Feature extraction of surface electromyography (sEMG) and signal processing technique in wavelet transform: A review

Burhan, Nuradebah; Kasno, Mohammad'Afif; Ghazali, Rozaimi

doi:10.1109/i2cacis.2016.7885304

Cited by 21 publications

(13 citation statements)

References 30 publications

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“…A Daubechies mother wavelet of fourth order ‘db4′ was used due to its similarity to the triphasic pattern of the motor unit action potential [44]. Consistent with the analysis of other bio-signals, DWT decomposition was performed using six levels [43,45,46,47,48]. The wavelet analysis was performed in two steps, as presented in Figure 2:The EMG signals were decomposed into seven sub-bands, one approximate coefficient (cA6), and six detail coefficients (cD1, …, cD6).The EMG signal was then reconstructed at each level using inverse discrete wavelet transform, and seven EMG reconstructed signals (A6, D1, …, D6) were obtained from their coefficients (cA6, cD1, …, cD1).…”

Section: Methodsmentioning

confidence: 99%

Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes

Masood

Abdullah

Seth

et al. 2019

Sensors

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This study aims to characterize traumatic spinal cord injury (TSCI) neurophysiologically using an intramuscular fine-wire electromyography (EMG) electrode pair. EMG data were collected from an agonist-antagonist pair of tail muscles of Macaca fasicularis, pre- and post-lesion, and for a treatment and control group. The EMG signals were decomposed into multi-resolution subsets using wavelet transforms (WT), then the relative power (RP) was calculated for each individual reconstructed EMG sub-band. Linear mixed models were developed to test three hypotheses: (i) asymmetrical volitional activity of left and right side tail muscles (ii) the effect of the experimental TSCI on the frequency content of the EMG signal, (iii) and the effect of an experimental treatment. The results from the electrode pair data suggested that there is asymmetry in the EMG response of the left and right side muscles (p-value < 0.001). This is consistent with the construct of limb dominance. The results also suggest that the lesion resulted in clear changes in the EMG frequency distribution in the post-lesion period with a significant increment in the low-frequency sub-bands (D4, D6, and A6) of the left and right side, also a significant reduction in the high-frequency sub-bands (D1 and D2) of the right side (p-value < 0.001). The preliminary results suggest that using the RP of the EMG data, the fine-wire intramuscular EMG electrode pair are a suitable method of monitoring and measuring treatment effects of experimental treatments for spinal cord injury (SCI).

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Section: Methodsmentioning

confidence: 99%

Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes

Masood

Abdullah

Seth

et al. 2019

Sensors

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“…The TFD features are a good choice for processing sEMG signals due to their ability to process non-stationary signal like sEMG signals where the presence of frequency components vary with time. (Burhan, Kasno, & Ghzali, 2016) [9] in their work explained the processing of such non-stationary signals in detail using TFD techniques such as Short-time Fourier Transform(STFT), wavelet transform(WT), and wavelet packet transform(WPT).…”

Section: 2: Feature Extractionmentioning

confidence: 99%

Performance Evaluation of EMG Pattern Recognition Techniques While Increasing The Number of Movement Classes

2020

JST

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:In the past few years of research done in the field of myoelectric control, many researchers have proposed several models imploying a combination of different features and classifiers to increase the movement classes, but all that work fails to explain if there is any correlation between multi-class classification and its accuracy. This paper focuses on finding the factors that decide the limit of movement classes that machine learning algorithms can accurately differentiate and to evaluate the performance of pattern classification techniques using the sEMG signal when the number of movement classes is increased while keeping the simplicity of the system. The results were obtained for eight channels sEMG signal using 7 independent time-domain features and four feature set combinations over 4 classifiers (Support Vector Machine(SVM), K-Nearest Neighbour(K-NN), Decision Tree(DT), and Naïve Bayes(NB)). Then the number of classes was increased in the manner of 5, 7, 10, 12, and 15 classes to determine the highest number of movement classes that the sEMG system with above-described features can classify efficiently. And the effect of increasing the number of movement classes on system accuracy was observed. The highest accuracies for all five class progression were obtained for SVM with the MFL feature, and for DT using MAV, it was successfully observed that the NB classifier had minimum performance depletion for the features used in this work

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“…Feature extraction and classification are the most critical technologies in sEMG-based pattern recognition technology. To date, the time domain, frequency domain, and time-frequency domain features have been widely used for the analysis and processing of sEMG signals (Burhan and Ghazali, 2016; Majid et al, 2018; Phinyomark et al, 2018). In addition, many classifier algorithms have appeared for classification, such as SVM, artificial neural networks (ANNs), and linear discriminant analysis (LDA) (Chowdhury et al, 2013; Nazmi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

sEMG-Based Trunk Compensation Detection in Rehabilitation Training

Chen

Zhang

et al. 2019

Front. Neurosci.

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Stroke patients often use trunk to compensate for impaired upper limb motor function during upper limb rehabilitation training, which results in a reduced rehabilitation training effect. Detecting trunk compensations can improve the effect of rehabilitation training. This study investigates the feasibility of a surface electromyography-based trunk compensation detection (sEMG-bTCD) method. Five healthy subjects and nine stroke subjects with cognitive and comprehension skills were recruited to participate in the experiments. The sEMG signals from nine superficial trunk muscles were collected during three rehabilitation training tasks (reach-forward-back, reach-side-to-side, and reach-up-to-down motions) without compensation and with three common trunk compensations [lean-forward (LF), trunk rotation (TR), and shoulder elevation (SE)]. Preprocessing like filtering, active segment detection was performed and five time domain features (root mean square, variance, mean absolute value (MAV), waveform length, and the fourth order autoregressive model coefficient) were extracted from the collected sEMG signals. Excellent TCD performance was achieved in healthy participants by using support vector machine (SVM) classifier (LF: accuracy = 94.0%, AUC = 0.97, F1 = 0.94; TR: accuracy = 95.8%, AUC = 0.99, F1 = 0.96; SE: accuracy = 100.0%, AUC = 1.00, F1 = 1.00). By using SVM classifier, TCD performance in stroke participants was also obtained (LF: accuracy = 74.8%, AUC = 0.90, F1 = 0.73; TR: accuracy = 67.1%, AUC = 0.85, F1 = 0.71; SE: accuracy = 91.3%, AUC = 0.98, F1 = 0.90). Compared with the methods based on cameras or inertial sensors, better detection performance was obtained in both healthy and stroke participants. The results demonstrated the feasibility of the sEMG-bTCD method, and it helps to prompt the stroke patients to correct their incorrect posture, thereby improving the effectiveness of rehabilitation training.

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Feature extraction of surface electromyography (sEMG) and signal processing technique in wavelet transform: A review

Cited by 21 publications

References 30 publications

Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes

Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes

Performance Evaluation of EMG Pattern Recognition Techniques While Increasing The Number of Movement Classes

sEMG-Based Trunk Compensation Detection in Rehabilitation Training

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