Beta Artificial Bee Colony Algorithm for EMG Feature Selection

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

doi:10.1007/978-981-19-5868-7_1

Cited by 1 publication

(1 citation statement)

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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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Section: Review Of Related Approachesmentioning

confidence: 99%