scholarly journals Identification and Analysis of Limb Rehabilitation Signal Based on Wavelet Transform

2021 ◽  
Vol 38 (3) ◽  
pp. 689-697
Author(s):  
Chao Zhang ◽  
Ji Zou ◽  
Zhongjing Ma
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Support Vector ◽  
Recognition Method ◽  
Semg Signal ◽  
Emg Signal ◽  
Effective Signal ◽  
Limb Rehabilitation ◽  
Rehabilitation Status ◽  
Rehabilitation Exercise

The development of science and technology has promoted the extensive application of surface electromyography (sEMG) collection technique in real-time exercise testing, assistive judgment of rehabilitation therapy, and assessment of intelligent artificial limb application. However, there is a severe lacking of studies on pattern recognition based on effective signal, and evaluation of limb rehabilitation status. To make up for the gap, this paper explores the identification and analysis of limb rehabilitation signal based on wavelet transform. Specifically, the authors detailed the basic flow of sEMG signal generation in motor unit during limb rehabilitation exercise, and proposed a limb EMG pattern recognition method. Then, support vector machine (SVM) was selected to recognize the pattern of the EMG signal extracted from the limb rehabilitation exercise of patients, and to judge the rehabilitation status. Finally, wavelet thresholding was combined with total variation denoising (TVD) to effectively remove the noise from EMG signal. The proposed method was proved effective through experiments.

Four-channel sEMG for Elbow Load Recognition

2021 ◽  
Author(s):  
Jiabin Cai ◽  
Junjun Song ◽  
Yuanqiang Long
Keyword(s):  
Neural Network ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Control Mode ◽  
Support Vector ◽  
Semg Signal ◽  
Joint Force ◽  
Emg Signal ◽  
The Relationship ◽  
Rehabilitation Exercise

Abstract In order to help patients after surgery to carry out reasonable rehabilitation training, avoid joint adhesions and movement disorders, the relationship between surface electromyograph (sEMG) signal changes and the size of the patient ' s joint force in the process of rehabilitation exercise was studied, hoping to use the relationship between them to redesign the control mode of the rehabilitation robot, and a method was proposed to identify the size of the elbow load based on wavelet packet. Firstly, s EMG signals of human elbow joint during stretching and bending under different loads were collected by 4-channel surface electromyography. Then, the wavelet packet decomposition method was used to obtain the feature vector composed of energy(E), variance(VAR) and mean absolute value(MAV) of wavelet packet coefficient. Finally, the improved support vector machine ( ISVM), BP neural network and RBF neural network were used for pattern recognition of three different forces. The experimental results show that the change of sEMG signal is indeed related to the size of joint force. It is feasible to identify the load of s EMG signal.

scholarly journals EMG Signal Based Pattern Recognition Of Grasping Movement Using MODWT And Weighted K- Nearest Neighbor

2019 ◽  
Vol 8 (10) ◽  
pp. 1759-1764
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Signal Processing Technique ◽  
Semg Signal ◽  
Emg Signal ◽  
Semg Signals

This study purposed and evaluates a method based on weighted K-NN classification of surface Electromyogram (sEMG) signals. The sEMG signal classification plays the key role in designing a prosthetic for amputee persons. Wavelet transform is new signal processing technique, which provides better resolution in time and frequency domain simultaneously. Due to these wavelet properties, it can be effectively used in processing the sEMG signal to determine certain amplitude changes at certain frequencies. This paper propose a Maximal overlap Discrete Wavelet Transform (MODWT) approach for Weighted K-NN classifier for classification of sEMG signals based Grasping movements. At level 5 signal decomposition using MODWT, useful resolution component of the sEMG signal is obtained. In this paper Time-domain (TD) features set is used, which shows a decent performance. In WKNN, use a square-inverse weighted technique to improve the performance of the K-NN. Hence, a novel feature set obtained from decomposed signal using MODWT is used to improve the performance of sEMG for classification. MODWT was used for de-noising and time scale feature extraction of sEMG signals. Several WKNN classifiers are tested to optimize classification accuracy and computational problems. PCA is use to reduce the size of the level 5 decomposed data. WKNN performance evaluation on K=10 values with or without PCA. Six hand grasping movements have been classified, results indicate that this method allows the classification of hand pattern recognition with high precision.

scholarly journals Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Jingzong Yang ◽  
Xiaodong Wang ◽  
Zao Feng ◽  
Guoyong Huang
Keyword(s):  
Pattern Recognition ◽  
Impulse Response ◽  
Information Entropy ◽  
Operating Conditions ◽  
Support Vector ◽  
Entropy Theory ◽  
Pattern Recognition Method ◽  
Response Signal ◽  
Recognition Method ◽  
Learning Machine

Aiming at the nonstationary and nonlinear characteristics of acoustic impulse response signal in pipeline blockage and the difficulty in identifying the different degrees of blockage, this paper proposed a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM). Firstly, the impulse response signals of pipeline extracted in different operating conditions were decomposed with LMD method into a series of product functions (PFs). Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors. Finally, ELM was introduced for classification of pipeline blockage. Through the analysis of acoustic impulse response signal collected under the condition of health and different degrees of blockages in pipeline, the results show that the proposed method can well characterize the state information. Also, it has a great advantage in terms of accuracy and it is time consuming when compared with the support vector machine (SVM) and BP (backpropagation) model.

scholarly journals Design of Mirror Therapy System Base on Multi-Channel Surface-Electromyography Signal Pattern Recognition and Mobile Augmented Reality

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2142
Author(s):  
Lizheng Liu ◽  
Jianjun Cui ◽  
Jian Niu ◽  
Na Duan ◽  
Xianjia Yu ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Augmented Reality ◽  
Surface Electromyography ◽  
Hand Movement ◽  
Low Noise ◽  
Support Vector ◽  
Mobile Augmented Reality ◽  
Mirror Therapy ◽  
Semg Signal ◽  
Signal Pattern

Numerous studies have proven that the mirror therapy can make rehabilitation more effective on hemiparesis following a stroke. Using surface electromyography (SEMG) to predict gesture presents one of the important subjects in related research areas, including rehabilitation medicine, sports medicine, prosthetic control, and so on. However, current signal analysis methods still fail to achieve accurate recognition of multimode motion in a very reliable way due to the weak physiological signal and low noise-ratio. In this paper, a mirror therapy system based on multi-channel SEMG signal pattern recognition and mobile augmented reality is studied. Besides, wavelet transform method is designed to mitigate the noise. The spectrogram obtained by analyzing electromyography signals is proposed to be used as an image. Two approaches, including Convolutional Neural Network (CNN) and grid-optimized Support Vector Machine (SVM), are designed to classify the SEMG of different gestures. The mobile augmented reality provides a virtual hand movement in the real environment to perform mirror therapy process. The experimental results show that the overall accuracy of SVM is 93.07%, and that of CNN is up to 97.8%.

Pattern Recognition of Chatter Gestation Based on Hybrid PCA-SVM

2011 ◽  
Vol 120 ◽  
pp. 190-194 ◽  
Author(s):  
Qiang Shao ◽  
Chang Jian Feng
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Support Vector Machine ◽  
Vibration Signal ◽  
Cutting Process ◽  
Support Vector ◽  
Recognition Method ◽  
Chatter Prediction ◽  
Dynamic Patterns

To distinguish chatter gestation, chatter recognition method based on hybrid PCA(Principal Compenent Analysis) and SVM(Support Vector Machine) is proposed for dynamic patterns of chatter gestation in cutting process. At first, FFT features are extracted from the vibration signal of cutting process, then FFT vectors are presorted and introduced to PCA-SVM for machine learning and classification. Finally the results of chatter gestation recognition and chatter prediction experiments are presented and show that the method proposed is effective.

scholarly journals Automatic amyotrophic lateral sclerosis detection using tunable Q-factor wavelet transform

2020 ◽  
Vol 9 (4) ◽  
pp. 744
Author(s):  
Abdelouahad Achmamad ◽  
Abdelali Belkhou ◽  
Atman Jbari
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Wavelet Transform ◽  
Nearest Neighbor ◽  
Neuromuscular Disorders ◽  
Computation Time ◽  
Support Vector ◽  
Q Factor ◽  
K Nearest Neighbor ◽  
Emg Signal ◽  
Lateral Sclerosis

Early diagnosis of amyotrophic lateral sclerosis (ALS) based on electromyography (EMG) is crucial. The processing of a non-stationary EMG signal requires powerful multi-resolution methods. Our study analyzes and classifies the EMG signals. In the present work, we introduce a novel flexible method for classification of EMG signals using tunable Q-factor wavelet transform (TQWT). Different sub-bands generated by the TQWT technique were served to extract useful information related to energy and then the calculated features were selected using a filter selection (FS) method. The effectiveness of the feature selection step resulted not only in the improvement of classification performance but also in reducing the computation time of the classification algorithm. The selected feature subsets were used as inputs to multiple classifier algorithms, namely, k-nearest neighbor (k-NN), least squares support vector machine (LS-SVM) and random forest (RF) for automated diagnosis. The experimental results show better classification measures with k-NN classifier compared with LS-SVM and RF. The robustness of the classification task was tested using a ten-fold cross-validation method. The outcomes of our proposed approach can be exploited to aid clinicians in neuromuscular disorders detection.

scholarly journals Statistical Feature Extraction Combined with Generalized Discriminant Component Analysis Driven SVM for Fault Diagnosis of HVDC GIS

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7674
Author(s):  
Ruixu Zhou ◽  
Wensheng Gao ◽  
Weidong Liu ◽  
Dengwei Ding ◽  
Bowen Zhang
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Pulse Current ◽  
State Of The Art ◽  
Selection Criterion ◽  
Component Analysis ◽  
Support Vector ◽  
Stable Operation ◽  
Pattern Recognition Method ◽  
Recognition Method

Accurately identifying the types of insulation defects inside a gas-insulated switchgear (GIS) is of great significance for guiding maintenance work as well as ensuring the safe and stable operation of GIS. By building a set of 220 kV high-voltage direct current (HVDC) GIS experiment platforms and manufacturing four different types of insulation defects (including multiple sizes and positions), 180,828 pulse current signals under multiple voltage levels are successfully measured. Then, the apparent discharge quantity and the discharge time, two inherent physical quantities unaffected by the experimental platform and measurement system, are obtained after the pulse current signal is denoised, according to which 70 statistical features are extracted. In this paper, a pattern recognition method based on generalized discriminant component analysis driven support vector machine (SVM) is detailed and the corresponding selection criterion of involved parameters is established. The results show that the newly proposed pattern recognition method greatly improves the recognition accuracy of fault diagnosis in comparison with 36 kinds of state-of-the-art dimensionality reduction algorithms and 44 kinds of state-of-the-art classifiers. This newly proposed method not only solves the difficulty that phase-resolved partial discharge (PRPD) cannot be applied under DC condition but also immensely facilitates the fault diagnosis of HVDC GIS.

scholarly journals A Study on Stock Graph Recognition Based on Wavelet Denoising and DTW Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ji Wei Luo
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Stock Price ◽  
Time Window ◽  
Recognition Rate ◽  
Future Price ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Window Method ◽  
Price Patterns

The “classical pattern” of stock price formation has long been widely used in the determination of future price trends of stocks, and the identification and analysis of classical price patterns have an important guiding role in investors’ decision-making and trading. The wavelet transform is a useful tool to remove some of the noise of time series because it has the characteristic of multiresolution. In this study, we propose a method for stock price pattern recognition based on the wavelet transform and dynamic time warp (DTW). A pattern recognition method with similar quantified results is developed to obtain accurate pattern recognition results. That is, using the wavelet transform to smooth the original price graph, and then using the DTW algorithm improved in this study to find the graph with the smallest distance from the target graph under the sliding window method, the identification and analysis of the target graph can be realized. In order to improve the recognition rate of the target graph, we preprocessed the raw price sequence using the moving average convergence and divergence (MACD) algorithm based on the control experiments set up in this study. The pattern recognition method used in this study will identify the price patterns of a certain time window as a whole, thus avoiding the problem of how to objectively select the important points that constitute a price pattern and the mathematical definition of different price patterns in the previous traditional methods.

FATIGUE CONTRACTION ANALYSIS USING EMPIRICAL MODE DECOMPOSITION AND WAVELET TRANSFORM

2015 ◽  
Vol 77 (6) ◽  
Author(s):  
Rubana Haque Chowdhury ◽  
Mamun Bin Ibne Reaz
Keyword(s):  
Wavelet Transform ◽  
Muscle Fatigue ◽  
Empirical Mode Decomposition ◽  
Surface Emg ◽  
Semg Signal ◽  
Emg Signal ◽  
Mode Decomposition ◽  
Spectral Variable ◽  
Walking Exercise ◽  
The Difference

Muscle fatigue is a long lasting reduction of the ability to contract and it is the condition when produced force is reduced. Walking fast can cause muscle fatigue, which is unhealthy and it is incurable when the level of fatigue is high. Muscle fatigue during walk can be determined using several spectral variables. The amplitude and frequency of the surface EMG signal provide a more accurate reflection of motor unit pattern among these spectral variables. This research reports on the effectiveness of Empirical mode decomposition (EMD) and wavelet transform based filtering method applied to the surface EMG (sEMG) signal as a means of achieving reliable discrimination of the muscle fatigue during human walking exercise. In this research, IAV, RMS and AIF values were used as spectral variable. These spectral variables extensively identifies the difference between fatigue and normal muscle when using EMD method compared with other different wavelet functions (WFs). The result shows that the sEMG amplitude and frequency momentously changes from rest position to maximum contraction position.

A NEW FAULT PATTERN RECOGNITION METHOD BASED ON WPT AND DAGSVM

2009 ◽  
Vol 08 (03) ◽  
pp. 345-353 ◽  
Author(s):  
GUANG-MING XIAN ◽  
BI-QING ZENG
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Roller Bearing ◽  
Support Vector ◽  
Second Phase ◽  
Pattern Recognition Method ◽  
Recognition Method ◽  
Fault Pattern ◽  
Model Setup

A new pattern recognition method based on wavelet packet transform (WPT) and directed acyclic graph support vector machine (DAGSVM) is put forward for fault diagnosis of roller bearing. The fault pattern recognition model setup has two phases. The first phase is to extract the feature of faulty vibration signals from roller bearing by WPT via a db3 wavelet. The second phase is to use DAGSVM to recognize fault pattern of roller bearing. The testing results illustrates that WPT is more effective to diagnose fault types than the WT method. It is observed that among the strategy of multi-class SVM, DAGSVM acquires the highest accuracy, and therefore, this demonstrates the fact that suitable fault pattern recognition strategy can improve the overall performance of fault diagnosis. The present research illustrated that the features extracted by WPT represent the fault pattern of roller bearing, and the DAGSVM trained on these features achieved high recognition accuracies.

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