[HTML][HTML] Design of robust adaptive Volterra noise mitigation architecture for sEMG signals using metaheuristic approach
Surface Electromyogram (sEMG) signals, like other electrophysiological measurements, get
corrupted by several artefacts; much critical helpful information regarding a person's clinical …
corrupted by several artefacts; much critical helpful information regarding a person's clinical …
[HTML][HTML] Automated detection and removal of artifacts from sEMG signals based on fuzzy inference system and signal decomposition methods
Surface electromyography (sEMG) signal quality decreases when it is contaminated by
different types of artifacts. Detection and removal of the contaminants from sEMG signals …
different types of artifacts. Detection and removal of the contaminants from sEMG signals …
[HTML][HTML] Noise confiscation from sEMG through enhanced adaptive filtering based on evolutionary computing
Electromyogram (EMG) signal is the electrical form of muscular activity that could be used to
diagnose myopathy and neuropathy disorders. Several artefacts are getting imposed on the …
diagnose myopathy and neuropathy disorders. Several artefacts are getting imposed on the …
[HTML][HTML] sEMG signal filtering study using synchrosqueezing wavelet transform with differential evolution optimized threshold
C Li, H Deng, S Yin, C Wang, Y Zhu - Results in Engineering, 2023 - Elsevier
Most gesture recognition studies based on surface electromyography (sEMG) signals focus
on filtering, in which the lack of diversity for considered noises can still be the problem. In …
on filtering, in which the lack of diversity for considered noises can still be the problem. In …
Denoising of surface electromyogram based on complementary ensemble empirical mode decomposition and improved interval thresholding
Surface electromyogram (sEMG) signals are physiological signals that are widely applied in
certain fields. However, sEMG signals are frequently corrupted by noise, which can lead to …
certain fields. However, sEMG signals are frequently corrupted by noise, which can lead to …
基于NMF-SVM 模型的上肢sEMG 手势识别方法.
隋修武， 牛佳宝， 李昊天… - Journal of Computer …, 2020 - search.ebscohost.com
针对基于表面肌电信号进行动作识别的问题, 按照不同的运动形态对应的各肌肉激活程度不同的
思路, 建立基于非负矩阵分解(NMF) 与支持向量机(SVM) 的联合模型, 对从肌电信号中提取的 …
思路, 建立基于非负矩阵分解(NMF) 与支持向量机(SVM) 的联合模型, 对从肌电信号中提取的 …
[HTML][HTML] A threshold selection criterion based on the number of runs for the detection of bursts in EMG signals
JA Guerrero, JE Macías-Díaz - Biomedical Signal Processing and Control, 2020 - Elsevier
In this work, a method to calculate the threshold value in the detection of activity phases of
electromyographic signals is introduced. The criterion used by the method is based on the …
electromyographic signals is introduced. The criterion used by the method is based on the …
Machine Learning-Based Intelligent Smart Embedded Sensors for Automatic Detection and Classification of Neuromuscular Disorders using EMG Signals
The objective of this work is to create a novel computer-aided health monitoring system for
diagnosing neuromuscular disorders (NMDs). Additionally, we will propose the use of …
diagnosing neuromuscular disorders (NMDs). Additionally, we will propose the use of …
[PDF][PDF] Applications of normalised mutual information in high density surface electromyography
A Bingham - 2019 - core.ac.uk
Normalised mutual information (NMI) is a measure derived from Shannon's entropy that is
used to analyse the dependence between variables [1]. Since it measures the dependence …
used to analyse the dependence between variables [1]. Since it measures the dependence …