1) The document proposes a new approach for automatic gender recognition based on fusing features extracted from electromyography (EMG) and heart rate variability (HRV) signals recorded during stepping exercises.
2) The feature fusion approach chains the feature vectors extracted from the EMG and HRV signals into a single composite feature vector, which is then used for classification.
3) Experimental results on 10 subjects show that the feature fusion approach achieves 80% sensitivity and nearly 100% specificity for gender recognition using a 1-nearest neighbor classifier, outperforming models based solely on the EMG or HRV signals.
Epilepsy is one of the prominent and disturbing neurological disorder and many
people across the world are victims of this problem. The sudden motor disturbances in
the brain cause and trigger these seizures. Due to the hypersynchronous discharges
happening on the cortical regions of the brain, the activities of the motor becomes
abnormal and so seizures are triggered. The seizures caused due to epilepsy are quite
heterogeneous in nature and so diagnosing it is quite challenging.
Electroencephalography (EEG) is the most widely used instrument for the detection of
epileptic seizures. In this work, Haar and Sym8 wavelets are employed to extract the
wavelet features at level 4 from EEG signals. The extracted features like alpha, beta,
theta, gamma and delta are classified through the Soft Discriminant Classifier (SDC) to
obtain the epilepsy risk level from EEG signals. The final results show that when Haar
wavelet is employed and classified with SDC, an average classification accuracy of
95.20% is obtained and when Sym8 wavelet is utilized and classified with SDC, an
average classification accuracy of 94.68% is obtained.
Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic ...CSCJournals
The electrocardiograph (ECG) contains cardiac features unique to each individual. By analyzing ECG, it should therefore be possible not only to detect the rate and consistency of heartbeats but to also extract other signal features in order to identify ECG records belonging to individual subjects. In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Eighteen temporal, amplitude, width and autoregressive (AR) model parameters are extracted from each ECG beat and classified in order to identify each individual. Proposed system uses pre-processing stage to decrease the effects of noise and other unwanted artifacts usually present in raw ECG data. Following pre-processing steps, ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. Window estimation is based on the localization of the R peaks in the ECG stream that detected by Filter bank method for QRS complex detection. ECG features – temporal, amplitude and AR coefficients are then extracted and used as an input to K-nn and SVM classification algorithms in order to identify the individual subjects and beats. Signal pre-processing techniques, applied feature extraction methods and some intermediate and final classification results are presented in this paper.
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.
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.
Sleep Apnea Identification using HRV Features of ECG Signals IJECEIAES
Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subjectspecific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features.
Epilepsy is one of the prominent and disturbing neurological disorder and many
people across the world are victims of this problem. The sudden motor disturbances in
the brain cause and trigger these seizures. Due to the hypersynchronous discharges
happening on the cortical regions of the brain, the activities of the motor becomes
abnormal and so seizures are triggered. The seizures caused due to epilepsy are quite
heterogeneous in nature and so diagnosing it is quite challenging.
Electroencephalography (EEG) is the most widely used instrument for the detection of
epileptic seizures. In this work, Haar and Sym8 wavelets are employed to extract the
wavelet features at level 4 from EEG signals. The extracted features like alpha, beta,
theta, gamma and delta are classified through the Soft Discriminant Classifier (SDC) to
obtain the epilepsy risk level from EEG signals. The final results show that when Haar
wavelet is employed and classified with SDC, an average classification accuracy of
95.20% is obtained and when Sym8 wavelet is utilized and classified with SDC, an
average classification accuracy of 94.68% is obtained.
Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic ...CSCJournals
The electrocardiograph (ECG) contains cardiac features unique to each individual. By analyzing ECG, it should therefore be possible not only to detect the rate and consistency of heartbeats but to also extract other signal features in order to identify ECG records belonging to individual subjects. In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Eighteen temporal, amplitude, width and autoregressive (AR) model parameters are extracted from each ECG beat and classified in order to identify each individual. Proposed system uses pre-processing stage to decrease the effects of noise and other unwanted artifacts usually present in raw ECG data. Following pre-processing steps, ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. Window estimation is based on the localization of the R peaks in the ECG stream that detected by Filter bank method for QRS complex detection. ECG features – temporal, amplitude and AR coefficients are then extracted and used as an input to K-nn and SVM classification algorithms in order to identify the individual subjects and beats. Signal pre-processing techniques, applied feature extraction methods and some intermediate and final classification results are presented in this paper.
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.
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.
Sleep Apnea Identification using HRV Features of ECG Signals IJECEIAES
Sleep apnea is a common sleep disorder that interferes with the breathing of a person. During sleep, people can stop breathing for a moment that causes the body lack of oxygen that lasts for several seconds to minutes even until the range of hours. If it happens for a long period, it can result in more serious diseases, e.g. high blood pressure, heart failure, stroke, diabetes, etc. Sleep apnea can be prevented by identifying the indication of sleep apnea itself from ECG, EEG, or other signals to perform early prevention. The purpose of this study is to build a classification model to identify sleep disorders from the Heart Rate Variability (HRV) features that can be obtained with Electrocardiogram (ECG) signals. In this study, HRV features were processed using several classification methods, i.e. ANN, KNN, N-Bayes and SVM linear Methods. The classification is performed using subjectspecific scheme and subject-independent scheme. The simulation results show that the SVM method achieves higher accuracy other than three other methods in identifying sleep apnea. While, time domain features shows the most dominant performance among the HRV features.
Moving One Dimensional Cursor Using Extracted ParameterCSCJournals
This study focuses on developing a method to determine parameters to control cursor movement using noninvasive brain signals, or electroencephalogram (EEG) for brain-computer interface (BCI). There were two conditions applied i.e. Control condition where subjects relax (resting state); and Task condition where subjects imagine a movement. During both conditions, EEG signals were recorded from 19 scalp locations. In Task condition, subjects were asked to imagine a movement to move the cursor on the screen towards target position. Fast Fourier Transform (FFT) was used to analyse the recorded EEG signals. To obtain maximum speed and accuracy, EEG data were divided into various interval and difference in power values between Task and Control conditions were calculated. As conclusion, the present study suggests that difference in delta frequency band between resting and active imagination may be use to control one dimensional cursor movement and the region that gives optimum output is at the parietal region.
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.
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.
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.
Training feedforward neural network using genetic algorithm to diagnose left ...TELKOMNIKA JOURNAL
In this research work, a new technique was proposed for the diagnosis of left ventricular hypertrophy (LVH) from the ECG signal. The advanced imaging techniques can be used to diagnose left ventricular hypertrophy, but it leads to time-consuming and more expensive. This proposed technique overcomes thesef issues and may serve as an efficient tool to diagnose the LVH disease. The LVH causes changes in the patterns of ECG signal which includes R wave, QRS and T wave. This proposed approach identifies the changes in the pattern and extracts the temporal, spatial and statistical features of the ECG signal using windowed filtering technique. These features were applied to the conventional classifier and also to the neural network classifier with the modified weights using a genetic algorithm. The weights were modified by combining the crossover operators such as crossover arithmetic and crossover two-point operator. The results were compared with the various classifiers and the performance of the neural network with the modified weights using a genetic algorithm is outperformed. The accuracy of the weights modified feedforward neural network is 97.5%.
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.
Detection of Parkinson’s disease (PD) at an early stage
is necessary for its treatment. The commonly used methods
available in the literature use observation of certain symptoms
such as Tremor, Loss of Smell and Troubled Sleeping, Moving or
Walking. The motion pattern in this disease can be characterized
by a spatio-temporal phenomenon that signifies gait recognition as
reported in the literature. However, non-invasive methods such as
use of Gait image sequences are handy in terms of cost and
comfort. In this paper we propose a statistical approach for
detection of Parkinson’s diseases by considering segmental feature
of gait image sequences by using Hidden Markov Model (HMM).
A set of key features from the image frames is identified during
the gait cycle. The input binary silhouette images are preprocessed
by morphological operations to fill the holes and remove noise. An
image feature vector is created from the outer contour of the
image sequences. From the feature vectors of the gait cycle, a set
of initial exemplars is constructed. The similarity between the
feature vector and the exemplar is measured by the inner product
distance. An HMM is trained iteratively using the Viterbi
algorithm and Baum-Welch algorithm and then used for detection
of Parkisonian gait. The characteristics of one dimensional HMM
best fit to one dimensional image vector thus the proposed method
reduces image feature from the two-dimensional plane to a onedimensional
vector. The statistical nature of the HMM makes it
robust to PD gait representation and recognition. The proposed
HMM-based method in LabVIEW and MATLAB is evaluated
using the CMU MoBo database as well as our own prepared
database for PD detection
A comparative study of wavelet families for electromyography signal classific...journalBEEI
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.
EFFECT OF POSTURAL CONTROL BIOMECHANICAL GAIN ON PSYCHOPHYSICAL DETECTION THR...ijbbjournal
A Sliding Linear Investigative Platform for Assessing Lower Limb Stability (SLIP-FALLS) was employed to study postural control biomechanical reaction to external perturbations in a short ≤16mm postural perturbation. Head acceleration were evaluated while blindfolded subjects stood on a platform that was given a short anterior perturbation presented in one of 2 sequential 4s intervals (2-Alternative-ForcedChoice) for a set of 30 trials. Anterior-Posterior head acceleration (Head Accl AP) were investigated among the movement and non- movement intervals for the healthy adults. A strong ringing signal was observed in Head Accl AP movement interval that was absent in non-movement interval. A positive power
law trading relationship was found between Head Accl AP gain and move length standing blindfolded subjects. This could explain the observed negative power law relationship between translation length and peak acceleration threshold in previous psychophysical detection threshold studies.
A study of variations in an athlete’s reaction time performance based on the ...Sports Journal
The purpose of this study was to investigate the difference in the reaction time responses of an athlete
based on various types of stimuli. Reaction time is duration between applications of a stimulus to onset of
response. The present study was measured reaction time in 197 athletes, for the comparison in groups
which were into 3 categories 1. Gender wise (Female and Male), 2. Game wise (Individual and Team), 3.
Standard wise (5th, 6th, 7th, 8th, 9th, 10th) and correlation was done between the group based on the 3 tests.
The VRT, SRT and ViRT was measured by the Jerry (Version: 0.6.4) software. During the reaction time
testing visual, sound and tactile stimuli were given for five times and average reaction time after omitting
highest and lowest reaction time, was taken as the final reaction time. Results suggest that a comparison
was done between the performance of male and female athletes and no significant difference was seen in
their performance in all the three test. Similarly a comparison was also done based on athletes playing a
team and individual game and a significant difference was seen in all the three test (VRT: F = 11.538, p =
0.001); (SRT: F = 8.546, p = 0.004); (ViRT: F = 27.240, p = 0.001). Further a comparison was also done
based on the standard in which the athletes study and it was seen that there is significant difference in
two of test (VRT: F = 4.287, p = 0.001); (ViRT: F = 5.434, p = 0.001). Co-relational analysis was also
done based on gender, and a significant negative correlation was found in females VRT and SRT (r = -
.285, p = .001) and the males showed a significantly positive correlation in VRT and ViRT (r = .243, p =
.001) and a significant negative correlation in SRT and ViRT (r = -.353, p = .001). Further, the
correlation done based on individual and team game. A significant negative correlation was found in
individual game athletes VRT and SRT (r = -.532, p = .001) and a positive correlation between SRT and
ViRT (r = .104, p = .001). The team game athletes showed a significant negative correlation in SRT and
ViRT (r = -.462, p = .001). The correlation was done based on standards athletes. It was seen that in 5th
standard a significant negative correlation was found between SRT and ViRT (r = -.764, p = .001), in 6th
standard a significant negative correlation was found in VRT and SRT (r = -.554, p = .001), in 7th
standard a significant negative correlation was found between VRS and SRS (r = -.396, p = .001), and
SRT and ViRT (r = -.381, p = .001). There was no correlation found in 8th standard. In 9th standard a
significant negative correlation was found in SRT and ViRT (r = -.446, p = .001). In 10th standard a
significant negative correlation was found in VRT and SRT (r = -.554, p = .001).
CURRENT STATUS AND FUTURE RESEARCH DIRECTIONS IN MONITORING VIGILANCE OF INDI...ijsc
Working in monotonous environment often causes lack of concentration or fatigue in an operator and many
times such non-vigilance leads to accidents. Therefore early detection of fatigued state has become
essential in monotonous working environments like driving vehicle, operating machines etc. Such fatigued
state often gets developed gradually and can be identified by certain symptoms. Different types of symptoms
help in modelling non-vigilance in different ways. This paper reviews and compares current status of
research in modelling fatigue where fatigue is modelled using probabilistic models, machine learning
models, finite state machine etc. The paper also presents possible future research directions in the same
field like identifying non-fatigue non-vigilance mental states, extending non-vigilance monitoring for mass
audience etc.
Extraction of respiratory rate from ppg signals using pca and emdeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Extraction of respiratory rate from ppg signals using pca and emdeSAT Journals
Abstract Photoplethysmography is a non-invasive electro-optic method developed by Hertzman, which provides information on the blood volume flowing at a particular test site on the body close to the skin. PPG waveform contains two components; one, attributable to the pulsatile component in the vessels, i.e. the arterial pulse, which is caused by the heartbeat, and gives a rapidly alternating signal (AC component). The second one is due to the blood volume and its change in the skin which gives a steady signal that changes very slowly (DC component). PPG signal consists of not only the heart-beat information but also a respiratory signal. Estimation of respiration rates from Photoplethysmographic (PPG) signals would be an alternative approach for obtaining respiration related information.. There have been several efforts on PPG Derived Respiration (PDR), these methods are based on different signal processing techniques like filtering, wavelets and other statistical methods, which work by extraction of respiratory trend embedded into various physiological signals. PCA identifies patterns in data, and expresses the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool for analyzing such data. Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The basis functions, called Intrinsic Mode Functions (IMFs) represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. The contribution reviews the technique of EMD and related algorithms and discusses illustrative applications. Test results on PPG signals of the well known MIMIC database from Physiobank archive reveal that the proposed EMD method has efficiently extracted respiratory information from PPG signals. The evaluated similarity parameters in both time and frequency domains for original and estimated respiratory rates have shown the superiority of the method. Index Terms: Respiratory signal, PPG signal, Principal Component Analysis, EMD, ECG
Moving One Dimensional Cursor Using Extracted ParameterCSCJournals
This study focuses on developing a method to determine parameters to control cursor movement using noninvasive brain signals, or electroencephalogram (EEG) for brain-computer interface (BCI). There were two conditions applied i.e. Control condition where subjects relax (resting state); and Task condition where subjects imagine a movement. During both conditions, EEG signals were recorded from 19 scalp locations. In Task condition, subjects were asked to imagine a movement to move the cursor on the screen towards target position. Fast Fourier Transform (FFT) was used to analyse the recorded EEG signals. To obtain maximum speed and accuracy, EEG data were divided into various interval and difference in power values between Task and Control conditions were calculated. As conclusion, the present study suggests that difference in delta frequency band between resting and active imagination may be use to control one dimensional cursor movement and the region that gives optimum output is at the parietal region.
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.
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.
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.
Training feedforward neural network using genetic algorithm to diagnose left ...TELKOMNIKA JOURNAL
In this research work, a new technique was proposed for the diagnosis of left ventricular hypertrophy (LVH) from the ECG signal. The advanced imaging techniques can be used to diagnose left ventricular hypertrophy, but it leads to time-consuming and more expensive. This proposed technique overcomes thesef issues and may serve as an efficient tool to diagnose the LVH disease. The LVH causes changes in the patterns of ECG signal which includes R wave, QRS and T wave. This proposed approach identifies the changes in the pattern and extracts the temporal, spatial and statistical features of the ECG signal using windowed filtering technique. These features were applied to the conventional classifier and also to the neural network classifier with the modified weights using a genetic algorithm. The weights were modified by combining the crossover operators such as crossover arithmetic and crossover two-point operator. The results were compared with the various classifiers and the performance of the neural network with the modified weights using a genetic algorithm is outperformed. The accuracy of the weights modified feedforward neural network is 97.5%.
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.
Detection of Parkinson’s disease (PD) at an early stage
is necessary for its treatment. The commonly used methods
available in the literature use observation of certain symptoms
such as Tremor, Loss of Smell and Troubled Sleeping, Moving or
Walking. The motion pattern in this disease can be characterized
by a spatio-temporal phenomenon that signifies gait recognition as
reported in the literature. However, non-invasive methods such as
use of Gait image sequences are handy in terms of cost and
comfort. In this paper we propose a statistical approach for
detection of Parkinson’s diseases by considering segmental feature
of gait image sequences by using Hidden Markov Model (HMM).
A set of key features from the image frames is identified during
the gait cycle. The input binary silhouette images are preprocessed
by morphological operations to fill the holes and remove noise. An
image feature vector is created from the outer contour of the
image sequences. From the feature vectors of the gait cycle, a set
of initial exemplars is constructed. The similarity between the
feature vector and the exemplar is measured by the inner product
distance. An HMM is trained iteratively using the Viterbi
algorithm and Baum-Welch algorithm and then used for detection
of Parkisonian gait. The characteristics of one dimensional HMM
best fit to one dimensional image vector thus the proposed method
reduces image feature from the two-dimensional plane to a onedimensional
vector. The statistical nature of the HMM makes it
robust to PD gait representation and recognition. The proposed
HMM-based method in LabVIEW and MATLAB is evaluated
using the CMU MoBo database as well as our own prepared
database for PD detection
A comparative study of wavelet families for electromyography signal classific...journalBEEI
Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.
EFFECT OF POSTURAL CONTROL BIOMECHANICAL GAIN ON PSYCHOPHYSICAL DETECTION THR...ijbbjournal
A Sliding Linear Investigative Platform for Assessing Lower Limb Stability (SLIP-FALLS) was employed to study postural control biomechanical reaction to external perturbations in a short ≤16mm postural perturbation. Head acceleration were evaluated while blindfolded subjects stood on a platform that was given a short anterior perturbation presented in one of 2 sequential 4s intervals (2-Alternative-ForcedChoice) for a set of 30 trials. Anterior-Posterior head acceleration (Head Accl AP) were investigated among the movement and non- movement intervals for the healthy adults. A strong ringing signal was observed in Head Accl AP movement interval that was absent in non-movement interval. A positive power
law trading relationship was found between Head Accl AP gain and move length standing blindfolded subjects. This could explain the observed negative power law relationship between translation length and peak acceleration threshold in previous psychophysical detection threshold studies.
A study of variations in an athlete’s reaction time performance based on the ...Sports Journal
The purpose of this study was to investigate the difference in the reaction time responses of an athlete
based on various types of stimuli. Reaction time is duration between applications of a stimulus to onset of
response. The present study was measured reaction time in 197 athletes, for the comparison in groups
which were into 3 categories 1. Gender wise (Female and Male), 2. Game wise (Individual and Team), 3.
Standard wise (5th, 6th, 7th, 8th, 9th, 10th) and correlation was done between the group based on the 3 tests.
The VRT, SRT and ViRT was measured by the Jerry (Version: 0.6.4) software. During the reaction time
testing visual, sound and tactile stimuli were given for five times and average reaction time after omitting
highest and lowest reaction time, was taken as the final reaction time. Results suggest that a comparison
was done between the performance of male and female athletes and no significant difference was seen in
their performance in all the three test. Similarly a comparison was also done based on athletes playing a
team and individual game and a significant difference was seen in all the three test (VRT: F = 11.538, p =
0.001); (SRT: F = 8.546, p = 0.004); (ViRT: F = 27.240, p = 0.001). Further a comparison was also done
based on the standard in which the athletes study and it was seen that there is significant difference in
two of test (VRT: F = 4.287, p = 0.001); (ViRT: F = 5.434, p = 0.001). Co-relational analysis was also
done based on gender, and a significant negative correlation was found in females VRT and SRT (r = -
.285, p = .001) and the males showed a significantly positive correlation in VRT and ViRT (r = .243, p =
.001) and a significant negative correlation in SRT and ViRT (r = -.353, p = .001). Further, the
correlation done based on individual and team game. A significant negative correlation was found in
individual game athletes VRT and SRT (r = -.532, p = .001) and a positive correlation between SRT and
ViRT (r = .104, p = .001). The team game athletes showed a significant negative correlation in SRT and
ViRT (r = -.462, p = .001). The correlation was done based on standards athletes. It was seen that in 5th
standard a significant negative correlation was found between SRT and ViRT (r = -.764, p = .001), in 6th
standard a significant negative correlation was found in VRT and SRT (r = -.554, p = .001), in 7th
standard a significant negative correlation was found between VRS and SRS (r = -.396, p = .001), and
SRT and ViRT (r = -.381, p = .001). There was no correlation found in 8th standard. In 9th standard a
significant negative correlation was found in SRT and ViRT (r = -.446, p = .001). In 10th standard a
significant negative correlation was found in VRT and SRT (r = -.554, p = .001).
CURRENT STATUS AND FUTURE RESEARCH DIRECTIONS IN MONITORING VIGILANCE OF INDI...ijsc
Working in monotonous environment often causes lack of concentration or fatigue in an operator and many
times such non-vigilance leads to accidents. Therefore early detection of fatigued state has become
essential in monotonous working environments like driving vehicle, operating machines etc. Such fatigued
state often gets developed gradually and can be identified by certain symptoms. Different types of symptoms
help in modelling non-vigilance in different ways. This paper reviews and compares current status of
research in modelling fatigue where fatigue is modelled using probabilistic models, machine learning
models, finite state machine etc. The paper also presents possible future research directions in the same
field like identifying non-fatigue non-vigilance mental states, extending non-vigilance monitoring for mass
audience etc.
Extraction of respiratory rate from ppg signals using pca and emdeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Extraction of respiratory rate from ppg signals using pca and emdeSAT Journals
Abstract Photoplethysmography is a non-invasive electro-optic method developed by Hertzman, which provides information on the blood volume flowing at a particular test site on the body close to the skin. PPG waveform contains two components; one, attributable to the pulsatile component in the vessels, i.e. the arterial pulse, which is caused by the heartbeat, and gives a rapidly alternating signal (AC component). The second one is due to the blood volume and its change in the skin which gives a steady signal that changes very slowly (DC component). PPG signal consists of not only the heart-beat information but also a respiratory signal. Estimation of respiration rates from Photoplethysmographic (PPG) signals would be an alternative approach for obtaining respiration related information.. There have been several efforts on PPG Derived Respiration (PDR), these methods are based on different signal processing techniques like filtering, wavelets and other statistical methods, which work by extraction of respiratory trend embedded into various physiological signals. PCA identifies patterns in data, and expresses the data in such a way as to highlight their similarities and differences. Since patterns in data can be hard to find in data of high dimension, where the luxury of graphical representation is not available, PCA is a powerful tool for analyzing such data. Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition is ideally suited to extract essential components which are characteristic of the underlying biological or physiological processes. The basis functions, called Intrinsic Mode Functions (IMFs) represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. The contribution reviews the technique of EMD and related algorithms and discusses illustrative applications. Test results on PPG signals of the well known MIMIC database from Physiobank archive reveal that the proposed EMD method has efficiently extracted respiratory information from PPG signals. The evaluated similarity parameters in both time and frequency domains for original and estimated respiratory rates have shown the superiority of the method. Index Terms: Respiratory signal, PPG signal, Principal Component Analysis, EMD, ECG
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.
Comparative analysis of machine learning algorithms on myoelectric signal fro...IAESIJAI
Control strategies of smart hand prosthesis-based myoelectric signals in recent years don't provide the patients with the sensation of biological control of prostheses hand fingers. Therefore, in current work hyperparameters optimization in machine learning algorithm and hand gesture recognition techniques were applied to the myoelectric signal-based on residual muscles contraction of the amputees corresponding to intact forearm limb movement to improve their biological control. In this paper, myoelectric signals are extracted using the MYO armband to recognize ten gestures from ten volunteers (healthy and transradial amputation) on the forearm, thereafter the noise of myoelectric signals using a notch filter (NF) is removed. The proposed classification system involved two machine learning algorithms: (1) the decision tree (DT), tri-layered neural network (TLNN), k-nearest-neighbor (KNN), support vector machine (SVM) and ensemble boosted tree (EBT) classifiers. (2) the optimized machine learning classifiers, i.e., OKNN, OSVM, OEBT with optical diffraction tomography (ODT) and ommatidia detecting algorithm (ODA). The experimental results of classifiers comparison pointed out an algorithm that outperformed with high accuracy is OEBT closely followed by OKNN achieves an accuracy of 97.8% and 97.1% for intact forearm limb, while for transradial amputation with an accuracy of 91.9% and 91.4%, respectively.
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.
The Effect of Kronecker Tensor Product Values on ECG Rates: A Study on Savitz...IIJSRJournal
This article presents a study on ECG signal filtering algorithms to denoise signals corrupted by various types of noise sources. The study also examines the effect of Kronecker tensor product values on ECG rates. The study is conducted in a Matlab environment, and the results demonstrate that a constant number for the respective codes can effectively denoise ECG signals without any trouble. These findings have significant implications for diagnosing abnormal heart rhythms and investigating chest pains. The present study is novel in that it explores the relationship between ECG rate and Kronecker delta values across different age groups, which has not been extensively studied in previous literature. The study's unique contribution is the determination of age-specific values of the constant K required to represent this relationship accurately in different populations, which could inform the development of more effective algorithms for denoising ECG signals in clinical settings. Additionally, this study's finding of an inverse relationship between ECG rate and Kronecker delta values could have broader implications for understanding the physiological factors that contribute to variability in ECG measurements. The study provides valuable insights into ECG signal processing and suggests that the implemented techniques can improve the accuracy of ECG signal analysis in real-time clinical settings. Overall, the manuscript is a valuable contribution to the field of biomedical signal processing and provides important information for researchers and healthcare professionals.
Accuracy Assessment for Multi-Channel ECG Waveforms Using Soft Computing Meth...IJERA Editor
ECG waveform rhythmic analysis is very important. In recent trends, analysis processes of ECG waveform applications are available in smart devices. Still, existing methods are not able to accomplish the complete accuracy assessment while classify the multi-channel ECG waveforms. In this paper, proposed analysis of accuracy assessment of the classification of multi-channel ECG waveforms using most popular Soft Computing algorithms. In this research, main focus is on the better rule generation to analyze the multi-channel ECG waveforms. Analysis is mainly done inSoft Computing methods like the Decision Trees with different pruning analysis, Logistic Model Trees with different regression process and Support Vector Machine with Particle Swarm Optimization (SVM-PSO). All these analysis methods are trained and tested with MIT-BIH 12 channel ECG waveforms. Before trained these methods, MSO-FIR filter should be used as data preprocessing for removal of noise from original multi-channel ECG waveforms. MSO technique is used for automatically finding out the cutoff frequency of multichannel ECG waveforms which is used in low-pass filtering process. The classification performance is discussed using mean squared error, member function, classification accuracy, complexity of design, and area under curve on MIT-BIH data. Additionally, this research work is extended for the samples of multi-channel ECG waveforms from the Scope diagnostic center, Hyderabad. Our study assets the best process using the Soft Computing methods for analysis of multi-channel ECG waveforms
A Novel Approach to Study the Effects of Anesthesia on Respiratory Signals by...IJECEIAES
General anesthesia plays a crucial role in many surgical procedures, and it therefore has an enormous impact on human health. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored.
General anesthesia plays a crucial role in many surgical procedures. It is a drug-induced, reversible state characterized by unconsciousness, anti-nociception or analgesia, immobility and amnesia. On rare occasions, however, the patient can remain unconscious longer than intended, or may regain awareness during surgery. There are no precise measures for maintaining the correct dose of anesthetic, and there is currently no fully reliable instrument to monitor depth of anesthesia. Although a number of devices for monitoring brain function or sympathetic output are commercially available, the anesthetist also relies on clinical assessment and experience to judge anesthetic depth. The undesirable consequences of overdose or unintended awareness might in principle be ameliorated by improved control if we could understand better the changes in function that occur during general anesthesia. Coupling functions prescribe the physical rule specifying how the inter-oscillator interactions occur. They determine the possibility of qualitative transitions between the oscillations, e.g. routes into and out of phase synchronization. Their decomposition can describe the functional contribution from each separate subsystem within a single coupling relationship. In this way, coupling functions offer a unique means of describing mechanisms in a unified and mathematically precise way. It is a fast growing field of research, with much recent progress on the theory and especially towards being able to extract and reconstruct the coupling functions between interacting oscillations from data, leading to useful applications in cardio respiratory interactions. In this paper, a novel approach has been proposed for detecting the changes in synchronism of brain signals, taken from EEG machine. During the effect of anesthesia, there are certain changes in the EEG signals. Those signals show changes in their synchronism. This phenomenon of synchronism can be utilized to study the effect of anesthesia on respiratory parameters like respiration rate etc, and hence the quantity of anesthesia can be regulated, and if any problem occurs in breathing during the effect of anesthesia on patient, that can also be monitored.
NIRS-BASED CORTICAL ACTIVATION ANALYSIS BY TEMPORAL CROSS CORRELATIONsipij
In this study we present a method of signal processing to determine dominant channels in near infrared spectroscopy (NIRS). To compare measuring channels and identify delays between them, cross correlation is computed. Furthermore, to find out possible dominant channels, a visual inspection was performed. The
outcomes demonstrated that the visual inspection exhibited evoked-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with comparable studies and the cross correlation study discovered dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels and adjacent channels. For that reason, our results present a new
method to identify dominant regions in the cerebral cortex using near-infrared spectroscopy. These findings have also implications in the decrease of channels by eliminating irrelevant channels for the experiment.
INFLUENCE OF GENDER ON MUSCLE ACTIVITY PATTERNS DURING NORMAL AND FAST WALKING ijbesjournal
Electromyography (EMG) signals are often described as electrical manifestation of neuromuscular
activation associated with the muscles. These signals are commonly utilized as principal input signals to
control several prosthetic devices such as prosthetic hands, arm, lower limbs, and exoskeleton robots as
well as in designing of rehabilitation and assistive devices. It is well proven that EMG signals vary among
subjects and gender is one of the major factors that play a significant role in this variation. This study
detects the possible gender differences by measuring changes in the EMG activity during different phases
of human walking by acquiring the surface EMG signals from Gluteus Maximus, Hamstrings (biceps
femoris), Quadriceps (rectus femoris) and Soleus muscles of the leg with the healthy subjects walking
barefoot at two paces-normal and fast. The statistical analysis of the results showed no gender differences
at normal speed of walking but when speed of walking changed; it showed clear differences in the
behavior of these muscles. The results from this study would aid in designing closed loop control strategy
for designing a smart functional electrical stimulator (FES) which is the larger goal of this research.
This research task develops a mobile healthcare analysis system (PHAS) which combines both easy ECG signal measurement and reliable analysis of heart rate variability for home care purpose. The PHAS is composed by a health care platform (HCP) and a data system analysis (DSA) module. The HCP consists of a self-developed two pole electrocardiography (ECG) measuring device and the DSA a data processing unit for detection and analysis of heart rate variability. For the DSA module, the adaptive R Peak detection algorithm is proposed to reliably detect the R peak of ECG for HRV analysis. A number of features are extracted from ECG signals. A data mining method is employed for HRV analysis to exploit the correlation between HRV and these features. Experiments are conducted by establishing a database of ECG signals measured from 29 subjects under rest and exercise condition. The results show the PHAS’s significant potential in mobile applications of personal daily health care.
Similar to The Fusion of HRV and EMG Signals for Automatic Gender Recognition during Stepping Exercise (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
In this paper, snake optimization algorithm (SOA) is used to find the optimal gains of an enhanced controller for controlling congestion problem in computer networks. M-file and Simulink platform is adopted to evaluate the response of the active queue management (AQM) system, a comparison with two classical controllers is done, all tuned gains of controllers are obtained using SOA method and the fitness function chose to monitor the system performance is the integral time absolute error (ITAE). Transient analysis and robust analysis is used to show the proposed controller performance, two robustness tests are applied to the AQM system, one is done by varying the size of queue value in different period and the other test is done by changing the number of transmission control protocol (TCP) sessions with a value of ± 20% from its original value. The simulation results reflect a stable and robust behavior and best performance is appeared clearly to achieve the desired queue size without any noise or any transmission problems.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
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This paper considers the fusion of EMG and HRV signals from the same stepping
sequence to carry out gender recognition and the feature fusion is used in order to compare the
performance of the combination of EMG and HRV at this level. To the best of our knowledge
this is the first method to combine EMG with the HRV for automatic gender recognition during
short-term stepping exercise using a stepper. After that, the ongoing work can be used to
support the interface system of the controllable current-induced stair stepper in rehab
application. This paper is a further research on [2] and part of an attempt to use data take out
from the distinct physiological signals in order to design a robust and reliable automatic system.
So, gender could be assessed and less monitoring lower limb could be developed in
rehabilitation system [16], which may assist to isolate male and female subjects to undergo
rehabilitation process. It seems that the gender factor motivates people differently, in performing
consistent exercise for rehab.
2. Research Method
The proposed young gender recognitions are composed of processing steps as
described in the following sections.
2.1. Data Acquisition
. The research was accomplished in the Faculty of Biomedical and Health Science
Engineering, Universiti Teknologi Malaysia, Johor Bahru. This study was done on 10 healthy,
untrained young volunteers (mean 23.9 years, range 25 to 30), 5 were male and 5 were female
participants. All participants were free from any disease and clarified about the procedures and
their informed consent was obtained.
The TMSi DAQ was applied to record the EMG and ECG signals synchronously for
gender recognition during exercise using stair stepper. The TMSi DAQ was utilized with three
pairs of active surface electrodes and a single reference surface electrode to determine the
electrical signals from the muscle and cardio. These surface electrodes are circular in shape
(diameter=11.4 mm) and are composed of silver/silver chloride (Ag/AgCl) material. The stair
stepper was applied to perform a stepping activity in order to produce the electrical activity of
the muscle and heart as shown in Figure. 1. Metronome (45 beat per minute) was utilized to set
the stepping rate. A metronome is a instrument used by musicians that indicates time at a
chosen rate by giving a fixed tick.
Figure 1. Experimental setup during stepping activity for EMG
and ECG data acquisition [2]
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2.2. Preprocessing
ECG: The ECG pre-processing and HRV quantification is implemented by using the
Kubios HRV software [17]. Kubios HRV is progressive and convenient to utilize the software for
HRV interpretation. Moreover, the software supports a few information for ECG and RR interval
(RRi) data. It contains the algorithm to detect an adaptive QRS peaks and mechanisms to
correct the artifacts, remove the trend and analysed the sample.
The R-wave is automatically recognized by applying an assembled QRS detection
algorithm in ECG data processing using Kubios. Basically, the Pan-Tompkins algorithm is
utilized for the QRS detection algorithm in the Kubios software [18]. The preprocessing part
contains bandpass filtering of the ECG, squaring of sample data and moving average filtering.
The function of the bandpass filtering is to reduce the powerline interference, baseline wander
and any other noises. Highlight peaks and smooth, close by peaks is the function of the
squaring and moving average filtering respectively.
There are about three minutes of ECG signal recording is required for HRV signal
analysis under normal physical and mental circumstances. Essentially, the HRV signal obtained
from the Kubios was used in this research.
EMG: The EMG was filtered using a lowpass filter and highpass filter with a cutoff
frequency of 20Hz and 300Hz respectively. The synchronization of EMG and HRV as shown in
Figure 2, are required to achieve the combination between two modalities as explained later.
2.3. Feature Extraction
This section explains in brief the analysis parameters comprised in the Kubios software
for HRV and analysis parameters for EMG.
2.3.1. HRV Features
The measurements and the indications operated are basically in accordance with the
guidelines given in [19]. HRV features were extracted from time domain, frequency domain and
non-linear generated from the Kubios Software as shown in the Figure 3.
Time domain features: The mean of RR interval (ms), standard deviation of RR
(SDNN (ms)) , mean of heart rate (HR (1/min)), SD of HR (1/min), RMSSD (ms), NN50 (count),
pNN50 (%), HRV Triangular Index and TINN (ms).
Frequency Domain - Fast Fourier Transform (FFT) spectrum: The very low
frequency (VLF (Hz)) peak, low frequency (LF (Hz)) peak, high frequency (HF (Hz)) peak, VLF
power (ms
2
), VLF power (%), LF power (ms
2
), LF power (%), LF power (n.u.), HF power (ms
2
),
HF power (%), HF power (n.u.), ratio of LF and HF (ms
2
) and total power (ms
2
).
Frequency Domain - Autoregressive (AR) spectrum: The VLF peak (Hz), LF peak
(Hz), HF peak (Hz), VLF power (ms
2
), VLF power (%), LF power (ms
2
), LF power (%), LF power
(n.u.), HF power (ms
2
), HF power (%), HF power (n.u.), ratio of LF and HF (ms
2
) and total power
(ms
2
).
Nonlinear: The poincare plot (SD1 (ms) & SD2 (ms)) where SD1 and SD2 is the
standard descriptors, approximate entropy (Apen), sample entropy (Sampen), correlation
dimension, Detrended Fluctuation Analysis (DFA (alpha 1 & alpha 2)), recurrence plot (Lmean
Figure 2. The synchronization of EMG and HRV
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(beats), Lmax (beats) where Lmax is the length of the longest line of recurrence point in a
continuous row within the recurrence plot and Lmean is a mean line length , recurrence rate
(REC (%)), determinism (DET (%)) and shannon entropy (Shanen).
2.3.2. EMG Features
EMG features were extracted from time domain and frequency domain as follows: Time
domain features: The mean, standard deviation (SD), root mean square (RMS), variance,
skewness and kurtosis. Frequency domain features: The mean frequency and mid frequency.
2.3.3. Feature Selection
The accuracy of the classification process may adversely affect if some of redundant
irrelevant features of the features extracted are not appropriately eliminating [20, 21]. Hence,
the feature selection method is applied, which is the wrapper method developed in [20, 21]
using Weka in order to pick out an ideal feature subset of EMG and HRV with least redundancy
and greatest class discriminability. If the dimensionality of the feature set is decreased while the
precision of the classification is either enhanced or stay natural, the feature selection is viewed
as successful.
2.3.4. Feature Fusion of EMG and HRV during Short-Term Stepping Activity by Stepper
The proposed structure for feature fusion of EMG and HRV is depicted in Figure 4. The
EMG and HRV windows were preprocessed before implementing feature extraction processes.
From the huge set of features extracted, a feature subset was picked using the wrapper-based
feature selection method [20]. Then, The resulting feature vector 𝐹 𝐸𝑀𝐺̅̅̅̅̅̅̅ and 𝐹 𝐻𝑅𝑉̅̅̅̅̅̅ were chained
to build a single composite feature vector, 𝐹𝐶, presented by
FC = [FEMG̅̅̅̅̅̅̅|FHRV̅̅̅̅̅̅̅] (1)
After that, the composite feature vector was applied to several statistical classifiers for
the feature fusion process. The explored classifiers were: Decision Tree (J48), NB (Na𝑖̈ve
Bayes) and k-NN (k-Nearest Neighbors) with k=1, 3 and 5. The results are demonstrated in
section 3.
Figure 3. Kubios software for HRV
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3. Performance Analysis and Discussion
The performance of the EMG-HRV feature fusion process, as specified by the
classification outcomes, is shown in Figure 5. The figure demonstrates that the 1-NN and 5-NN
yields the optimal overall performance for gender recognition during short term stepping
exercise by stepper machine. Both 1-NN and 5-NN classifiers achieved 80% sensitivity and
almost 100% specificity.
Figure 5. Performance of different classifier for gender assessment during short-term stepping
exercise based on EMG and HRV features fusion
To evaluate the additional estimation of the proposed classification approach for gender
recognition, their performances are compared to those based on EMG or HRV. Table 1
demonstrates the performances of 1-NN classifiers in terms of sensitivity (SEN) and specificity
(SPE). The feature-based combination classifier achieved 80% SEN and almost 100% SPE
compared to 60% SEN and 60% SPE using the EMG features alone and 80% SEN and 60%
SPE using the HRV features only. Table 2 shows the performances of 5-NN classifier in terms
of SEN and SPE too. The feature-based combination classifier reached 80% SEN and almost
100% SPE compared to 40% SEN and 80% SPE using the EMG features and 80% SEN and
80% SPE using the HRV features only.
This indicates that the fused features for 1-NN have significantly enhanced the SPE
(+40%) of gender recognition during short-term stepping exercise by stair stepper machine
compared to SPE achieved using either EMG or HRV features. The improvement of SEN
through feature fusion was remarkable (+20% using EMG alone and +0% using HRV alone).
Meanwhile, the combined features for 5-NN have significantly improved the SPE (+20%) of
gender recognition during short-term stepping exercise by stepper machine compared to SPE
Figure 4. Automatic gender recognition process based on EMG-HRV feature fusion
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achieved using either EMG or HRV features. Also, the improvement of SEN through feature
fusion was remarkable (+40% using EMG alone and +0% using HRV alone). Thus, from the
discussion above, the 1-NN classifier is better performance compared to 5-NN classifier.
Furthermore, this paper also improves the gender recognition results compared to latest
previous research. Das D obtained 76.79% gender recognition rate based on human gait using
Support Vector Machine (SVM) [22], Archana GS [23] obtained 80% gender classification of
speech performance using SVM and Hossain S [24] found 85% of male and 74% accurate
decision based on the fingerprint based gender identification. This paper achieved 80% SEN
and almost 100% SPE which is 90% gender correct rate using 1-NN classifier based on the
fusion of HRV and EMG physiological signals.
Table 1. Performance Comparison Of Gender Recognition During Short-Term Stepping
Exercise Using A Stepper Machine From Single Signal Classification (EMG/HRV) and The
Proposed Fusion System for 1-NN Classifier
EMG HRV Feature Fusion
SEN (%) SPE (%) SEN (%) SPE (%) SEN (%) SPE (%)
60 60 80 60 80 ~ 100
Table 2. Performance Comparison Of Gender Recognition During Short-Term Stepping
Exercise Using A Stepper Machine From Single Signal Classification (EMG/HRV) and the
Proposed Fusion System for 5-NN Classifier
EMG HRV Feature Fusion
SEN (%) SPE (%) SEN (%) SPE (%) SEN (%) SPE (%)
40 80 80 80 80 ~ 100
4. Conclusion
An approach for the fusion of EMG and HRV signals acquired during short-term
stepping activity by stepper machine were proposed in this study. The fusion of the features
extracted from EMG and HRV signals has prompted a better performing automatic gender
recognition compared to solely on EMG and HRV signals. The outcomes affirmed that
information from EMG and HRV complement each other and their combination provides better
gender recognition performance compared to solely on EMG and HRV. Gender factor
encourages people contrastingly in committing consistent exercise in rehab application.
Therefore, this effort may support to isolate male and female subjects to experience
rehabilitation process.
Acknowledgements
The work presented in this study is funded by Ministry of Education Malaysia and
Universiti Teknologi Malaysia under research grant VOTE NO: 15H81.
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![TELKOMNIKA, Vol.15, No.2, June 2017, pp. 756~762
ISSN: 1693-6930, accredited A by DIKTI, Decree No: 58/DIKTI/Kep/2013
DOI: 10.12928/TELKOMNIKA.v15i2.6113 756
Received January 25, 2017; Revised April 18, 2017; Accepted May 1, 2017
The Fusion of HRV and EMG Signals for Automatic
Gender Recognition during Stepping Exercise
Nor Aziyatul Izni Mohd Rosli
1
, Mohd Azizi Abdul Rahman*
2
, Malarvili Balakrishnan
3
,
Saiful Amri Mazlan
4
, Hairi Zamzuri
5
1,2,4,5
Malaysia-Japan International Institute of Technology. Universiti Teknologi Malaysia,
Jalan Semarak, 54100, Kuala Lumpur, Malaysia
3
Department of Biotechnology and Medical Engineering, Faculty of Biosciences and Medical Engineering,
Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia
*Corresponding author, e-mail: azizi.kl@utm.my
Abstract
In this paper, a new gender recognition approach in accordance with the fusion of features
extracted from electromyogram (EMG) and heart rate variability (HRV) during stepping activity using a stair
stepper device is proposed. The fusion of EMG and HRV is investigated based on feature fusion approach.
The feature fusion is carried out by chaining the feature vector extracted from the EMG and HRV signals.
A proposed approach comprises of a sequence of processing steps which are preprocessing, feature
extraction, feature selection and the feature fusion. The results demonstrated that the fusion approach had
enhanced the performance of gender recognition compared to solely on EMG or HRV for the gender
recognition.
Keywords: gender recognition, feature fusion, heart rate variability (HRV), electromyography (EMG),
Sstepper
Copyright © 2017 Universitas Ahmad Dahlan. All rights reserved.
1. Introduction
Automatic gender recognition plays an essential role in recognition of an individual.
Gender recognition can help adequately to reduce the search time by constraining the further
seeking stage to either a male or female database. Any appropriate biosignal trait can be
utilized for further classification once a person is recognized as male or female. Automatic
recognition of gender can also give an imperative sign in various security, surveillance and
rehabilitation based applications.
Gender is known to have an effect on HRV [1, 2] and EMG [3, 4]. A few studies have
examined gender differences in autonomic control during exercise [5], [6]. Ahamed et al.,
examined gender-related changes in the biceps brachii muscle by EMG signal analysis [4].
They found that the male subjects produced a higher and steadier signal than the female
subjects. More recently, our previous work had explored the HRV response during short-term
exercise by stair stepper machine [2]. The paper also makes a comparison of the finding
significant HRV features between young healthy male and female and showed that there exist a
significant gender difference in HRV features during short-term stepping exercise.
Furthermore, there are various techniques for tackling the issue of gender recognition,
for example, based on fusion of face and gait information [7], fusion of facial strips [8], color
information [9], sift features [10], fusion of different spatial scale features elected by mutual
information from the hyhistogram of LBP (local binary patterns), intensity, and shape [11]. Plus,
there are also techniques based on gabor filters combined with binary features [12], using a
hybrid of gabor filters and local binary patterns to draw out face features and use self-organized
map (SOM) for classification [13]. Next, there are methods based on extraction of the hip joint
data that was computed from the Biovision Hierarchical data [14] and from gait sequences with
arbitrary walking directions [15]. Moreover, it is true that physiological signals are also able to be
implemented in gender classification. Thus, the physiological signals which are EMG and HRV
signals collected during stepping activity are utilized in this research. But, there is not strong
enough classification results to identify gender if using only one physiological signal. Hence, the
information from both EMG and HRV is combined together to get better classification result.](https://image.slidesharecdn.com/276113-200909092533/85/The-Fusion-of-HRV-and-EMG-Signals-for-Automatic-Gender-Recognition-during-Stepping-Exercise-1-320.jpg)
![TELKOMNIKA ISSN: 1693-6930
The Fusion of HRV and EMG Signals for Automatic Gender … (Nor Aziyatul Izni Mohd Rosli)
757
This paper considers the fusion of EMG and HRV signals from the same stepping
sequence to carry out gender recognition and the feature fusion is used in order to compare the
performance of the combination of EMG and HRV at this level. To the best of our knowledge
this is the first method to combine EMG with the HRV for automatic gender recognition during
short-term stepping exercise using a stepper. After that, the ongoing work can be used to
support the interface system of the controllable current-induced stair stepper in rehab
application. This paper is a further research on [2] and part of an attempt to use data take out
from the distinct physiological signals in order to design a robust and reliable automatic system.
So, gender could be assessed and less monitoring lower limb could be developed in
rehabilitation system [16], which may assist to isolate male and female subjects to undergo
rehabilitation process. It seems that the gender factor motivates people differently, in performing
consistent exercise for rehab.
2. Research Method
The proposed young gender recognitions are composed of processing steps as
described in the following sections.
2.1. Data Acquisition
. The research was accomplished in the Faculty of Biomedical and Health Science
Engineering, Universiti Teknologi Malaysia, Johor Bahru. This study was done on 10 healthy,
untrained young volunteers (mean 23.9 years, range 25 to 30), 5 were male and 5 were female
participants. All participants were free from any disease and clarified about the procedures and
their informed consent was obtained.
The TMSi DAQ was applied to record the EMG and ECG signals synchronously for
gender recognition during exercise using stair stepper. The TMSi DAQ was utilized with three
pairs of active surface electrodes and a single reference surface electrode to determine the
electrical signals from the muscle and cardio. These surface electrodes are circular in shape
(diameter=11.4 mm) and are composed of silver/silver chloride (Ag/AgCl) material. The stair
stepper was applied to perform a stepping activity in order to produce the electrical activity of
the muscle and heart as shown in Figure. 1. Metronome (45 beat per minute) was utilized to set
the stepping rate. A metronome is a instrument used by musicians that indicates time at a
chosen rate by giving a fixed tick.
Figure 1. Experimental setup during stepping activity for EMG
and ECG data acquisition [2]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)