Classification of Hand Motions from Surface Electromyography with Rough Entropy

Shi, Jun; Zhao, Panbo; Cai, Yin; Jia, Jie

doi:10.1166/jmihi.2015.1396

Cited by 2 publications

(3 citation statements)

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“…37 However, the hierarchical segmentation technique cannot be applied to large datasets and does not work well with mixed data types. 37 Nonetheless, there have been some recent advances, including envelope extraction, 38 rough segmentation technique 39 and segmentation adjustment based on integral signal. 40 Envelope extraction aids in identifying the start and endpoints of an EMG signal and is executed by specifying the window length and the desired signal amplitude.…”

Section: Segmentation Of Electromyography Signalsmentioning

confidence: 99%

“…38 Rough segmentation tends to compute the derivative value of the enveloped signal according to the specified signal amplitude. 39 If there are still some interference left in the EMG signal, signal adjustment based on integral value can be implemented by computing the integral of the enveloped signal through adjustment of the window length. 40 These segmentation methods tend to overcome the issues of previous techniques and can be applied to mixed and large datasets.…”

Section: Segmentation Of Electromyography Signalsmentioning

confidence: 99%

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Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction

Ali

Khan

Azim

et al. 2022

Proc Inst Mech Eng H

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Upper limb myoelectric prosthetic control is an essential topic in the field of rehabilitation. The technique controls prostheses using surface electromyogram (sEMG) and intramuscular EMG (iEMG) signals. EMG signals are extensively used in controlling prosthetic upper and lower limbs, virtual reality entertainment, and human-machine interface (HMI). EMG signals are vital parameters for machine learning and deep learning algorithms and help to give an insight into the human brain’s function and mechanisms. Pattern recognition techniques pertaining to support vector machine (SVM), k-nearest neighbor (KNN) and Bayesian classifiers have been utilized to classify EMG signals. This paper presents a review on current EMG signal techniques, including electrode array utilization, signal acquisition, signal preprocessing and post-processing, feature selection and extraction, data dimensionality reduction, classification, and ultimate application to the community. The paper also discusses using alternatives to EMG signals, such as force sensors, to measure muscle activity with reliable results. Future implications for EMG classification include employing deep learning techniques such as artificial neural networks (ANN) and recurrent neural networks (RNN) for achieving robust results.

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Section: Segmentation Of Electromyography Signalsmentioning

confidence: 99%

Section: Segmentation Of Electromyography Signalsmentioning

confidence: 99%

Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction

Ali

Khan

Azim

et al. 2022

Proc Inst Mech Eng H

View full text Add to dashboard Cite

show abstract

“…In order to resolve the above problems, SampEn can be used [20]. FuzzyEn is a proper criterion for representing fuzziness and differentiating the complicated signals [22].…”

Section: Wwwjbpeirmentioning

confidence: 99%

Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF

Valipour

Esteki

2019

J Biomed Phys Eng

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Background: Hand tremor is one of the consequences of MS disease degrading quality of patient’s life. Recently DBS is used as a prominent treatment to reduce this effect. Evaluation of this approach has significant importance because of the prevalence rate of disease.Objective: The purpose of this study was the nonlinear analysis of tremor signal in order to evaluate the quantitative effect of DBS on reducing MS tremor and differentiating between them using pattern recognition algorithms.Material and Methods:In this paper, nine features were extracted from the tremor signal. Through statistical analysis, the significance level of each feature was examined. Finally, tremor signals were categorized by SVM, weighted KNN and NN classifiers. The performance of methods was compared with an ROC graph.Results:The results have demonstrated that dominant frequency, maximum amplitude and energy of the first IMF, deviation of the direct path, sample entropy and fuzzy entropy have the potential to create a significant difference between the tremor signals. The classification accuracy rate of tremor signals in three groups for Weighted KNN, NN and SVM with Gaussian and Quadratic kernels resulted in 95.1%, 93.2%, 91.3% and 88.3%, respectively.conclusion: Generally, nonlinear and nonstationary analyses have a high potential for a quantitative and objective measure of MS tremor. Weighted KNN has shown the best performance of classification with the accuracy of more than 95%. It has been indicated that DBS has a positive influence on reducing the MS tremor. Therefore, DBS can be used in the objective improvement of tremor in MS patients.

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Classification of Hand Motions from Surface Electromyography with Rough Entropy

Cited by 2 publications

References 0 publications

Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction

Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction

Pattern Classification of Hand Movement Tremor in MS Patients with DBS ON and OFF

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