CleanEMG &amp;#x2014; Power line interference estimation in sEMG using an adaptive least squares algorithm

Fraser, G. D.; Chan, Adrian D. C.; Green, James R.; Abser, N.; MacIsaac, Dawn

doi:10.1109/iembs.2011.6091958

Cited by 19 publications

(12 citation statements)

References 9 publications

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“…Several solutions have been developed to reduce the interference in the acquired biomedical signals. However, a residual interference of these interferences still presents [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. The signal contamination by motion artifacts causes data irregularities.…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Moura

Balbinot

2018

Sensors

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A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior.

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Section: Introductionmentioning

confidence: 99%

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Moura

Balbinot

2018

Sensors

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“…The strategies adopted to contaminant detection generally relies on extracting statistical metrics (Fraser et al 2013;Thongpanja et al 2016) and handcrafted features of sEMG signal (Fraser et al 2014;Fraser et al 2012b). For contaminant removal, it is commonly employed filters (De Luca et al 2010;Petersen et al 2020) or techniques to estimate the interference parameters (Fraser et al 2012a;Fraser et al 2011).…”

Section: Analysis Of the Presence Of Contaminants In The Semg Signalmentioning

confidence: 99%

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Tosin

Machado²,

Balbinot³

2022

jair

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Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants. This review exposes the current scenario of the movement classification system, providing valuable information for new researchers and guiding future works towards myo-controlled devices.

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“…urface Electromyography (EMG) is a non-invasive measurement method of muscle activity that can be used for telehealth [1] and for prosthesis control [2] [3]. The presence of noise [4][5][6][7][8][9][10][11][12] such as measurement noise [7,8], power line interference, quantisation noise, ECG and motion artifact [4,5] obscure the information content of the signal and reduce its usefulness for pattern recognition-based prosthetic control by causing a reduction in gesture classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Once digitized, band pass filtering is used to restrict the frequency content to the band within which most of the energy of the EMG resides. Notch filtering or adaptive filtering [5] blind rejection [9] or spectral interpolation [10] can also be performed to remove 50Hz or 60Hz power line interference. ECG can be removed by applying template methods or moving average filtering [11].…”

Section: Introductionmentioning

confidence: 99%

“…The methods were tested by artificially adding noise to EMG signals. ECG, motion artifact, Additive White Gaussian Noise (AWGN), amplifier saturation and power line interference were added to EMG in [4][5][6] to test the feasibility of pattern recognition. AWGN was used in [12] to assess the robustness of features and to assess noise reduction techniques such as wavelets in [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Improved pattern recognition classification accuracy for surface myoelectric signals using spectral enhancement

McCool

Petropoulakis

Soraghan

et al. 2015

Biomedical Signal Processing and Control

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In this paper, we demonstrate that Spectral Enhancement techniques can be configured to improve the classification accuracy of a pattern recognition-based myoelectric control system. This is based on the observation that, when the subject is at rest, the power in EMG recordings drops to levels characteristic of the noise. Two Minimum Statistics techniques, which were developed for speech processing, are compared against electromyographic (EMG) de-noising methods such as wavelets and Empirical Mode Decomposition. In the cases of simulated EMG signals contaminated with white noise and for real EMG signals with added and intrinsic noise the gesture classification accuracy was shown to increase. The mean improvement in the classification accuracy is greatest when Improved Minima-Controlled Recursive Averaging (IMCRA)-based Spectral Enhancement is applied, thus demonstrating the potential of Spectral Enhancement techniques for improving the performance of pattern recognition-based myoelectric control

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CleanEMG — Power line interference estimation in sEMG using an adaptive least squares algorithm

Cited by 19 publications

References 9 publications

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Improved pattern recognition classification accuracy for surface myoelectric signals using spectral enhancement

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