Faculty Publications
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Publications by NITK Faculty
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Item Hybrid Electric Bicycle with Regeneration Capability(Springer Science and Business Media Deutschland GmbH, 2021) Koppolu, P.K.; Cmc, K.; Jagtap, A.; Dharmadikari, R.; Gajare, T.This paper presents a method to make a hybrid electric bicycle by modifying the existing conventional bicycle keeping the ability to pedal. The cycle consists of a battery, motor drive, which comprises a DC–DC converter for the motor, as well as regenerative braking. This cycle has a unique feature of exercise mode where the user can charge the battery as in stationary cycles in the gymnasium. The bicycle covers 28 km with a maximum speed of 23 km/h. In one charge which is sufficient from a city point of view, where pollution and traffic congestion is a significant concern these days. This paper also presents mechanical designs, electronics design of DC–DC converter, power supply, and a microcontroller with test results. © 2021, Springer Nature Singapore Pte Ltd.Item 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. © 2022 IEEE.Item 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. © 2023 IEEE.Item 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. © 2023 IEEE.Item 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