Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion

Triwiyanto, Triwiyanto; Wahyunggoro, Oyas; Nugroho, Hanung Adi; Herianto, Herianto

doi:10.15598/aeee.v15i3.2173

Cited by 8 publications

(9 citation statements)

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“…In the following sections we will explain in detail the design process that was carried out for each of the blocks. muscle [13][14][15][16]. However, its use is sometimes restricted due to factors such as cost and lack of technical knowledge.…”

Section: Methodsmentioning

confidence: 99%

“…In the sports field, with electromyography it is possible to observe muscle fatigue caused by static exercises [12], in which sustained contractions are generated without varying the length of the muscle, or dynamic, where there are contractions in which there is a flexion and extension of the muscle [13][14][15][16]. However, its use is sometimes restricted due to factors such as cost and lack of technical knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Implementation of a Portable Electromyographic Prototype for the Detection of Muscle Fatigue

2019

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Surface electromyography (sEMG) applied to the sports training area makes possible the observation of fatigue as well as the generation of muscular strength, through the study of changes in signal characteristics, such as peak-to-peak amplitude, mean frequency and median, among others. In this sense, this work presents the design of a portable prototype for the acquisition and processing of electromyographic (EMG) signals aimed at the detection of muscle fatigue in athletes. Using two Bluetooth Bee modules, a wireless communication was performed in order to send the muscular electrical activity of the skin surface to a user interface developed in LabVIEW. A group of players from the Volleyball team of the Universidad del Magdalena, performed a series of exercise routines with dynamic contractions and as they experienced fatigue, samples were taken of the contractions made. The tests were performed on the vastus lateralis and rectus femoris muscles. The analysis of fatigue under dynamic conditions of the two parameters studied, in frequency and time, showed that it is more pertinent to estimate fatigue indices in the frequency domain.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Implementation of a Portable Electromyographic Prototype for the Detection of Muscle Fatigue

2019

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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%

“…Previous studies were preferred to use a non-invasive method for detecting the muscle fatigue using EMG signal [19,20]. Since the muscle fatigue caused the frequency and amplitude of the EMG signal to change [21][22][23], a new method has to be introduced to compensate the effect of muscle fatigue so that the estimation can be maintained. Na proposed a method to estimate the elbow joint force using muscle twitch model [24].…”

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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“…Therefore, it needs an investigation of the muscle group that was related to the movements and motion direction of the limb. Muscle fatigue is a condition to be further addressed in the data collection process, as Basmajian and de Luca reported that muscle fatigue affected spectral parameters of surface electromyography signal [22,23]. In this analysis, the muscle was assumed to be in a non-fatigue state.…”

Section: Estimationmentioning

confidence: 99%

Dynamic feature for an effective elbow-joint angle estimation based on electromyography signals

Triwiyanto

Rahmawati

Yulianto

et al. 2020

IJEECS

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<a name="_Hlk561743"></a><span>Some physical parameters influence the electromyography signal (EMG). when the EMG signal is used to estimate the position of the elbow. An adaptable feature was important to reduce a variation on the parameters. The aim of this paper is to estimate the joint position of the elbow using EMG signal based on a dynamic function. The major contribution of this work is that the method proposed is capable of determining the elbow position using the non-pattern (NPR) recognition (PR) method. A Wilson amplitude (WAMP) which used a dynamic threshold was used to reduce the EMG signal. The dynamic threshold was generated from the root mean square (RMS) processor. With the dynamic threshold, the model could adapt to any variations on the independent variables. In order to confirm this opportunity, this work involved ten healthy male subjects to perform an experimental protocol. After a tuning and calibration process, the mean of RMS error and correlation coefficient are 9.83º±1.69º and 0.98±0.01 for a single cycle of motion, 10.39º±1.82º and 0.97±0.01 for a continuous cycle of motion and 15.19º±1.92º and 0.94±0.02 for the arbitrary gesture. For conclusion, the performance of the prediction did not significantly depend on the varying cycle of gesture (p-value>0.05). This study has confirmed that the success of the non-pattern recognition-based prediction of elbow position is adaptable to any different subjects, loads, and speed of motion</span><span>.</span>

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Continuous Wavelet Transform Analysis of Surface Electromyography for Muscle Fatigue Assessment on the Elbow Joint Motion

Cited by 8 publications

References 0 publications

Implementation of a Portable Electromyographic Prototype for the Detection of Muscle Fatigue

Implementation of a Portable Electromyographic Prototype for the Detection of Muscle Fatigue

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

Dynamic feature for an effective elbow-joint angle estimation based on electromyography signals

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