A real-time EMG pattern recognition method for virtual myoelectric hand control

Xing, Kexin; Yang, Peipei; Huang, Jian; Wang, Yongji; Zhu, Quanmin

doi:10.1016/j.neucom.2013.12.010

Cited by 105 publications

(48 citation statements)

References 19 publications

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“…WPT has also gained interest because of its ability to provide the frequency information in both low frequency band and high frequency band. Although TFD features are computationally more complex than TD features, they can be implemented with fast algorithms that have shown to be capable to meet the real-time requirements in sEMG classification when appropriate dimensional reduction and segmentation techniques are used [92,98,122,152,154]. Wavelet transforms may improve the robustness of the system compared to TD and FD features because by using subsets of wavelet coefficients the analysis can be restricted only to interesting frequency bands.…”

Section: Time-frequency Domain Featuresmentioning

confidence: 99%

Current state of digital signal processing in myoelectric interfaces and related applications

Hakonen

Piitulainen

Visala

2015

Biomedical Signal Processing and Control

258

185

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a b s t r a c tThis review discusses the critical issues and recommended practices from the perspective of myoelectric interfaces. The major benefits and challenges of myoelectric interfaces are evaluated. The article aims to fill gaps left by previous reviews and identify avenues for future research. Recommendations are given, for example, for electrode placement, sampling rate, segmentation, and classifiers. Four groups of applications where myoelectric interfaces have been adopted are identified: assistive technology, rehabilitation technology, input devices, and silent speech interfaces. The state-of-the-art applications in each of these groups are presented.

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Section: Time-frequency Domain Featuresmentioning

confidence: 99%

Current state of digital signal processing in myoelectric interfaces and related applications

Hakonen

Piitulainen

Visala

2015

Biomedical Signal Processing and Control

258

185

View full text Add to dashboard Cite

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“…Electrode Type and Body Region [5,14,16,18,19,37,39,40,[48][49][50] Monopolar and Upper limb [2,12,26,51] Monopolar and Lower limb [36,47] Monopolar and Facial muscles [4,6,[9][10][11]15,20,22,24,27,29,31,32,34,35,44,[52][53][54] Bipolar and Upper limb [13,30] Bipolar and Lower limb [45] Bipolar and Cheek [7] Bipolar and Facial muscles…”

Section: Referencementioning

confidence: 99%

“…TFD features are more sophisticated computationally than time-domain features. However, there are fast algorithms with which the characteristics can be implemented in TFD in order that real-time requirements necessary for MES classification are still met [9,11,50,57].…”

Section: Time-frequency Domain (Tfd)mentioning

confidence: 99%

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

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This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

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“…These systems work by extracting relevant features from the EMG signals and then using a classification method to separate them into different groups. Most of the proposed methods in EMG classification are focused on using signals to identify a specific arm, hand, or leg movement and use this information to command prosthesis or other mechanisms (Alkan & Günay, 2012;Phinyomark et al, 2013;Xing, Yang, Huang, Wang, & Zhu, 2014). Nevertheless, there are a number of researchers interested in the development of a system specifically intended to diagnose neuromuscular diseases (Yousefi & Hamilton-Wright, 2014).…”

Section: Introductionmentioning

confidence: 99%

Neuromuscular disease detection by neural networks and fuzzy entropy on time‐frequency analysis of electromyography signals

et al. 2018

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Analysis of electromyography (EMG) signals is a necessary step in the diagnosis of neuromuscular diseases. Automatic classification systems can assist specialists and optimize the diagnostic process by applying time‐frequency analysis, fuzzy entropy, and neural networks to EMG signals in order to identify the presence of characteristics of a specific disorder, such as myopathy and amyotrophic lateral sclerosis. The performance of a decision support system depends on three important issues: the correct estimation of features from the EMG signal, the proper criteria for relevance analysis, and the learning process of the classification algorithm. In this paper, Discrete Wavelet Transform and Fuzzy Entropy are used to extract and select features from EMG signals, whereas Artificial Neural Networks are used to give the recognition result. The database used in this study is available for public use in EMGLAB, which is a website for sharing data, software, and information related to EMG decomposition. Results using the combination of these techniques show an accuracy around 98% for identifying EMG signals from three classes: healthy, patients with myopathy, or evidence of amyotrophic lateral sclerosis.

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A real-time EMG pattern recognition method for virtual myoelectric hand control

Cited by 105 publications

References 19 publications

Current state of digital signal processing in myoelectric interfaces and related applications

Current state of digital signal processing in myoelectric interfaces and related applications

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Neuromuscular disease detection by neural networks and fuzzy entropy on time‐frequency analysis of electromyography signals

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