Electromyography Signal on Biceps Muscle in Time Domain Analysis

Yahya, Abu Bakar; Daud, Wan Mohd Bukhari Wan; Horng, Chong Shin; Sudirman, Rubita

doi:10.15282/jmes.7.2014.17.0115

Cited by 9 publications

(5 citation statements)

References 14 publications

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“…To get the effective signals, each channel using bipolar configuration and distance between two electrodes is chosen at 15mm [30]. Feature extraction is one of the challenging parts in surface EMG pattern recognition [14,32]. It is important to reduce noise in EMG signal.…”

Section: Methodology 21 Materials and Methodsmentioning

confidence: 99%

“…In the pursuit of accurate EMG analysis, the development of recommendations for SEMG sensors and sensor placement procedures by Hermens et al, [9] and study of EMG sensor to muscle serves as a valuable resource [10][11][12]. Additionally, studies by Corbett et al, [13], as well as Yahya et al, [14], have shed light on the extraction of neural strategies from surface EMG and the associations between motor unit action potential features and surface electromyography parameters.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Electromyography Indices of Handgrip Force with Swinging Motion

Wan Hisarudin Wan Abdullah,

Wan Mohd Bukhari Wan Daud,

Mohammad Osman Tokhi

et al. 2023

ARASET

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Electromyography (EMG) is a powerful tool for studying muscle activity, but there is limited research on EMG signals in the muscles of the human forearm, which poses challenges for prosthetic hand development. This study utilized the maximum voluntary contraction (MVC) normalization method to analyze the flexor carpi radialis muscle during handgrip and swinging motions. The MVC indices revealed a significant proportion of high-amplitude MVC results. We conducted three statistical analyses to validate the indices. One-way ANOVA showed significant differences in mean values among the seven subjects during the percent MVC test. RMS study demonstrated a linear correlation between muscle contraction and movement. Boxplot analysis revealed variations within the interquartile range and median values across the entire MVC range. To achieve these results, we employed an eighth-order Gaussian function for curve fitting and exponential weighted moving average. The median interquartile range showed high discrepancies, while the differences between MVC increments were minimal, providing reliable indices for swinging motion. This suggests that the fat layer thickness may influence the muscle signal's frequency characteristics. In summary, our study highlights the untapped potential of EMG signals in the forearm muscles for prosthetic hand development. By employing MVC normalization and conducting rigorous statistical analyses, we uncovered significant findings that contribute to advancements in this field. Our insights provide hope and inspiration for researchers and practitioners seeking to enhance prosthetic technology.

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Section: Methodology 21 Materials and Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electromyography Indices of Handgrip Force with Swinging Motion

Wan Hisarudin Wan Abdullah,

Wan Mohd Bukhari Wan Daud,

Mohammad Osman Tokhi

et al. 2023

ARASET

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“…In Yahya et al [5], the authors have extracted several features of temporal EMG signals during hand-lifting of several loads. The authors suggested that interpreting EMG signals in time domain through feature extraction is better than studying those signals in frequency domain.…”

Section: Related Workmentioning

confidence: 99%

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

et al. 2019

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Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

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“…Commonly the threshold-values of these features were set by a real constant value [35][36][37]. Over a long period of contraction in dynamic motion (flexion and extension), the muscle fatigue would appear and the power of EMG also increased significantly as mention in the previous studies [23,26,27]. In order the features able to adapt to the fatigue condition, the threshold values needed to be adjusted adaptively so that the output of the estimation could be maintained as in the non-fatigue condition.…”

Section: Data Processing Evaluation and Statistical Analysismentioning

confidence: 99%

“…Therefore, the feature such as MPF which based on frequency domain needs a high computation time to process. In addition to the features based on the frequency domain, the time domain features are the most popular used to extract the EMG signal into some information, related to the elbow-joint angle [3,10,26]. Time domain features are divided into three categories, namely based on energy, complexities, and frequency [27].…”

Section: Introductionmentioning

confidence: 99%

Adaptive threshold to compensate the effect of muscle fatigue on elbow-joint angle estimation based on electromyography

Triwiyanto¹,

Wahyunggoro²,

Nugroho³

et al. 2018

JMES

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Muscle fatigue is a major problem in the study based on electromyography (EMG) signal. When the muscle in the fatigue condition, the power of the EMG signal increases significantly. This phenomenon can be a problem in the elbow-joint angle estimation. The purpose of this study is to develop a method in order to compensate the effect of the muscle fatigue on the elbow-joint angle estimation based on time domain features using EMG signal. The EMG signal was collected from biceps while the subjects performed a fatiguing motion of flexion and extension. The EMG was extracted using four time-domain features, namely zero crossing (ZC), sign slope change (SSC), Wilson amplitude (WAMP) and myopulse percentage rate (MYOP). The yielded features were filtered using second order Butterworth low pass filter. In the proposed method, to compensate the effect of the muscle fatigue, the RMS of the EMG signal was calculated for every cycle and used it as a threshold value of the features. The results show that the proposed method is able to compensate the effect of muscle fatigue with a consistent root mean square error (RMSE). The improvement of the performance ranges between 17.41% and 37.9% (for all adaptive features).

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Electromyography Signal on Biceps Muscle in Time Domain Analysis

Cited by 9 publications

References 14 publications

Electromyography Indices of Handgrip Force with Swinging Motion

Electromyography Indices of Handgrip Force with Swinging Motion

Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network

Adaptive threshold to compensate the effect of muscle fatigue on elbow-joint angle estimation based on electromyography

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