Processing Surface Electromyographical Signals for Myoelectric Control

Herle, S.; Man, S.

doi:10.5772/7383

Cited by 5 publications

(7 citation statements)

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“…In the last decades, experiments on active prosthetics based on surface electromyography (sEMG) signals have increased significantly. Such experiments aimed to increase functionalities by using multichannel sEMG signals and to decrease the total response time [1][2][3][4]. Hence, active prosthetics will be more intuitive and possess close functionalities from the limbs.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, an experiment conducted by Khokhar et al [1] showed that the sEMGbased system can classify movement commands after detecting sEMG signals with high accuracy (up to ±96%) and a short total time response (250 ms). Figure 1 illustrates the general block diagram of sEMG-based active prosthetics: (a) general diagram block of active prosthetics for controlling its velocity; (b) a general block diagram of active prosthetic controlling its velocity and functionality [4]. Kirchner et al developed another method to shorten the total time response of active prosthetics [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Brain-Computer Interface: a Novel Method on the Integration of EEG and sEMG Signal for Active Prosthetic Control

Darmakusuma¹,

Prihatmanto²,

Indrayanto³

et al. 2018

MST

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This paper describes a novel method for controlling active prosthetics by integrating surface electromyography (sEMG) and electroencephalograph signals to improve its intuitiveness. This paper also compares the new method (RTA-2) with other existing methods (AND and OR) for controlling active prosthetics. Based on analysis, RTA-2 features higher true positive rate (TPR) and balanced accuracy (BA) than AND method. On the other hand, the new method (RTA-2) yields lower false detection rate (FPR) than OR method. Analysis also shows that RTA-2 possesses equal TPR, FPR, and BA with the detection of movement intention using sEMG-based system. Although the RTA-2 method shows equal performance with the sEMG-based system, it presents an advantage for driving active prosthetics to move faster and to reduce its total time response by generating more movement commands. Abstrak Hybrid Brain-Computer Interface: Metode Baru dalam Integrasi Sinyal EEG dan sEMG untuk Pengendalian Aktif Prosthetics. Paper ini menjelaskan metode baru untuk mengendalikan prosthetics aktif dengan mengintegrasikan sinyal elektromiograf (sEMG) dan elektroensefalograf (EEG) dalam rangka meningkatkan sifat intuitif yang dimilikinya. Selain itu, dalam paper ini juga membandingkan metode baru (RTA-2) dengan metode lain yang telah ada (AND dan OR) untuk mengendilikan prosthetics aktif. Berdasarkan analisis, metode RTA-2 memiliki nilai True Positive Rate (TPR) dan Balanced Accuracy (BA) lebih tinggi dibandingkan metode AND. Selain hal terebut, metode RTA-2 memilki kesalahan deteksi (FPR) yang lebih rendah dibandingkan metode OR. Berdasarkan analasis, nilai TPR, FPR dan BA yang dimiliki metode RTA-2 ini sama dengan akurasi deteksi intensi gerakan berbasis sinyal sEMG. Namun demikian, meskipun TPR, FPR dan BA dari metode RTA-2 sama dengan metode yang hanya berbasis sinyal sEMG, metode RTA-2 memiliki keunggulan dalam mengendalikan prosthetic aktif sehingga dapat bergerak dengan kecepatan lebih cepat dari sebelumnya dan mengurangi total waktu responnya dengan cara menghasilkan perintah keluaran kecepatan gerakan yang lebih banyak.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Brain-Computer Interface: a Novel Method on the Integration of EEG and sEMG Signal for Active Prosthetic Control

Darmakusuma¹,

Prihatmanto²,

Indrayanto³

et al. 2018

MST

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“…In measuring the neural drive of muscles it is common practice to convert the raw EMG into a form more convenient for further analysis. Features may be computed in several domains, such as time domain, frequency domain, time-frequency and time-scale representations [5], [6].…”

Section: Introductionmentioning

confidence: 99%

EMG real-time classification for robotics and HMI

Zimenko

Margun

Kremlev

2013

2013 18th International Conference on Methods &Amp; Models in Automation &Amp; Robotics (MMAR)

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There is method for real time frequency analysis of the electromyogram is described in article. This method is proposed to use in control systems of advanced robotics for medical and healthcare applications. Method consists of realtime frequency estimation with preliminary using of band-pass filter.The method is easy to program and does not require any additional resources.The article gives an example of using this method in classification of movements problem which is actual for robotics and human-machine interfaces.

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“…The classification is based on features for each type of movement. These features may be computed in several domains, such as time domain, frequency domain, timefrequency and time-scale representations (Herle and Man [2009]; Parker et al [2006] Time-frequency domain features show better performance than other-domain features in case of assessing transient properties of a signal. The central concept of most of the methods is a decomposition of a signal into time-frequency atoms:…”

Section: Introductionmentioning

confidence: 99%