Identification of Contaminant Type in Surface Electromyography (EMG) Signals

McCool, Paul; Fraser, G. D.; Chan, Adrian D. C.; Petropoulakis, Lykourgos; Soraghan, John J.

doi:10.1109/tnsre.2014.2299573

Cited by 76 publications

(43 citation statements)

References 25 publications

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“…The robustness of the features was examined by adding an artificial white Gaussian noise (AWGN) to the original EMG signal [3] [5]. The AWGN was generated randomly with zero mean and adjusted standard deviation (SD).…”

Section: B Signal To Noise Ratiomentioning

confidence: 99%

“…In the standard MYOP feature, the threshold value is defined in the initial process using a real constant value. Nevertheless, in the modified MYOP M feature, the RMS of the EMG signal was used as a threshold value, as formulated in the equation (3). Therefore, the threshold values were changed adaptively by the RMS of the EMG signal.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Recently, the electromyography (EMG) has been used a lot in many applications to support human for better life, such as prosthetic devices, exoskeleton robotic (as an assistive or rehabilitative function) [1]. Generally, the EMG signal has a low amplitude, so it is easy to be contaminated by external or internal noise [2] [3]. The internal noise may come from the electrocardiography (ECG) signal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust Features for Elbow Joint Angle Estimation Based on Electromyography

Triwiyanto

Wahyunggoro

Nugroho

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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A noisy environment is a major problem which has to be resolved to get a good performance in the estimation. A robust feature is important in order to obtain an accurate position of the elbow joint from the electromyography (EMG) signal. The objective of this research is to modify and assess the time domain features which robust against the white Gaussian noise. In this work, the EMG signal (from biceps) contaminated by artificial white Gaussian noise was extracted using twelve standard time domain features and one modified feature. The threshold of the modified feature (MYOP M ) was calculated based on the root mean square (RMS) of the contaminated EMG signal. The linear Kalman filter was used to refine the EMG features and to improve the estimation. The robustness of the features was calculated using the root mean square error (RMSE). Based on the RMSE values, it shows that the proposed feature MYOP M is the most robust feature (the lowest median RMSE of 9º) for the signal to noise ratio (SNR) ranged between 17.96 and 60 dB, compared with the others' features. The mean RMSE of the MYOP M feature improves by 27.91% from the prior feature (MYOP).

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Section: B Signal To Noise Ratiomentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Features for Elbow Joint Angle Estimation Based on Electromyography

Triwiyanto

Wahyunggoro

Nugroho

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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show abstract

“…Based on different voluntary signals and motions, there are roughly four categories of ATs for persons with tetraplegia [1], [2]: 1) Physiological Signal Based ATs: Various types of physiological signals are employed to control computers, such as ElectroMyoGram (EMG) [3], [4], ElectroEncephaloGram (EEG) [5], [6] and ElectroOculoGram (EOG) [7], in which Z.-P. Bian, Junhui Hou and L.-P. Chau are with the School of Electrical and Electronics Engineering, Nanyang Technological University, 639798 Singapore (email: zbian1@e.ntu.edu.sg, houj0001@e.ntu.edu.sg, elpchau@ntu.edu.sg).…”

Section: A Related Workmentioning

confidence: 99%

Facial Position and Expression-Based Human–Computer Interface for Persons With Tetraplegia

Bian

Hou

Chau

et al. 2016

IEEE J. Biomed. Health Inform.

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A human-computer interface (namely Facial position and expression Mouse system, FM) for the persons with tetraplegia based on a monocular infrared depth camera is presented in this paper. The nose position along with the mouth status (close/open) is detected by the proposed algorithm to control and navigate the cursor as computer user input. The algorithm is based on an improved Randomized Decision Tree, which is capable of detecting the facial information efficiently and accurately. A more comfortable user experience is achieved by mapping the nose motion to the cursor motion via a nonlinear function. The infrared depth camera enables the system to be independent of illumination and color changes both from the background and on human face, which is a critical advantage over RGB camera-based options. Extensive experimental results show that the proposed system outperforms existing assistive technologies in terms of quantitative and qualitative assessments.

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“…Most of the noise, artifacts and interference that may contaminate sEMG signals consist of electrode noise, motion artifacts, power line noise, ambient noise and inherent noise in electrical & electronic equipment [18,19]. There are some works like [2,19,20] which respectively aim on classify and identify the destructive interference present in the sEMG signal. Indeed, the acquisition process is a critical stage that directly affects the posterior processing stages, undermining the classification process and accuracy of the system.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive Method for sEMG Movement Classification Based on Continuous Optimal Electrode Assortment

Favieiro

Cene

Balbinot

2016

BJIC

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Abstract-Surface electromyography (sEMG) analysis is becoming increasingly popular in a broad variety of applications. Despite satisfactory classification rates are frequently obtained through supervised machine learning (ML) algorithms, there are some issues mostly related to the data acquisition which are not properly addressed in current studies. In this paper we present a method capable of mitigate the noise in the sEMG acquisition caused mainly by loose or misplaced non-invasive electrodes. To address this issue we propose a stage of pre-processing capable of being adapted on a variety of classifiers. The proposed method is capable of identify this two anomalies in the signal and provide the data to retrain the classifier, discarding the problematic channels. Once the method is retrained using only the most relevant channels it is possible to increase the accuracy rate of the ML method. The method was tested on a database containing five ablebodied subjects and four amputee subjects of both sexes. The average classification accuracy for the adaptive input selection method was 83,96  6,5% for the able-bodied subjects and 61,15  7,7% for the amputees subjects against 72,06  8,0% in ablebodied subjects and 39,77  10,6% for the amputees subjects considering the non-adaptive approach. Both systems make use of the proposed method to classify 9 distinguish upper-limb movements with different degrees of freedom.Index Terms-electrode assortment, upper-limb signal, neural network, auto-adaptive methods, surface electromyography

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Identification of Contaminant Type in Surface Electromyography (EMG) Signals

Cited by 76 publications

References 25 publications

Robust Features for Elbow Joint Angle Estimation Based on Electromyography

Robust Features for Elbow Joint Angle Estimation Based on Electromyography

Facial Position and Expression-Based Human–Computer Interface for Persons With Tetraplegia

Self-adaptive Method for sEMG Movement Classification Based on Continuous Optimal Electrode Assortment

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