Browsing by Author "Chemmangat, K."

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A Comparison of Different Signal Processing Techniques for Upper Limb Muscle Activity Onset Detection using Surface Electromyography
(Institute of Electrical and Electronics Engineers Inc., 2023) Koppolu, P.K.; Chemmangat, K.
This work presents the use of real-time experimental Surface Electromyography (sEMG) signals to determine muscle activity of upper limb by detecting the exact onset and offset timings. Various muscle activity detection methods were evaluated, such as Sample Entropy (SEn), Permutation Entropy (PEn), Amplitude Aware Permutation Entropy (AAPEn), and Integrated Profile (IP). The performance of these methods was compared, and it was found that IP detects muscle activity quickly and requires less computation for real-time implementation as compared to other methods. Â© 2023 IEEE.
A Computationally Efficient sEMG based Silent Speech Interface using Channel Reduction and Decision Tree based Classification
(Elsevier B.V., 2020) Abdullah, A.; Chemmangat, K.
Silent Speech Interface is one of the promising areas of Human-Computer Interaction research. The surface electromyography based silent speech interface is a technique where the electric activity of facial muscles are used to detect speech. The existing sEMG based SSI techniques use complex machine learning algorithms and too many number of electrodes on the subject's face. It creates inconvenience to the user who might have undergone laryngectomy. More number of electrodes becomes highly invasive to the user, while complex classification algorithms increase the computational cost and prevents real time implementation of sEMG based SSI. Thus the objective of this research work was to develop a less complex and computationally less expensive model to classify words. To achieve this goal channel reduction technique and the use of Decision Tree based classification algorithm was employed. Only the time domain features are used as input to the classification algorithm. The motive was to exploit the advantage of computational ease in extracting the time domain features as compared to the frequency domain features. The sEMG data of the words used in this work are obtained from the complete utterance of the sentences and not by individual utterances of the word. Our algorithm was able to achieve a word accuracy of 95.17% even after applying a channel reduction, thereby allowing us to use only the data of 5 channels, in place of a conventional seven channel setup. Â© 2020 The Authors. Published by Elsevier B.V.
A novel pre-processing procedure for enhanced feature extraction and characterization of electromyogram signals
(Elsevier Ltd, 2018) Powar, O.S.; Chemmangat, K.; Figarado, S.
In the analysis of electromyogram signals, the challenge lies in the suppression of noise associated with the measurement and signal conditioning. The main aim of this paper is to present a novel pre-processing step, namely Minimum Entropy Deconvolution Adjusted (MEDA), to enhance the signal for feature extraction resulting in better characterization of different upper limb motions. MEDA method is based on finding the set of filter coefficients that recover the output signal with maximum value of kurtosis while minimizing the low kurtosis noise components. The proposed method has been validated on surface electromyogram dataset collected from seven subjects performing eight classes of hand movements (wrist flexion, wrist radial deviation, hand close, tripod, wrist extension, wrist ulnar deviation, cylindrical and key grip) with only two pairs of electrodes recorded from flexor carpi radialis and extensor carpi radialis on the forearm. The performance of the MEDA has been compared across four classifiers namely J-48, k-nearest neighbours (KNN), Naives Bayes and Linear Discriminant Analysis (LDA) attaining the classification accuracy of 85.3 ± 4%, 85.67 ± 5%, 76 ± 6% and 91.2 ± 2% respectively. Practical results demonstrate the feasibility of the approach with mean percentage increase in classification accuracy of 20.5%, without significant increase in computational time across seven subjects demonstrating the significance of MEDA in classification. © 2018 Elsevier Ltd
A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis
(Institute of Physics, 2024) Kumar Koppolu, P.; Chemmangat, K.
Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time. © 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.
A Search for Suitable Mother Wavelet in Discrete Wavelet Transform Based Analysis of Acoustic Emission Partial Discharge Signals
(University of Kragujevac Faculty of Technical Sciences in Cacak, 2024) Vippala, S.R.; Punekar, G.S.; Chemmangat, K.; Tangella, B.
Signal processing helps monitor the condition of power equipment. Partial discharge (PD) signals used in condition-based maintenance give crucial information in the diagnosis of degradation of insulation. The acoustic emission technique (AET) is one of the most widely used techniques in PD signal analysis due to its inherent advantages. Analyzing acoustic emission partial discharge (AEPD) signals in the wavelet-domain provides critical insights into the location and type of the sources of PD. Selection of the most suitable mother wavelet in applying discrete wavelet transform (DWT) on AEPD signals is important as it will directly impact the outcome. For this selection, 36 wavelets belonging to the Daubechies, Symlets, Coiflets, and Bi-orthogonal families are investigated. For this purpose, five experimentally collected AEPD test signals are used. The selection is based on the “accuracy of wavelet decomposition results” in this work, probably for the first time. One mother wavelet from each family is individually shortlisted for all three performances, namely (a) reconstruction, (b) denoising, and (c) compression, by computing and comparing their commonly used metrics. Further, based on percentage energy criteria, the most suitable mother wavelets are identified as coif3, coif4, and coif5, respectively, for the three performances. © 2024, University of Kragujevac Faculty of Technical Sciences in Cacak. All rights reserved.
A two-stage classification strategy to reduce the effect of wrist orientation in surface myoelectric pattern recognition
(Institute of Electrical and Electronics Engineers Inc., 2022) Koppolu, P.K.; Chemmangat, K.
The myoelectric Pattern Recognition (PR) collects surface Electromyographic (sEMG) signals using the electrodes placed on the upper limb of the amputee. Then it recognizes patterns in those signals based on the intended limb movement using signal processing and machine learning techniques. The performance of the PR system should be robust against multiple factors, like wrist orientation, muscle force level changes, limb position changes, and electrode shifts. This paper demonstrates how performance is affected by wrist orientation and proposes a method to overcome those effects. A two-stage classification technique with Dynamic Time Warping (DTW) as the classifier, along with features extracted from a three-axis accelerometer and six-channel sEMG sensors, is proposed here. Accelerometer features are used to identify the wrist orientation, and sEMG features are used to classify the various limb movements performed by ten subjects. The performance of the proposed method was measured by classification error and classification accuracy of limb movements. The corresponding results were compared with the state-of-the-art techniques. Â© 2022 IEEE.
Application of Fractal Analysis based Feature Extractor for Channel Reduction of Silent Speech Interface Using Facial Electromyography
(Intelligent Network and Systems Society, 2023) Abdullah, A.; Powar, O.S.; Chemmangat, K.
Surface electromyography (sEMG) based silent speech interface (SSI) is an actively investigated topic among the broad area of human computer interaction studies which is currently dominated by acoustic sound based speech recognition research. This research is an attempt to help people who have an impaired vocal system if they are having no issues with their facial muscle functions. The basic idea is to reduce the total number of sEMG electrodes that has to be affixed on the face thereby reducing the invasiveness of the silent speech recognition module. This is achieved by incorporating a new detrended fluctuation analysis (DFA) based feature along with the already existing features associated with electromyographic signals. DFA is used for the first time in literature in the area of surface electromyography based silent speech recognition. The main idea is to incorporate the DFA feature along with the state-of-the-art features to improve the performance of a sEMG based SSI model so that an efficient channel reduced model can be realised. Different channel combinations were tried to analyse the impact of each channel in word recognition accuracy and the optimal channel combination was identified. As a result of this research work, a reduced channel setup with 5 electrodes was proposed in place of the conventional 7 channel data acquisition setup. This was achieved while maintaining an accuracy of 83.88 % and 92.92 % using the decision tree (DT) model and K-nearest neighbours (KNN) model respectively © 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.
Automatic selection of IMFs to denoise the sEMG signals using EMD
(Elsevier Ltd, 2023) Koppolu, P.K.; Chemmangat, K.
Surface Electromyography (sEMG) signals are muscle activation signals, which has applications in muscle diagnosis, rehabilitation, prosthetics, and speech etc. However, they are known to be affected by noises such as Power Line Interference (PLI), motion artifacts etc. Currently, Empirical Mode Decomposition (EMD) and its modifications such as Ensemble EMD (EEMD), and Complementary EEMD (CEEMD) are used to decompose EMG into a series of Intrinsic Mode Functions (IMFs). The denoised EMG can be obtained from the selected IMFs. Statistical methods are used to select the signal dominant IMFs to reconstruct the denoised signal. In this work, a novel procedure is proposed to automatically separate noisy IMFs from the original sEMG signal. For this purpose, Permutation Entropy (PE) is employed in EEMD sifting process called Partly EEMD (PEEMD), to separate the noisy IMFs from the original sEMG signal according to the preset PE threshold. PEEMD decomposes the original signal into various modes according to a preset PE threshold and the denoised signal is reconstructed from resultant IMFs. The PEEMD denoising procedure is applied on the experimental sEMG data collected from eight subjects, that include six various upper limb movement classes. The proposed denoising procedure achieved an improved denoising performance in comparison with EMD, EEMD, and CEEMD. An alternate measure called Sample Entropy (SE) is also used in place of PE, for the automated sifting process as a comparison. Signal to Noise Ratio (SNR), Root Mean Square Error (RMSE), and Reconstruction Error (RE) parameters are used to evaluate the denoising performance. The results, averaged across eight subjects, demonstrate that the proposed denoising procedure outperforms the state-of-the-art EMD techniques in terms of these performance measures on the experimentally collected sEMG data samples. © 2023 Elsevier Ltd
Classification of Hand Gestures with Real Time Muscle Activity Detection for Myoelectric Control of Upper Limb Prosthesis
(Institute of Electrical and Electronics Engineers Inc., 2023) Koppolu, P.K.; Chemmangat, K.
This paper presents the classification of basic hand movements with the determination of onset and offset timings of muscle activity in real time using surface Electromyography (sEMG). Integration Profile (IP) method is evaluated to detect muscle activity in real time. Dynamic Time Warping (DTW) is used to classify hand movements using detected muscle activity signals. The sEMG data collection, muscle activity detection and classification of different muscle activities are performed in the National Instrument (NI) LabView environment. The movement classification results suggest that the proposed procedure accurately demonstrates the importance of muscle activity detection in the classification. Â© 2023 IEEE.
Comparison of pre-processing filters on the performance of sEMG based pattern recognition
(Institute of Electrical and Electronics Engineers Inc., 2019) Powar, O.S.; Chemmangat, K.
The noise present in the surface electromyography (sEMG) signals is a significant problem in the control of the rehabilitation scheme. Different noise reducing methods have been discussed and considered individually in previous studies. However, there is limited work on the comparison of different noise reduction strategies. To achieve good performance of Myoelectric Control (MEC) system, the selection of filters becomes essential. The vital contribution of this work is a study into the comparison of three denoising methods including Butterworth filter, Weiner filter and Spectral Subtraction (SS) filter that has been used to remove the noise from sEMG signal. Performance evaluation of the three noise reduction methods is done regarding classification accuracy and computation time. The three denoising methods have been validated on the recorded sEMG of seven healthy subjects while performing eight classes of movements from the two muscle positions on the right forearm. The accuracy is compared with four classifiers namely, J48, k-nearest neighbors (KNN), Naive Bayes and Linear Discriminant Analysis (LDA). Results show that the Butterworth filter provides marginally better performance than the other two filters regarding classification accuracy; when computation time is considered SS filter offers significant savings. A visual inspection of the output of the Weiner filter hints at its utility as a muscle activity onset detection tool. Â© 2019 IEEE.
Design and implementation of passivity-based controller for active suspension system using port-Hamiltonian observer
(SAGE Publications Ltd, 2023) Sistla, P.; Chemmangat, K.; Figarado, S.
The objective of this study is to design and implement an observer for quarter-car active suspension system in Port-Hamiltonian form. A novel state observer is designed for active suspension system modelled in port-Hamiltonian form to estimate the states in presence of road disturbances. The observer is designed considering suspension deflection alone as the output, which is an easily measurable output. Performance of the proposed observer is evaluated experimentally with road disturbance input mimicking a sudden bump and a continuously varying road input, and proven to be effective in minimising the error dynamics in presence of bounded unmodelled disturbances. To prove the effectiveness of the state-estimator, an Interconnection and Damping Assignment Passivity Based Control (IDA-PBC) designed using the desired physical properties of the closed-loop system is implemented using the observer states. Experimental results of the controller implemented using the designed state observer show good improvement in the ride comfort, ride stability and suspension stroke of the active suspension system, which proves the effectiveness of the proposed port-Hamiltonian observer in terms of minimising the error dynamics. © IMechE 2023.
Design and performance comparison of interconnection and damping assignment passivity-based control for vibration suppression in active suspension systems
(SAGE Publications Inc., 2021) Sistla, P.; Figarado, S.; Chemmangat, K.; Manjarekar, N.S.; Kallu Valappil, G.
This study presents the design of interconnection and damping assignment passivity-based control for active suspension systems. It is well known that interconnection and damping assignment passivity-based control’s design methodology is based on the physical properties of the system where the kinetic and potential energy profiles are shaped, and asymptotic stability is achieved by damping injection. Based on the choice of control variables, special cases of the control law are derived, and tuning of the control law with the physical meaning of the variables is demonstrated along with their simulation results. The proposed control law is experimentally validated on a scaled model of a quarter-car active suspension system with different road profiles, varying load conditions, and noise and delay in the sensor measurements and actuator respectively. The results are compared with that of an uncontrolled system with linear quadratic regulator and sliding mode control. © The Author(s) 2020.
Detection of arrhythmia from electrocardiogram signals using a novel gaussian assisted signal smoothing and pattern recognition
(Elsevier Ltd, 2022) Chandrasekar, A.; Shekar, D.D.; Hiremath, A.C.; Chemmangat, K.
The electrocardiogram is a widely used measurement for individual heart conditions, and much effort has been put into automatic arrhythmia diagnosis using machine learning. However, the classification performance is hampered by the use of less representative data in conjunction with traditional machine learning models. This paper proposes a novel algorithm for pre-processing raw Electrocardiogram signals via Gaussian Assisted Signal Smoothing. In this method, the ECG signal is modeled as a low pass component and a weighted sum of Gaussians. The Gaussians are used to model the peak characteristics of the signal, effectively preserving its structure and morphology while eliminating the noise, which is evident by the enhanced peak signal-to-noise ratio of the GASS signal. The R peaks obtained from the Pan Tompkins algorithm are used to extract the heartbeats from the filtered signal using a windowing technique. A cascaded combination of a Convolutional Neural Network and a Quadratic Support Vector Machine is then used to classify the heartbeats. The CNN model has 131,661 parameters, making it much lighter than previously reported works. The MIT-BIH Arrhythmia Database was used for our experiments. Across eleven classes, our results reveal that the model has an accuracy of 97.63% and an average F1 score of 0.9263. In contrast, previous works have primarily focused on a one vs. all or a five-class classification. From a signal processing standpoint, the proposed method offers a promising solution for Signal Filtering and Arrhythmia Classification. © 2021 Elsevier Ltd
Dynamic time warping for reducing the effect of force variation on myoelectric control of hand prostheses
(Elsevier Ltd, 2019) Powar, O.S.; Chemmangat, K.
Research in pattern recognition (PR) for myoelectric control of the upper limb prostheses has been extensive. However, there has been limited attention to the factors that influence the clinical translation of this technology. A relevant factor of influence in clinical performance of EMG PR-based control of prostheses is the variation in muscle activation level, which modifies the EMG patterns even when the amputee attempts the same movement. To decrease the effect of muscle activation level variations on EMG PR, this work proposes to use dynamic time warping (DTW) and is validated on two databases. The first database, which has data from ten intact-limbed subjects, was used to test the baseline performance of DTW, resulting in an average classification accuracy of more than 90%. The second database comprised data from nine upper limb amputees recorded at three levels of force for six hand grips. The results showed that DTW trained at a single force level achieved an average classification accuracy of 60 ± 9%, 70 ± 8%, and 60 ± 7% at the low, medium and high force levels respectively across all amputee subjects. The proposed scheme with DTW achieved a significant 10% improvement in classification accuracy when trained at a low force level when compared to the traditional time-dependent power spectrum descriptors (TD-PSD) method. © 2019 Elsevier Ltd
Feature Selection and Ranking in EMG Analysis for Hand Movement Classification
(Institute of Electrical and Electronics Engineers Inc., 2023) Chandrika, P.R.; Powar, O.S.; Chemmangat, K.
Surface Electromyography has gained tremendous significance in the recent years due to its suitability and reliability in a wide range of applications like automatic prosthetic control, diagnosis of neuromuscular disorders, in robotics and many such fields. Considering such applications, identification of various muscular movements is necessary and hence, EMG pattern recognition is needed. This paper focusses on a generalised EMG pattern recognition of various hand movements. The data from Ninapro Database - 4 has been used for pattern recognition. The database has Surface Electromyogram (sEMG) data of 52 various hand movements. The data was subjected to pre-processing, feature extraction and classification. An average accuracy of 64.87% was obtained for a combination of seven features in the time (temporal) domain, using Linear Discriminant Analysis (LDA) as the classification model. The obtained classification accuracies are compared and discussed with respect to the state-of-the-art literature. Â© 2023 IEEE.
Feature selection for myoelectric pattern recognition using two channel surface electromyography signals
(2017) Powar, O.S.; Chemmangat, K.
Pattern recognition scheme is used for discriminating various classes of hand motion with feature extracted from the surface electromyography signals. However, while using a relatively large feature set for classification process, the computational complexity increases tremendously. To overcome this, the paper implements feature selection technique using wrapper evaluation and four different search methods without significantly affecting the classification accuracy. The performance of the features is tested on surface electromyography data collected from seven subjects, with eight classes of movements. Practical results indicate that using feature selection methods can achieve the same accuracy with lesser number of features. � 2017 IEEE.
Feature selection for myoelectric pattern recognition using two channel surface electromyography signals
(Institute of Electrical and Electronics Engineers Inc., 2017) Powar, O.S.; Chemmangat, K.
Pattern recognition scheme is used for discriminating various classes of hand motion with feature extracted from the surface electromyography signals. However, while using a relatively large feature set for classification process, the computational complexity increases tremendously. To overcome this, the paper implements feature selection technique using wrapper evaluation and four different search methods without significantly affecting the classification accuracy. The performance of the features is tested on surface electromyography data collected from seven subjects, with eight classes of movements. Practical results indicate that using feature selection methods can achieve the same accuracy with lesser number of features. Â© 2017 IEEE.
Harmonic and switching loss analysis of two-level space vector based pulse width modulation schemes
(2017) Krishna, M, R, P.; Arumalla, R.T.; Figarado, S.; Chemmangat, K.
This paper presents the analysis of harmonic and switching loss for two-level space vector based pulse width modulation (PWM) schemes. The harmonic performance of the inverter is evaluated through weighted voltage total harmonic distortion for different modulation indices. To calculate switching loss, analytical expressions for switching instants are generated by comparing the triangular carrier signal with the equivalent modulating signals corresponding to space vector based modulation schemes and switching instants are obtained in terms of Kapteyn series. The losses are compared analytically for three popular space vector based modulation schemes, namely conventional space vector PWM, 60� bus clamping PWM and 30� bus clamping PWM. � 2017 IEEE.
Harmonic and switching loss analysis of two-level space vector based pulse width modulation schemes
(Institute of Electrical and Electronics Engineers Inc., 2017) Krishna M R, P.; Arumalla, R.T.; Figarado, S.; Chemmangat, K.
This paper presents the analysis of harmonic and switching loss for two-level space vector based pulse width modulation (PWM) schemes. The harmonic performance of the inverter is evaluated through weighted voltage total harmonic distortion for different modulation indices. To calculate switching loss, analytical expressions for switching instants are generated by comparing the triangular carrier signal with the equivalent modulating signals corresponding to space vector based modulation schemes and switching instants are obtained in terms of Kapteyn series. The losses are compared analytically for three popular space vector based modulation schemes, namely conventional space vector PWM, 60Â° bus clamping PWM and 30Â° bus clamping PWM. Â© 2017 IEEE.
Improved Robustness of EMG Pattern Recognition for Transradial Amputees with EMG Features Against Force Level Variations
(Institute of Electrical and Electronics Engineers Inc., 2023) Powar, O.S.; Chemmangat, K.
Feature extraction is an essential process for removing the unwanted part and interference of the Electromyography (EMG) signal, and to extract the useful information hidden in it. Inorder to obtain high performance of Myoelectric Control (MEC), the choice of features plays an important role. The studies carried out earlier to overcome force level variation have used features which are redundant, affecting the robustness and the classification performance. This study's main objective is to assess a database's performance consisting of nine upper limb amputee subjects with EMG data recorded at three different force levels when six motions were classified using twenty different time domain features that are frequently found in the literature. Training is carried out at one force level, and the other two unknown force levels are used for testing. Out of the twenty features, the one that is the most stable is displayed for each force level. The results show that root mean square (RMS) feature outperformed other features for training at low and medium force levels, and Wilson amplitude (WAMP) feature for training at a high force level, when compared with the most widely used linear discriminant analysis (LDA) classifier. The average classification accuracy for the nine amputee subjects trained with the RMS feature at low and medium force levels was 42% and 51.78% percent, respectively. For high force level, when trained using WAMP feature, an accuracy of 46.78% has been obtained. The features are verified using histogram plots. This study will help select those features which are not important for robust classification of hand movements. Â© 2023 IEEE.