Accurate EMG onset detection in pathological, weak and noisy myoelectric signals

Yang, Dapeng; Zhang, Huajie; Gu, Yikun; Liu, Hong

doi:10.1016/j.bspc.2016.12.014

Cited by 51 publications

(34 citation statements)

References 18 publications

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“…and has been proven to enhance performances of data processing algorithm when dealing with EMG onset identification [18], [19]. A buffer of 350 samples of the energy of the signal is stored and the envelope of the signal is obtained by weighting the resulting 350 samples with a Gaussian window of weights w G [n] and summing them up together.…”

Section: Methodsmentioning

confidence: 99%

Command Acknowledge through Tactile Feedback Improves the Usability of an EMG-based Interface for the Frontalis Muscle

Franco

Salvietti

Prattichizzo

2019

2019 IEEE World Haptics Conference (WHC)

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This work presents a study on the effectiveness of tactile feedback for the acknowledgement of a correct command detection in an EMG-based interface for the frontalis muscle. EMG interfaces are increasingly used in assistive robotics to control robots exploiting the repeatability and robustness of the electromyographic signal. However, in many application a feedback about the correct detection of an input is often missed and the user has to wait for the device motion in order to understand if his/her will has been correctly detected by the system. We demonstrate with a user study involving fifteen subjects, that a simple vibrotactile feedback can reduce the muscular effort and the time needed to execute a sequence of action commanded by an EMG device. As a case study, an EMG interface for the frontalis muscle has been used, however proposed results could be extended to EMG interfaces designed for other muscles, e.g., for prosthesis or exoskeleton control.

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Section: Methodsmentioning

confidence: 99%

Command Acknowledge through Tactile Feedback Improves the Usability of an EMG-based Interface for the Frontalis Muscle

Franco

Salvietti

Prattichizzo

2019

2019 IEEE World Haptics Conference (WHC)

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“…Moreover, in statistical detection methods, cumulative sum method requires information about the probability density function which is not available in most real-life problems. Since the firing of action potentials increase the signal amplitude of sEMG signal, the Teager-Kaiser Energy (TKE) operator [6][7][8][9] was proposed to highlight this increase and to achieve good performance for the detection of muscle activities especially for sEMG signals that have low SNR. However, the detection methods based on TKE operator was mainly limited to the background noise with Gaussian distribution and they are very sensitive to the spurious background spikes.…”

Section: Introductionmentioning

confidence: 99%

Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study

Hameed

Hasan

Shafie

et al. 2020

IJEECS

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The amplitude dependent muscle activity detection algorithms of the surface electromyography (sEMG) signals are very sensitive to the changes in the background noise levels and the performance of these amplitude-based methods is highly deteriorated when the Signal to Noise ratio (SNR) of the sEMG signal is low. sEMG signals of deep and small muscles as well as sEMG signals recorded from patients that have neuromuscular diseases may not meet this SNR requirement which motivates the need for amplitude independent algorithms that can detect weak muscle activities. Moreover, the sEMG signal amplitude is not constant during the recording time due to the variation in the characteristics of the electrode-skin interface and due to the changes in the ground reference level. Therefore, the performance of the muscle activity detection algorithms should not be affected by the involuntary amplitude variations of the sEMG signal in order to achieve reliable control of robotic devices intended for disabled people. To accentuate the importance of the amplitude independent muscle activity detection methods over the amplitude dependent detection methods, a comparative study has been conducted in this paper between the performance of an amplitude independent muscle activity detection algorithm (FLA-MSE algorithm) and three amplitude dependent algorithms with respect to the detection capability of weak muscle activities and with respect to the immunity against false alarms. The results have showed that the performance of the amplitude independent algorithm outperformed the performance of the amplitude dependent algorithms for detecting weak muscle activities and for robustness against false alarms.

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“…I N the biomedical area, Electromyography (EMG) refers to the study of the electrical activity of the muscle [1]. From a technical side, EMG is a biological signal acquired from muscles with the aim of evaluating their activities [2].…”

Section: Introductionmentioning

confidence: 99%

Myopathy Detection and Classification Based on the Continuous Wavelet Transform

Belkhou

Achmamad

Jbari

2019

JCOMSS

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Electromyography (EMG) is the study of the electrical activity of the muscle. This technique is often used in the diagnosis of neuromuscular diseases. Myopathy is one of these cases, which affect the muscle and causes many changes in the electromyography signal characteristics. This paper presents a new method for analysis and classification of normal and myopathy EMG signals based on continuous wavelet transform (CWT). Classification algorithms, namely Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Discriminant Analysis (DA) and Naïve Bayes (NB) were used as classifiers in our study. Five Features were extracted from the continuous wavelet analysis and used as inputs to the mentioned classifiers. Comparison between different classification methods developed in this study was made by evaluation of their results based on multiple scalar performances, mainly accuracy, sensitivity, and specificity. Different combinations of features with different kernel functions were discussed to achieve better performances. Results showed that k-NN classifier achieved the best performances with an accuracy value of 93.68%.

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Accurate EMG onset detection in pathological, weak and noisy myoelectric signals

Cited by 51 publications

References 18 publications

Command Acknowledge through Tactile Feedback Improves the Usability of an EMG-based Interface for the Frontalis Muscle

Command Acknowledge through Tactile Feedback Improves the Usability of an EMG-based Interface for the Frontalis Muscle

Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study

Myopathy Detection and Classification Based on the Continuous Wavelet Transform

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