scholarly journals Estimation and Correlation Analysis of Lower Limb Joint Angles Based on Surface Electromyography

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 556 ◽  
Author(s):  
Junhong Wang ◽  
Lipeng Wang ◽  
Xugang Xi ◽  
Seyed M. Miran ◽  
Anke Xue
Keyword(s):  
Correlation Analysis ◽  
Prediction Model ◽  
Surface Electromyography ◽  
Wavelet Packet ◽  
Joint Angles ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Cord Injury ◽  
Semg Signals

Many people lose their motor function because of spinal cord injury or stroke. This work studies the patient’s continuous movement intention of joint angles based on surface electromyography (sEMG), which will be used for rehabilitation. In this study, we introduced a new sEMG feature extraction method based on wavelet packet decomposition, built a prediction model based on the extreme learning machine (ELM) and analyzed the correlation between sEMG signals and joint angles based on the detrended cross-correlation analysis. Twelve individuals participated in rehabilitation tasks, to test the performance of the proposed method. Five channels of sEMG signals were recorded, and denoised by the empirical mode decomposition. The prediction accuracy of the wavelet packet feature-based ELM prediction model was found to be 96.23% ± 2.36%. The experimental results clearly indicate that the wavelet packet feature and ELM is a better combination to build a prediction model.

scholarly journals EMD Method for Minimizing the Effect of Seasonal Trends in Detrended Cross-Correlation Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Keqiang Dong ◽  
You Gao ◽  
Nianpeng Wang
Keyword(s):  
Correlation Analysis ◽  
Long Range ◽  
Cross Correlation ◽  
Intrinsic Mode Functions ◽  
Nonstationary Signals ◽  
Seasonal Trends ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Correlated Signals ◽  
Emd Method

Detrended cross-correlation analysis (DCCA) is a scaling method commonly used to estimate long-range power-law cross-correlation in nonstationary signals. Recent studies have reported signals superimposed with trends, which often lead to the complexity of the signals and the susceptibility of DCCA. This paper artificially generates long-range cross-correlated signals and systematically investigates the effect of seasonal trends. Specifically, for the crossovers raised by trends, we propose a smoothing algorithm based on empirical mode decomposition (EMD) method which decomposes underlying signals into several intrinsic mode functions (IMFs) and a residual trend. After the removal of slowly oscillating components and residual term, seasonal trends are eliminated.

scholarly journals Feature Extraction of Surface Electromyography Using Wavelet Weighted Permutation Entropy for Hand Movement Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xiaoyun Liu ◽  
Xugang Xi ◽  
Xian Hua ◽  
Hujiao Wang ◽  
Wei Zhang
Keyword(s):  
Feature Extraction ◽  
Surface Electromyography ◽  
Recognition Accuracy ◽  
Hand Movement ◽  
Hand Movements ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Semg Signals

The feature extraction of surface electromyography (sEMG) signals has been an important aspect of myoelectric prosthesis control. To improve the practicability of myoelectric prosthetic hands, we proposed a feature extraction method for sEMG signals that uses wavelet weighted permutation entropy (WWPE). First, wavelet transform was used to decompose and preprocess sEMG signals collected from the relevant muscles of the upper limbs to obtain the wavelet sub-bands in each frequency segment. Then, the weighted permutation entropies (WPEs) of the wavelet sub-bands were extracted to construct WWPE feature set. Lastly, the WWPE feature set was used as input to a support vector machine (SVM) classifier and a backpropagation neural network (BPNN) classifier to recognize seven hand movements. Experimental results show that the proposed method exhibits remarkable recognition accuracy that is superior to those of single sub-band feature set and commonly used time-domain feature set. The maximum recognition accuracy rate is 100% for hand movements, and the average recognition accuracy rates of SVM and BPNN are 100% and 98%, respectively.

A Detection Method for Friction Vibration Based on Harmonic Wavelet Packet Transform and Cross-Correlation Analysis

2018 ◽  
Vol 140 (3) ◽  
Author(s):  
Pengfei Xing ◽  
Guobin Li ◽  
Ting Liu ◽  
Hongtao Gao ◽  
Guoyou Wang
Keyword(s):  
Steady State ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Detection Method ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Mean Square ◽  
Vibration Signals ◽  
Cross Correlation Analysis ◽  
Harmonic Wavelet

Running-in wear experiments were conducted on a spherical-on-disk tester. The vibration signals collected in the experiments were detected by a combination of harmonic wavelet packet transform (HWPT) and cross-correlation analysis (CCA) methods. Experimental results show that the friction vibration signals detected in tangential and normal directions have the characteristics of no time delay and strong correlation. Their root-mean-square (RMS) values gradually reduce and enter a steady-state of fluctuation with the experiments time, which are consistent with the variation of friction coefficient and reflect the change of wear states from the running-in wear to the stable wear. Therefore, the detection of friction vibration can be realized by a combination of HWPT and CCA methods.

Multiscale Detrended Cross-Correlation Analysis of Traffic Time Series Based on Empirical Mode Decomposition

2015 ◽  
Vol 14 (03) ◽  
pp. 1550023 ◽  
Author(s):  
Yi Yin ◽  
Pengjian Shang
Keyword(s):  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
Long Range ◽  
Cross Correlation ◽  
Traffic Signals ◽  
Correlation Structure ◽  
Noise Effects ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Cross Correlations

In this paper, we propose multiscale detrended cross-correlation analysis (MSDCCA) to detect the long-range power-law cross-correlation of considered signals in the presence of nonstationarity. For improving the performance and getting better robustness, we further introduce the empirical mode decomposition (EMD) to eliminate the noise effects and propose MSDCCA method combined with EMD, which is called MS-EDXA method, then systematically investigate the multiscale cross-correlation structure of the real traffic signals. We apply the MSDCCA and MS-EDXA methods to study the cross-correlations in three situations: velocity and volume on one lane, velocities on the present and the next moment and velocities on the adjacent lanes, and further compare their spectrums respectively. When the difference between the spectrums of MSDCCA and MS-EDXA becomes unobvious, there is a crossover which denotes the turning point of difference. The crossover results from the competition between the noise effects in the original signals and the intrinsic fluctuation of traffic signals and divides the plot of spectrums into two regions. In all the three case, MS-EDXA method makes the average of local scaling exponents increased and the standard deviation decreased and provides a relative stable persistent scaling cross-correlated behavior which gets the analysis more precise and more robust and improves the performance after noises being removed. Applying MS-EDXA method avoids the inaccurate characteristics of multiscale cross-correlation structure at the short scale including the spectrum minimum, the range for the spectrum fluctuation and general trend, which are caused by the noise in the original signals. We get the conclusions that the traffic velocity and volume are long-range cross-correlated, which is accordant to their actual evolution, while velocities on the present and the next moment and velocities on adjacent lanes reflect the strong cross-correlations both in temporal and spatial dimensions. We also reveal the similarity and uniqueness in the cross-correlation situations between velocities. Besides, signals on one lane show stronger long-range cross-correlation than that on adjacent lanes. Thus, the multiscale cross-correlation structure acquired by MS-EDXA is more close to the intrinsic mechanism of traffic system and reflects more accurate and more abundant traffic information.

MULTIFRACTAL DETRENDED CROSS-CORRELATION ANALYSIS OF HIGH-FREQUENCY STOCK SERIES BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION

Fractals ◽  
2020 ◽  
Vol 28 (02) ◽  
pp. 2050035 ◽  
Author(s):  
DANLEI GU ◽  
JINGJING HUANG
Keyword(s):  
Correlation Analysis ◽  
Empirical Mode Decomposition ◽  
High Frequency ◽  
Cross Correlation ◽  
Ensemble Empirical Mode Decomposition ◽  
Original Series ◽  
Mode Decomposition ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
Eemd Method

We used the multifractal detrended cross-correlation analysis (MFDCCA) method based on ensemble empirical mode decomposition (EEMD) to study the 5-min high-frequency data of two Chinese stocks and two US stocks. Using EEMD method to decompose the original high-frequency stock data can effectively reduce the interference of noise on the series, which helps to reveal the internal characteristics of the stock system and extract more accurate and rich information. We first conducted a cross-correlation test and cross-correlation coefficient analysis on the reconstructed stock data of two groups, and found that there is a cross-correlations between them. Then we used the EEMD-based MFDCCA method to analyze the cross-correlation between the data and found that there are significant cross-correlations between DJI and NASDAQ and between SSEC and SZSE. The cross-correlation of the two Chinese stocks is stronger than that of the two US stocks. The MFDCCA results of the comparison of the original series with the reconstructed series after decomposition by the EEMD method show that the reconstructed series can display more internal details of the multifractal cross-correlation metrics compared with the original series.

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