% to 100%.
Embedded system for upper-limb exoskeleton based on electromyography controlTELKOMNIKA JOURNAL
A major problem in an exoskeleton based on electromyography (EMG) control with pattern recognition-based is the need for more time to train and to calibrate the system in order able to adapt for different subjects and variable. Unfortunately, the implementation of the joint prediction on an embedded system for the exoskeleton based on the EMG control with non-pattern recognition-based is very rare. Therefore, this study presents an implementation of elbow-joint angle prediction on an embedded system to control an upper limb exoskeleton based on the EMG signal. The architecture of the system consisted of a bio-amplifier, an embedded ARMSTM32F429 microcontroller, and an exoskeleton unit driven by a servo motor. The elbow joint angle was predicted based on the EMG signal that is generated from biceps. The predicted angle was obtained by extracting the EMG signal using a zero-crossing feature and filtering the EMG feature using a Butterworth low pass filter. This study found that the range of root mean square error and correlation coefficients are 8°-16° and 0.94-0.99, respectively which suggest that the predicted angle is close to the desired angle and there is a high relationship between the predicted angle and the desired angle.
WAVELET DECOMPOSITION METHOD BASED AUTOMATED DIAGNOSIS OF MUSCLE DISEASESIRJET Journal
This document proposes a method for automated diagnosis of muscle diseases using electromyography (EMG) signals. It involves applying wavelet decomposition to EMG signals to extract features. A Hilbert transform is used to represent the EMG signal analytically, and features are calculated from the analytical signal. These features are input to a convolutional neural network (CNN) classifier to categorize the EMG signal and diagnose muscle diseases. Simulation software with MATLAB is used to test this process, with the goal of early and accurate detection of neuromuscular disorders.
This document presents a study comparing muscle activity characterization using standard electromyography (EMG) and a novel non-contact technique called Laser Doppler Myography (LDMi). Three muscle activity parameters - muscle activation timing, signal amplitude, and muscle fatigue - were analyzed using both techniques on the flexor carpi ulnaris and tibialis anterior muscles of 20 subjects. The results showed good correlation between EMG and LDMi for all three parameters, with maximum differences of 440ms in timing and Pearson correlation coefficients above 0.88. This suggests LDMi is a reliable non-contact method for measuring muscle activity characteristics traditionally obtained via EMG.
EEG SIGNAL IDENTIFICATION USING SINGLE-LAYER NEURAL NETWORKIJCI JOURNAL
EEG signal analysis is applied in various fields such as medicine, communication and control. To control based on EEG signals achieved good result, the system must identify effectively EEG signals. In this paper,
a novel approach proposes the EEG signal identification based on image with the EEG signal processing via Wavelet transform and the identification via single-layer neural network. The system model is designed and evaluated with the dataset of 21,000 samples. The accuracy rate can obtain 91.15%.
This document describes a new experimental setup that combines atomic force microscopy (AFM) and micro-electrode arrays (MEAs) to measure the mechanical properties of living cardiac myocytes during contraction and relaxation. The system uses MEAs to non-invasively record the extracellular electrical activity of cardiomyocytes as a timing reference for AFM nanoindentation measurements. This allows quantification of dynamic changes in cell morphology and elasticity with high temporal and spatial resolution during the cardiac cycle. Initial experiments using this integrated AFM-MEA platform demonstrated its ability to measure minimal changes in cardiac myocyte mechanics synchronized to the electrical activity recording.
Multilayer extreme learning machine for hand movement prediction based on ele...journalBEEI
Brain computer interface (BCI) technology connects humans with machines via electroencephalography (EEG). The mechanism of BCI is pattern recognition, which proceeds by feature extraction and classification. Various feature extraction and classification methods can differentiate human motor movements, especially those of the hand. Combinations of these methods can greatly improve the accuracy of the results. This article explores the performances of nine feature-extraction types computed by a multilayer extreme learning machine (ML-ELM). The proposed method was tested on different numbers of EEG channels and different ML-ELM structures. Moreover, the performance of ML-ELM was compared with those of ELM, Support Vector Machine and Naive Bayes in classifying real and imaginary hand movements in offline mode. The ML-ELM with discrete wavelet transform (DWT) as feature extraction outperformed the other classification methods with highest accuracy 0.98. So, the authors also found that the structures influenced the accuracy of ML-ELM for different task, feature extraction used and channel used.
Similar to A Detail Study of Wavelet Families for EMG Pattern Recognition (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
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Most studies to date indicated the performances of CWT and DWT were depending on the selection
of a mother wavelet function [3],[8]-[10]. In the past studies, Kakoty et al. [8] investigated the best mother
wavelet in DWT and CWT at different scale and decomposition level. The authors recommended the
Gaussian and Symlet 8 to be the optimal mother wavelets in CWT and DWT, respectively. Phinyomark et al.
[11] suggested that the use of DWT with the Daubechies 7 and 8 to ensure higher classification accuracy.
Omari et al. [6] studied four mother wavelet functions at four different decomposition levels. The authors
reported Symlet 4 offered the low classification error rate. Previous studies showed that the analysis of best
mother wavelet in WT is critically important, leading to the optimum classification performance. However,
the selection of mother wavelet is remains challenging in many areas.
The best mother wavelet is mostly subject independent, which means different mother wavelet
offers different kind of performance on different subject. In addition, previous works mostly focus on four to
eight mother wavelets in the classification of EMG signals, which is insufficient. Moreover, the performance
of mother wavelet at different scale and decomposition level provide significant difference in classification
performance. It is obvious that the analysis of the mother wavelet in CWT and DWT is remain insufficient
and unclear in EMG pattern recognition. Therefore, this study aims to evaluate the best mother wavelet in
CWT and DWT by employing a large number of mother wavelet functions with different scale and
decomposition level, respectively.
This paper presents a detail study of the selection of mother wavelet in DWT and CWT. 14 mother
wavelets of DWT and 12 mother wavelets of CWT at three different decomposition levels and scales are
investigated, respectively. Two popular features mean absolute value (MAV) and wavelength (WL) are
extracted from each wavelet coefficient for performance evaluation. The multiclass support vector machine
(SVM) is used to classify EMG signal since it offers better performance in previous work [8],[12]. Finally,
the best mother wavelet of DWT and CWT that offer the best classification performance are pointed.
2. MATERIAL AND RESEARCH METHOD
2.1. EMG data collection
This study was performed on ten healthy subjects (8 males and 2 females) with mean age of 28.6
( 𝝈=9.7) years. Each subject provided informed consent to participate in the experiment. Additionally, all
subjects were free from neurological and muscular disorder. Two wearable EMG devices named Shimmer
(Shimmer3 Consensys EMG Development Kits) with standard setting were used in data collection. The
resolution was set at 24 bits with a gain of 12. The EMG signal was gathered from four useful hand muscles
namely extensor digitorum (ch1), flexor carpi radialis (ch2), extensor carpi radialis longus (ch3) and flexor
carpi ulnaris (ch4) with two reference electrodes at the elbow. The signal was sampled at 1024 Hz and band-
pass filtered between 20 and 500 Hz. The skin was shaved and cleaned with alcohol pad before the electrode
placement. The surface electrodes with 30 mm diameter were used and the inter-electrode distance was set at
20 mm to reduce the crosstalk. The bipolar electrode configuration was shown in Figure 1.
Figure 1. Electrodes configuration
Subject was seated comfortably on a chair with the hand in neutral position. The surface EMG
signals were recorded as the subject performed ten different hand movements including thumb flexion (M1),
thumb extension (M2), wrist flexion (M3), wrist extension (M4), making a fist (M5), pinch index to the
thumb (M6), pinch middle to the thumb (M7), pinch ring to the thumb (M8), pinch little to the thumb (M9)
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and rest (M10). The experiments consisted of ten trials. Within each trial, the subject was asked to perform
ten different hand movements for 5 s each, followed by a resting state of 4 s. Moreover, a resting period of 1
min was introduced at the end of trial to avoid mental and muscle fatigue. The resting state was removed
before data segmentation.
A recent report of real time EMG application indicated that the optimal window length was ranging
from 150 to 250 ms to balance the controller delay and classification error rate [13]. Additionally, an
overlapped windowing technique was introduced to produce better classification accuracy in EMG pattern
recognition [14]. In this work, the EMG data were divided into 250 ms window (256 samples) with 50% (128
samples) overlapped. In total, a data matrix of 39 segments 256 samples 4 channels were obtained from
each movement from each subject.
Figure 2 shows the flow diagram of the proposed recognition system. In the first stage, the raw
EMG signals are collected and segmented. Next, MAV and WL features are extracted from CWT and
reconstructed DWT coefficients at different scale and decomposition level using different mother wavelet,
respectively. In the final stage, the SVM is used to recognize the EMG signals of ten different hand
movements.
Figure 2. The flow diagram of the proposed recognition system
2.2. Wavelet Transform
Wavelet transform (WT) is a powerful mathematical tool that is successful in the analysis of bio-
signal including EMG signal. WT offers high frequency resolution for low frequency component and good
time resolution for the high frequency component [13]. Generally, WT can be categorized into continuous
and discrete forms. Continuous wavelet transform (CWT) decomposes the signal based on the dilations and
translations of a single mother wavelet function. CWT is more consistent and efficient because it provides
localization time-frequency information without down-sampling [11]. Additionally, CWT is continuous in
term of shifting and it gives useful time-frequency information [15]. CWT can be defined as:
s,(s, ) ( ) ( )x bCWT b x t t dt (1)
where x(t) is the input signal and ψs,b(t) is the transformation of the mother wavelet function.
The transformation can be expressed as:
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s,
1
( )b
t b
t
ss
(2)
where s is the scaling parameter, b is referred to the translation parameter and 𝛹(t) is called mother wavelet.
The variables s and b provide the time scaling and shifting operation, respectively [16]. By using equation 1
and 2, CWT can be computed as:
1
(s, ) ( )
t b
CWT b x t dt
ss
(3)
Figure 3 demonstrates the scalogram of CWT at scale 32 using Mexican hat wavelet. The yellow
areas represent higher amplitude at each scale. In turn, dark blue areas refer to low amplitude.
Figure 3. Scalogram of continuous wavelet transform at scale 32 using Mexican hat
Discrete wavelet transform (DWT) is derived from CWT [17]. DWT is more widely used because it
offers low computation cost [11]. In DWT, the signal is decomposed into the approximation and detail
coefficient which involves the change of sampling rate [18]. The decomposition of DWT comprises of two
digital filters, which are high-pass and low-pass filters. The low-pass and high-pass filter down-sample the
input signal and provide the approximation, A and detail, D, respectively [11],[19]. For each decomposition
level, the filters down-sample the signal by the factor of 2. The first level of decomposition is defined as:
D[ ] [k] [2 ]
n
n x h n k
(4)
A[n] [k] [2 ]
n
x g n k
(5)
where x[k] is the input signal, D[n] is referred to the detail, D1 and A[n] is the approximation, A1.
The decomposition process is repeated until the desired final level is achieved. In the previous research, each
coefficient subset was reconstructed to obtain more reliable EMG signal part, resulting in better classification
accuracy [3],[13]. Therefore, the inverse wavelet transform is used to reconstruct each wavelet coefficient
into more effective subset, namely, estimated approximation, rA and estimated detail, rD. For example, the
estimated subset rD3 is obtained by performing the inverse wavelet transform on third-level detail, D3.
The wavelet reconstruction of estimated detail (rD1-rD6) and estimated approximation (rA1-rA6) were shown
in Figure 4.
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Figure 4. Wavelet reconstruction of DWT at sixth decomposition level using Symlet 4
2.3. Mother Wavelet Selection and Evaluation
Recent studies indicated WT has been recognized as one of the best time-frequency method in
biomedical signal processing [3],[18],[20]. However, the performance of WT is mostly depending on the
mother wavelet function. The selection of mother wavelet is remained challenging in many areas. Therefore,
this work aims to evaluate the best mother wavelet in DWT and CWT for EMG signal processing. In this
study, 14 mother wavelets in DWT and 12 mother wavelets in CWT are investigated. Table 1 is a lookup
table of the mother wavelet used in CWT and DWT. It is worth noting different scale and decomposition
level in CWT and DWT provide different property. For this reason, the performance of the mother wavelet at
the scale 8, 16, 32 and decomposition level of 2, 4 and 6 are examined.
Table 1. Mother wavelet of CWT and DWT used in this study
CWT DWT
1 Haar Haar
2 Daubechies 2 Daubechies 2
3 Daubechies 4 Daubechies 4
4 Daubechies 6 Daubechies 6
5 Symlet 2 Daubechies 8
6 Symlet 4 Daubechies 10
7 Symlet 6 Symlet 2
8 Morlet Symlet 4
9 Mayer Symlet 6
10 Mexicanhat Symlet 8
11 Gaussian 2 Coiflet 2
12 Gaussian 4 Coiflet 3
13 - Coiflet 4
14 - Coiflet 5
2.4. Feature Extraction using Wavelet Transform
Feature extraction is an essential step to reduce the dimensionality and extract the useful information
from the signal. In this work, wavelength (WL) and mean absolute value (MAV) are extracted from each
wavelet coefficient. MAV and WL can be expressed as [6]:
1
1 L
n
n
MAV x
L
(6)
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1
1
1
L
n n
n
WL x x
(7)
where Xn is the input signal and L is the length of signal.
2.5. Support Vector Machine
Support vector machine (SVM) has been proved to be an outstanding supervised machine learning
method in EMG pattern recognition [14]. Moreover, SVM has shown its superiority, especially for non-linear
and high dimensional pattern recognition [21]. SVM maps the predictors onto a high dimensional space by
using the concept of hyperplane partition for the data [22]. Some drawbacks of SVM are the complexity of
the selection of kernel function and the longer computation time [14]. A previous study reported that radial
basis function (RBF) was the best kernel function because it gave a higher classification performance [6]. In
this regard, SVM with RBF kernel function is applied and it can be defined as:
2
2
|| ||
( , ) exp
2
i
i
x x
K x x
(8)
where x-xi is the Euclidean distance between feature vectors and 𝜎 is the kernel parameter.
3. RESULTS AND ANALYSIS
In this work, 10-fold cross validation is applied in the classification of EMG signals. The data is
separated into 10 equal parts. Every part takes turn to test and the remaining parts are used in training phase.
In the first part of the experiments, 14 mother wavelet functions in DWT at the three different decomposition
level are evaluated. Table 2 outlines the mean classification accuracy of 14 mother wavelets of DWT at a
decomposition level of 2, 4 and 6 across ten different subjects. From the results, the mean classification
accuracy is found to be above 97% for all 14 mother wavelet functions in both WL and MAV feature sets.
Additionally, MAV has shown to be an effective and reliable feature because it offers better performance in
discriminating EMG patterns. By employing MAV feature, it is obvious that the highest classification
accuracy is obtained by Symlet 4 (98.74%), followed by Daubechies 4 (98.72%) at the second decomposition
level. On the other hand, Coiflet 3 outperforms other mother wavelets with a mean classification accuracy of
98.49% at the fourth decomposition level when WL is used. From the analysis, Symlet 4 and Daubechies 4 at
the second decomposition level are found to be the most suitable mother wavelet in DWT.
Table 2. Classification Accuracy (mean ± STD) of 14 Mother Wavelets of DWT at Three Different
Decomposition Level Across Ten Subjects
Mother wavelet
Classification performance (%)
Mother wavelet
Classification performance (%)
WL MAV WL MAV
Haar
Level 2 97.90 ± 1.02 98.43 ± 0.88 Sym 4 Level 2 98.09 ± 0.92 98.74 ± 0.66
Level 4 98.00 ± 0.90 98.28 ± 0.80 Level 4 98.36 ± 0.78 98.53 ± 0.67
Level 6 97.28 ± 0.94 97.64 ± 0.85 Level 6 97.50 ± 0.79 97.67 ± 0.81
Db 2
Level 2 97.97 ± 1.01 98.63 ± 0.68 Sym 6 Level 2 98.18 ± 0.87 98.67 ± 0.76
Level 4 98.31 ± 0.72 98.44 ± 0.70 Level 4 98.39 ± 0.67 98.55 ± 0.68
Level 6 97.32 ± 0.94 97.45 ± 0.78 Level 6 97.58 ± 0.84 97.65 ± 0.87
Db 4
Level 2 98.08 ± 0.88 98.72 ± 0.67 Sym 8 Level 2 98.19 ± 0.87 98.70 ± 0.71
Level 4 98.36 ± 0.78 98.56 ± 0.68 Level 4 98.45 ± 0.69 98.57 ± 0.72
Level 6 97.36 ± 0.91 97.55 ± 0.82 Level 6 97.67 ± 0.87 97.74 ± 0.85
Db 6
Level 2 98.23 ± 0.90 98.65 ± 0.73 Coif 2 Level 2 98.10 ± 0.90 98.69 ± 0.70
Level 4 98.48 ± 0.63 98.53 ± 0.69 Level 4 98.34 ± 0.79 98.52 ± 0.66
Level 6 97.48 ± 0.88 97.52 ± 0.85 Level 6 97.59 ± 0.91 97.70 ± 0.77
Db 8
Level 2 98.20 ± 0.90 98.69 ± 0.71 Coif 3 Level 2 98.18 ± 0.90 98.69 ± 0.71
Level 4 98.44 ± 0.67 98.59 ± 0.67 Level 4 98.49 ± 0.70 98.62 ± 0.62
Level 6 97.57 ± 0.74 97.60 ± 0.82 Level 6 97.56 ± 0.85 97.71 ± 0.73
Db 10
Level 2 98.17 ± 0.94 98.70 ± 0.68 Coif 4 Level 2 98.22 ± 0.93 98.70 ± 0.70
Level 4 98.48 ± 0.66 98.62 ± 0.60 Level 4 98.42 ± 0.74 98.56 ± 0.68
Level 6 97.43 ± 0.91 97.49 ± 0.88 Level 6 97.61 ± 0.83 97.71 ± 0.72
Sym 2
Level 2 97.97 ± 1.01 98.63 ± 0.68 Coif 5 Level 2 98.23 ± 0.88 98.70 ± 0.71
Level 4 98.31 ± 0.72 98.44 ± 0.70 Level 4 98.45 ± 0.72 98.56 ± 0.59
Level 6 97.32 ± 0.94 97.45 ± 0.78 Level 6 97.50 ± 0.86 97.59 ± 0.85
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In the second part of the experiments, 12 mother wavelets of CWT are studied. Table 3
demonstrates the mean classification accuracy of 12 mother wavelets of CWT at scale 8, 16 and 32 for ten
different subjects. At scale 8, Gaussian 2 and 4 exhibit the highest classification accuracy of 98.42% using
WL and MAV feature sets, respectively. However, their performance did not show much improvement at a
higher scale. At scale 16, it has been found that the Symlet 6 achieves the best classification accuracy of
98.56%, followed by Symlet 4, 98.53% when MAV is used. For instance, the Mexican hat has shown its
superiority at scale 32 with the best mean classification accuracy of 98.64% in WL feature set. Unfortunately,
MAV shows the decrement in classification performance at scale 32. This shows that MAV feature set is not
suitable for high scale wavelet function in CWT. As a result, the most suitable mother wavelet in CWT are
Mexican hat at scale 32 and Symlet 6 at scale 16.
Table 3. Classification Accuracy (mean ± STD) of 12 Mother Wavelets of CWT at Three Different Scale
Across Ten Subjects
Mother wavelet
Classification performance (%)
Mother wavelet
Classification performance (%)
WL MAV WL MAV
Haar
Scale 8 97.70 ± 0.96 98.00 ± 1.08 Sym 6 Scale 8 98.05 ± 0.86 98.17 ± 0.97
Scale 16 98.38 ± 0.92 98.31 ± 0.96 Scale 16 98.48 ± 0.81 98.56 ± 0.74
Scale 32 98.51 ± 0.76 98.19 ± 0.79 Scale 32 98.49 ± 0.72 98.35 ± 0.73
Db 2
Scale 8 97.88 ± 1.01 98.06 ± 1.13 Morl Scale 8 98.00 ± 0.83 98.07 ± 0.83
Scale 16 98.44 ± 0.88 98.42 ± 0.86 Scale 16 98.40 ± 0.86 98.40 ± 0.82
Scale 32 98.50 ± 0.72 98.29 ± 0.73 Scale 32 98.34 ± 0.74 98.26 ± 0.78
Db 4
Scale 8 97.99 ± 0.92 98.13 ± 1.03 Meyr Scale 8 98.06 ± 0.95 98.13 ± 0.95
Scale 16 98.46 ± 0.90 98.47 ± 0.78 Scale 16 98.45 ± 0.84 98.49 ± 0.75
Scale 32 98.45 ± 0.76 98.27 ± 0.76 Scale 32 98.36 ± 0.79 98.29 ± 0.81
Db 6
Scale 8 97.94 ± 1.01 98.08 ± 1.08 Mexh Scale 8 98.36 ± 0.82 98.15 ± 0.79
Scale 16 98.41 ± 0.93 98.46 ± 0.78 Scale 16 98.08 ± 0.76 97.49 ± 0.81
Scale 32 98.36 ± 0.75 98.27 ± 0.76 Scale 32 98.64 ± 0.66 96.26 ± 1.00
Sym 2
Scale 8 97.88 ± 1.01 98.06 ± 1.13 Gaus 2 Scale 8 98.42 ± 0.83 98.35 ± 0.84
Scale 16 98.44 ± 0.88 98.42 ± 0.86 Scale 16 98.28 ± 0.76 98.00 ± 0.77
Scale 32 98.50 ± 0.72 98.29 ± 0.73 Scale 32 98.50 ± 0.67 97.01 ± 0.87
Sym 4
Scale 8 98.03 ± 0.87 98.18 ± 0.99 Gaus 4 Scale 8 98.39 ± 0.93 98.42 ± 0.83
Scale 16 98.48 ± 0.83 98.53 ± 0.74 Scale 16 98.48 ± 0.70 98.42 ± 0.71
Scale 32 98.52 ± 0.69 98.34 ± 0.74 Scale 32 98.31 ± 0.69 97.80 ± 0.77
In the final part of the experiments, the paired two-tail t-test is used to measure the statistical
difference between the classification performances of WL and MAV features when different mother wavelet
function is used. Table 4 and 5 outline the result of t-test of the classification performance obtained from
DWT and CWT across ten subjects. In t-test, the null hypothesis is rejected if the p-value is less than 0.05.
This shows that there is a statistical difference between WL and MAV feature sets.
From Table 4, the results of the WL and MAV are statistical difference for all wavelet functions at
the second decomposition level. At fourth decomposition level, the p-value illustrates that the Daubechies 6
and Coiflet 5 show no significant difference when WL versus MAV. At sixth decomposition level, only
Haar, Daubechies 4 and Symlet 4 exhibit the significant difference. From Table 5, Haar, Symlet 4 and
Mexican hat show significant difference in scale 8. Additionally, at scale 16, only Mexican hat, Gaussian 2
and Gaussian 4 obtain p-value lower than 0.05. Moreover, other than Daubechies 6 and Symlet 6 exhibit
significant differences between the classification performance of WL and MAV at scale 32.
Table 4. Result of t-test of the Classification Performance between MAV and WL using DWT
Mother wavelet
p – value
Level 2 Level 4 Level 6
Haar 0.0007 0.0007 3E–05
Db 2 0.0012 0.0195 0.0521
Db 4 0.0006 0.0087 0.0031
Db 6 0.0070 0.3754 0.2340
Db 8 0.0037 0.0185 0.6085
Db 10 0.0020 0.0036 0.3163
Sym 2 0.0012 0.0195 0.0521
Sym 4 0.0009 0.0057 0.0138
Sym 6 0.0008 0.0046 0.0380
Sym 8 0.0007 0.0289 0.1081
Coif 2 0.0012 0.0178 0.0854
Coif 3 0.0031 0.0109 0.0625
Coif 4 0.0031 0.0157 0.0504
Coif 5 0.0010 0.0860 0.0807
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Table 5. Result of t-test of the Classification Performance between MAV and WL using CWT.
Mother wavelet
p – value
Scale 8 Scale 16 Scale 32
Haar 0.0377 0.2676 0.0003
Db 2 0.0539 0.8141 0.0003
Db 4 0.1104 0.8478 0.0104
Db 6 0.0525 0.3855 0.0670
Sym 2 0.0539 0.8141 0.0003
Sym 4 0.0409 0.5256 0.0037
Sym 6 0.0625 0.2172 0.0526
Morl 0.0635 0.9162 0.0050
Meyr 0.1266 0.3865 0.0207
Mexh 3E–05 7E–07 2E–06
Gaus 2 0.2864 7E–05 7E–07
Gaus 4 0.5683 0.0334 5E–05
4. CONCLUSION
In this study, the usefulness of the mother wavelet function in DWT and CWT has been
investigated. Two popular features, WL and MAV are extracted from the wavelet coefficients as the input to
the SVM. In CWT, the Mexican hat at scale 32 and Symlet 6 at scale 16 are suggested to be the optimal
mother wavelet selection for the classification of EMG signals. On the other hand, the reconstructed DWT
coefficient with Daubechies 4 and Symlet 4 at second decomposition level are recommended to be used in
EMG pattern recognition. The experimental results indicated DWT not only offered low computation cost,
but also yielded a high classification accuracy. As compared to CWT, DWT is more approariate to be used in
rehabilitation and clinical application.
ACKNOWLEDGEMENTS
The authors would like to thank the Universiti Teknikal Malaysia Melaka (UTeM), Skim Zamalah
UTeM and Minister of Higher Education Malaysia (MOHE) for funding research under grant
PJP/1/2017/FKEKK/H19/S01526.
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BIOGRAPHIES OF AUTHORS
Too Jing Wei has received his B. Eng. from Universiti Teknikal Malaysia in 2017. He is currently
pursuing his Master Eng. in Universiti Teknikal Malaysia. His research areas are in signal
processing, classification and feature selection for EMG pattern recognition.
Associate Prof. Dr. Abdul Rahim Bin Abdullah has received his B. Eng., Master Eng., PhD Degree
from Universiti Teknologi Malaysia in 2001, 2004 and 2011 in Electrical Engineering and Digital
Signal Processing respectively. He is currently an Associate Professor with the Department of
Electrical Engineering for Universiti Teknikal Malaysia Melaka (UTeM).
Dr. Norhashimah Binti Mohd Saad is currently working as a senior lecturer in Department
Computer, FKEKK, UTeM. She finished her study in Bachelor of Engineering, Master of
Engineering and PhD in Medical Image Processing from UTM, Malaysia.