scholarly journals A Muscle Fatigue Classification Model Based on LSTM and Improved Wavelet Packet Threshold

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6369
Author(s):  
Junhong Wang ◽  
Shaoming Sun ◽  
Yining Sun
Keyword(s):  
Muscle Fatigue ◽  
Vastus Lateralis ◽  
Wavelet Packet ◽  
Classification Model ◽  
Threshold Function ◽  
Support Vector ◽  
Semg Signal ◽  
Recognition Model ◽  
Model Based ◽  
Lstm Network

Previous studies have used the anaerobic threshold (AT) to non-invasively predict muscle fatigue. This study proposes a novel method for the automatic classification of muscle fatigue based on surface electromyography (sEMG). The sEMG data were acquired from 20 participants during an incremental test on a cycle ergometer using sEMG sensors placed on the vastus rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), and gastrocnemius (GA) muscles of the left leg. The ventilation volume (VE), oxygen uptake (VO2), and carbon dioxide production (VCO2) data of each participant were collected during the test. Then, we extracted the time-domain and frequency-domain features of the sEMG signal denoised by the improved wavelet packet threshold denoising algorithm. In this study, we propose a new muscle fatigue recognition model based on the long short-term memory (LSTM) network. The LSTM network was trained to classify muscle fatigue using sEMG signal features. The results showed that the improved wavelet packet threshold function has better performance in denoising sEMG signals than hard threshold and soft threshold functions. The classification performance of the muscle fatigue recognition model proposed in this paper is better than that of CNN (convolutional neural network), SVM (support vector machine), and the classification models proposed by other scholars. The best performance of the LSTM network was achieved with 70% training, 10% validation, and 20% testing rates. Generally, the proposed model can be used to monitor muscle fatigue.

scholarly journals A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 757
Author(s):  
Yongke Pan ◽  
Kewen Xia ◽  
Li Wang ◽  
Ziping He
Keyword(s):  
Optimization Algorithm ◽  
Oil And Gas ◽  
The Other ◽  
Whale Optimization Algorithm ◽  
Classification Model ◽  
Support Vector ◽  
Recognition Model ◽  
Novel Approach ◽  
Whale Optimization ◽  
Oil And Gas Reservoir

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition.

hERG Classification Model Based on a Combination of Support Vector Machine Method and GRIND Descriptors

2008 ◽  
Vol 5 (1) ◽  
pp. 117-127 ◽  
Author(s):  
Qiyuan Li ◽  
Flemming Steen Jørgensen ◽  
Tudor Oprea ◽  
Søren Brunak ◽  
Olivier Taboureau
Keyword(s):  
Support Vector Machine ◽  
Classification Model ◽  
Support Vector ◽  
Machine Method ◽  
Support Vector Machine Method ◽  
Model Based ◽  
Grind Descriptors

scholarly journals A Comparative Study of EMG Indices in Muscle Fatigue Evaluation Based on Grey Relational Analysis during All-Out Cycling Exercise

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lejun Wang ◽  
Yuting Wang ◽  
Aidi Ma ◽  
Guoqiang Ma ◽  
Yu Ye ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Power Output ◽  
Vastus Lateralis ◽  
Wavelet Packet ◽  
Vastus Lateralis Muscle ◽  
Cycling Exercise ◽  
Warm Up ◽  
Wavelet Packet Transformation ◽  
Fatigue Evaluation ◽  
Grey Relational

The increased popularization of cycling has brought an increase in cycling-related injuries, which has been suggested to be associated with muscle fatigue. However, it still remains unclear on the utility of different EMG indices in muscle fatigue evaluation induced by cycling exercise. In this study, ten cyclist volunteers performed a 30-second all-out cycling exercise after a warm-up period. Surface electromyography (sEMG) from vastus lateralis muscle (VL) and power output and cadence were recorded and EMG RMS, MF and MPF based on Fourier Transform, MDF and MNF based on wavelet packet transformation, and C(n) based on Lempel–Ziv complexity algorithm were calculated. Utility of the indices was compared based on the grey rational grade of sEMG indices and power output and cadence. The results suggested that MNF derived from wavelet packet transformation was significantly higher than other EMG indices, indicating the potential application for fatigue evaluation induced by all-out cycling exercise.

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.

Support vector machine (SVM) classification model based rational design of novel tetronic acid derivatives as potent insecticidal and acaricidal agents

RSC Advances ◽  
2015 ◽  
Vol 5 (61) ◽  
pp. 49195-49203 ◽  
Author(s):  
Ting-Ting Yao ◽  
Jing-Li Cheng ◽  
Bing-Rong Xu ◽  
Min-Zhe Zhang ◽  
Yong-Zhou Hu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Rational Design ◽  
Classification Model ◽  
Support Vector ◽  
Svm Classification ◽  
Tetronic Acid ◽  
Model Based

A novel SVM classification model was constructed and applied in the development of novel tetronic acid derivatives as potent insecticidal and acaricidal agents.

scholarly journals Data Driven Leakage Detection and Classification of a Boiler Tube

2019 ◽  
Vol 9 (12) ◽  
pp. 2450 ◽  
Author(s):  
Muhammad Sohaib ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Wavelet Packet ◽  
Thermal Power ◽  
Leak Detection ◽  
Classification Model ◽  
Support Vector ◽  
Boiler Tube ◽  
Proposed Model ◽  
Ae Signals

Boiler heat exchange in thermal power plants involves tubes to transfer heat from the fuel to the water. Boiler tube leakage can cause outages and huge power generation loss. Therefore, early detection of leaks in boiler tubes is necessary to avoid such accidents. In this study, a boiler tube leak detection and classification mechanism was designed using wavelet packet transform (WPT) analysis of the acoustic emission (AE) signals acquired from the boiler tube and a fully connected deep neural network (FC-DNN). WPT analysis of the AE signals enabled the extraction of features associated with the different conditions of the boiler tube, that is, normal and leak conditions. The deep neural network (DNN) effectively explores the salient information from the wavelet packet features through a deep architecture instead of considering shallow networks, such as k-nearest neighbors (k-NN) and support vector machines (SVM). This enhances the classification performance of the leak identification and classification model developed. The proposed model yielded a 99.2 % average classification accuracy when tested with AE signals from the boiler tube. The experimental results prove the efficacy of the proposed model for boiler tube leak detection and classification.

Rolling bearing fault detection approach based on improved dispersion entropy and AFSA optimized SVM

2020 ◽  
pp. 002072092094058
Author(s):  
Wuqiang Liu ◽  
Jinxing Shen ◽  
Xiaoqiang Yang
Keyword(s):  
Fault Detection ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Classification Model ◽  
Coarse Grained ◽  
Fault Classification ◽  
Support Vector ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

The support vector machine (SVM) does not have a fixed parameter selection method and the manual selection of parameters is difficult to determine the validity, which affects the accuracy of recognition. simultaneously, The existing coarse-grained approach cannot effectively analyze the high-frequency components of time series. In view of the shortcomings of the above method, we put forward a new technique of rolling bearings for fault detection, which combines wavelet packet dispersion entropy (WPDE) and artificial fish swarm algorithm (AFSA) optimize support vector machines (AFSA-SVM). First of all, wavelet packet is devoted to decompose the original vibration signal into components of different frequency bands. Secondly, the dispersion entropy (DE) are calculated for each of the obtained frequency band components to acquire more comprehensive and complete fault information. Afterward, Input feature samples into the SVM model for training, and AFSA is used to optimize the parameters of SVM to obtain the optimal value so as to establish the best classification model. Finally, the prepared test set is input into AFSA-SVM for fault classification. The achievement of bearing detection experiments show that this approach can accurately and quickly identify fault types.

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