An improved feature selection approach using global best guided Gaussian artificial bee colony for EMG classification

Sahu, Padmini; Singh, Bikesh Kumar; Nirala, Neelamshobha

doi:10.1016/j.bspc.2022.104399

Cited by 14 publications

(5 citation statements)

References 38 publications

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“…Ref. [18] introduced variations in the Artificial Bee Colony algorithm, which they applied in a KNN classification system to discern hand movements. Likewise, they highlight that this proposal can have applications in physical activities and physiotherapy therapies thanks to its notable performance.…”

Section: Related Workmentioning

confidence: 99%

Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization

Aviles,

Alvarez-Alvarado,

Robles-Ocampo

et al. 2024

Bioengineering

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Accurate classification of electromyographic (EMG) signals is vital in biomedical applications. This study evaluates different architectures of recurrent neural networks for the classification of EMG signals associated with five movements of the right upper extremity. A Butterworth filter was implemented for signal preprocessing, followed by segmentation into 250 ms windows, with an overlap of 190 ms. The resulting dataset was divided into training, validation, and testing subsets. The Grey Wolf Optimization algorithm was applied to the gated recurrent unit (GRU), long short-term memory (LSTM) architectures, and bidirectional recurrent neural networks. In parallel, a performance comparison with support vector machines (SVMs) was performed. The results obtained in the first experimental phase revealed that all the RNN networks evaluated reached a 100% accuracy, standing above the 93% achieved by the SVM. Regarding classification speed, LSTM ranked as the fastest architecture, recording a time of 0.12 ms, followed by GRU with 0.134 ms. Bidirectional recurrent neural networks showed a response time of 0.2 ms, while SVM had the longest time at 2.7 ms. In the second experimental phase, a slight decrease in the accuracy of the RNN models was observed, standing at 98.46% for LSTM, 96.38% for GRU, and 97.63% for the bidirectional network. The findings of this study highlight the effectiveness and speed of recurrent neural networks in the EMG signal classification task.

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Section: Related Workmentioning

confidence: 99%

Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization

Aviles,

Alvarez-Alvarado,

Robles-Ocampo

et al. 2024

Bioengineering

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“…The authors demonstrated the efficacy of denoising by classifying the EMG data before and after denoising, as well as analysing the characteristics of the recovered noise signal, similarly to [39]. Sahu et al [40,41] proposed using the beta artificial bee colony (BetaABC) and binary BetaABC (BBABC) to pick important features in the recognition of the EMG pattern to improve the classification performance while reducing the complexity of the classifier, where the EMG signal was decomposed and the characteristics were extracted using the discrete wavelet transform (DWT).…”

Section: Review Of Related Approachesmentioning

confidence: 99%

BiomacEMG: A Pareto-Optimized System for Assessing and Recognizing Hand Movement to Track Rehabilitation Progress

et al. 2023

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One of the most difficult components of stroke therapy is regaining hand mobility. This research describes a preliminary approach to robot-assisted hand motion therapy. Our objectives were twofold: First, we used machine learning approaches to determine and describe hand motion patterns in healthy people. Surface electrodes were used to collect electromyographic (EMG) data from the forearm’s flexion and extension muscles. The time and frequency characteristics were used as parameters in machine learning algorithms to recognize seven hand gestures and track rehabilitation progress. Eight EMG sensors were used to capture each contraction of the arm muscles during one of the seven actions. Feature selection was performed using the Pareto front. Our system was able to reconstruct the kinematics of hand/finger movement and simulate the behaviour of every motion pattern. Analysis has revealed that gesture categories substantially overlap in the feature space. The correlation of the computed joint trajectories based on EMG and the monitored hand movement was 0.96 on average. Moreover, statistical research conducted on various machine learning setups revealed a 92% accuracy in measuring the precision of finger motion patterns.

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“…Additional follower bees are dispatched to promising patches, enabling the colony to efficiently acquire food. If a candidate food source does not lead to improved solutions, it is considered ineffective and replaced 24 , 34 .…”

Section: Introductionmentioning

confidence: 99%

Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm

Azeez,

Abdelhaleem,

Elnaggar

et al. 2023

Sci Rep

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This study introduces and compares two optimization techniques, the basic Artificial Bee Colony (ABC) and the enhanced Artificial Bee Colony with multi-elite guidance (MGABC), for determining optimal gains of a Proportional-Integral-Derivative (PID) controller in a 3 degrees of freedom (DOF) rigid link manipulator (RLM) system. The objective function used in the optimization process is a novel function that is based on the well-known Lyapunov stability functions. This function is evaluated against established error-based objective functions commonly used in control systems. The convergence curves of the optimization process demonstrate that the MGABC algorithm outperforms the basic ABC algorithm by effectively exploring the search space and avoiding local optima. The evaluation of the controller's performance in trajectory tracking reveals the superiority of the Lyapunov-based objective function (LBF), with significant improvements over other objective functions such as IAE, ISE, ITAE, MAE and MRSE. The optimized system demonstrates robustness to diverse disturbance conditions and uncertainty in the mass of the payload, while also exhibiting adaptability to joints flexibility without inducing any vibrations in the movement of the end-effector. The proposed techniques and objective function offer promising avenues for the optimization of PID controllers in various robotic applications.

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An improved feature selection approach using global best guided Gaussian artificial bee colony for EMG classification

Cited by 14 publications

References 38 publications

Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization

Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization

BiomacEMG: A Pareto-Optimized System for Assessing and Recognizing Hand Movement to Track Rehabilitation Progress

Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm

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