Faculty Publications
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Item 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 LtdItem 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 LtdItem Reducing the effect of wrist variation on pattern recognition of Myoelectric Hand Prostheses Control through Dynamic Time Warping(Elsevier Ltd, 2020) Powar, O.S.; Chemmangat, K.For upper limb prostheses, research carried out earlier mainly focused on increasing the classification accuracy of the hand movements; but there exist a little work done on factors affecting it in real-time control such as wrist variation. Amputees with functional wrist use their prostheses in multiple wrist positions. Since the Electromyography (EMG) data is taken while the subject is performing the motion in different wrist position, it can degrade the performance of the Pattern Recognition (PR) system. In this work, a wrist independent PR scheme has been developed. In this regard, Dynamic Time Warping (DTW) is used to overcome the effects due to wrist variation. The performance of the DTW scheme as a PR system is validated using two training methods; with classification accuracy as a performance measure on data taken from the database of ten intact subjects for six hand motions carried out at three different wrist orientations. On the database, an average classification accuracy of about 93.3% was obtained while trained using EMG data from all possible wrist positions. The effectiveness of the method is demonstrated in terms of classification accuracy and processing time when compared with the Time-domain power spectral descriptors (TD-PSD) method which outperformed other methods in the literature for reducing the impact of wrist variation on EMG based PR. The results show that the DTW can be a computationally cheap and accurate PR system for real-time hand movement classification. © 2019 Elsevier LtdItem 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 LtdItem 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 LtdItem 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.Item IMU-based segmental root mean square analysis of gait in individuals with cerebellar ataxia: a pilot cross-sectional study(Nature Research, 2025) Mendonca, J.; Joshua, A.M.; Shetty, S.; Chemmangat, K.; Krishnan, S.; Kumar, K.V.; Misri, Z.; Pai, R.; Pai, S.Cerebellar ataxia (CA) affects limb movement, balance, and gait. Subjective rating scales like Scale for the Assessment and Rating of Ataxia (SARA) may underestimate gait severity. Inertial measurement units (IMUs) offer an objective gait analysis. Impaired trunk control might compromise gait performance and stability in individuals with ataxia. This study quantified trunk kinematics and gait parameters using Root Mean Square (RMS) values, comparing CA to healthy individuals. Ten CA cases and twenty healthy controls were recruited. Six IMU sensors positioned at anatomical landmarks recorded data via two ESP32 microcontrollers using Wi-Fi. Participants walked a 10-meter path at a self-selected pace. RMS mean linear and angular velocity and angular deviation were calculated. Individuals with CA showed decreased mediolateral linear acceleration at the left shoulder (p = 0.001) and an increased vertical linear acceleration at the right ankle (p = 0.015), left shoulder (p = 0.028), and back (p = 0.019). Total angular velocity was lower at the right shoulder (p = 0.017), left shoulder (p = 0.005), back (p = 0.002), and both ankles (right: p = 0.001; left: p = 0.001). The correlation between IMU-derived features and SARA-gait score in the CA group was not statistically significant (all p > 0.05), except for the right shoulder’s mediolateral angular velocity (p = 0.046). Both ankle segments’ angular deviations (right: p = 0.001; left: p = 0.006) were reduced. The CA group revealed reduced RMS linear and angular velocities. IMU-based trunk and gait analysis provides a more objective method that would help in planning targeted rehabilitation treatments. Trial registration: The study was approved by the Institutional Ethics Committee (IEC), Kasturba Medical College, Mangalore, Manipal Academy of Higher Education (IEC KMC MLR 12/2023/483) on 21st December 2023 and the Clinical Trial Registration (CTRI/2024/07/070614) on July 15th, 2024. © The Author(s) 2025.