Human shoulder motion extraction using EMG signals

Jang, Gunhee; Kim, Jung-Hoon; Choi, Youngjin; Yim, Jongguk

doi:10.1007/s12541-014-0580-x

Cited by 15 publications

(9 citation statements)

References 9 publications

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“…The Wigner-Willy distribution is the distribution of signal energy in time and frequency, and it has many excellent features. For example, the domain identity, inversion characteristics, etc., which makes it in the non-stationary EMG signal processing has considerable potential [7].…”

Section: Time-frequency Analysis Methodsmentioning

confidence: 99%

Research on EMG Signal Extraction Based on Biomimetic Hand

Xu¹,

Sun²,

Li³

2017

Proceedings of the 7th International Conference on Management, Education, Information and Control (MEICI 2017)

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Abstract. EMG is a signal which could reflect the information of human body's movement in residual limb muscle group. As a source of information, it could control the bionic prosthesis to complete the multi-degree of freedom of action. EMG prosthesis controlled the bionic prosthesis through the body's own physiological signal. It is remarkable significant for the disabled users that if the bionic prosthesis could understand the movement of the disabled users will. The purpose of the EMG signal acquisition is driving a bionic hand which could help the hand disabilities to achieve grasping, pushing, taking, holding etc. EMG prosthetic is a complex system, which involving multidisciplinary disciplines. And this paper mainly introduces the method of EMG extraction.

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Section: Time-frequency Analysis Methodsmentioning

confidence: 99%

Research on EMG Signal Extraction Based on Biomimetic Hand

Xu¹,

Sun²,

Li³

2017

Proceedings of the 7th International Conference on Management, Education, Information and Control (MEICI 2017)

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“…In this stage, the normalized WAMP parameter can represent the elbow-joint angle estimation with some ripples and noises. Therefore, to smooth the EMGN, it needs a filtering process as recommended by previous researchers [10]. The LPF was produced digitally, based on Infinite Impulse Responses (IIR).…”

Section: Data Processingmentioning

confidence: 99%

“…However, they reported that the correlation coefficient was lower for a light object and high speed. Jang et al used a spring-damper pendulum model to measure the shoulder joint for flexion movement based on an EMG signal [10]. By extracting the EMG signal into the function, Yu et al used the EMG signal to predict the position of the elbow.…”

Section: Introductionmentioning

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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“…However, the problem in elbow joint angle estimation which involves machine learning based methods is that the method requires a learning stage to recognize the EMG pattern. Previous studies also developed a non-machine learning-based method to estimate the elbow-joint angle and force using EMG signal [15][16][17][18]. However, the problem and limitation are that the previous studies have not considered the effect of the muscle fatigue to the performance of the estimation.…”

Section: Introductionmentioning

confidence: 99%

“…where denotes the n th input sample, denotes n th output sample, b0, b1, bM, a1, a2, aN are the filter coefficients, and P=Q is the filter order. Previous studies used this filtering technique to smooth the estimation [17,42]. In this study, the coefficients of low pass filter, the feature extraction, and low pass filtering was performed using MATLAB (Student version, Math Works, Inc., USA).…”

mentioning

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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Human shoulder motion extraction using EMG signals

Cited by 15 publications

References 9 publications

Research on EMG Signal Extraction Based on Biomimetic Hand

Research on EMG Signal Extraction Based on Biomimetic Hand

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

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

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