Journal ArticleDOI
Research on athlete skipping surface electromyography and energy consumption based on principal component analysis of wavelet packet
TLDR
The research shows that the wavelet packet principal component analysis model performance is significantly better than the traditional algorithm, and the recognition rate for some subtle motions is also high.Abstract:
EMG signal acquisition is mostly used in medical research. However, it has not been applied in athletes’ sports state recognition and body state detection, and there are few related studies at present. In order to promote the application of EMG signal acquisition in sports, this study combined with the actual needs of athletes to construct an EMG signal acquisition system that can collect athletes’ motion status. At the same time, in order to improve the effect of EMG signal acquisition, a wavelet packet principal component analysis model is proposed. In addition, in order to ensure the recognition efficiency of athletes’ motion state, this paper uses linear discriminant analysis method as the motion recognition assistant algorithm. Finally, this paper judges the performance of this research model by setting up comparative experiments. The research shows that the wavelet packet principal component analysis model performance is significantly better than the traditional algorithm, and the recognition rate for some subtle motions is also high. In addition, this study provides a theoretical reference for the application of EMG signals in the sports industry.read more
Citations
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Journal ArticleDOI
Effects of Motion Vision and Neural Efficiency On Target Capture in Basketball Players
TL;DR: In this paper , 20 basketball players and 20 non-athletes received a motion vision test and a neurological efficiency test and the experimental stimulus was to determine whether there was a ball on the picture.
Journal ArticleDOI
Analysis and System Construction of ALSTM-LSTM Model-based Sports Jumping Rope Movement
TL;DR: In this article , an ALSTM-LSTM model based on visual human posture estimation is proposed for motion system analysis, which can improve the accuracy of the analysis of the jump rope sport's posture based on the characteristics of human movement, and inspire new technical tools for teaching instruction.
References
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Journal ArticleDOI
Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system
Jasmin Kevric,Abdulhamit Subasi +1 more
TL;DR: Results indicate that the proposed model has the potential to obtain a reliable classification of motor imagery EEG signals, and can thus be used as a practical system for controlling a wheelchair.
Journal ArticleDOI
WPD-PCA-Based Laser Welding Process Monitoring and Defects Diagnosis by Using FNN and SVM
TL;DR: The feedforward neural network prediction model and the support vector machine classification model built in this research help to guarantee accurate estimation on welding status and effective identification of welded defect.
Journal ArticleDOI
The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform
TL;DR: In this paper, the effect of feature extraction and classification that caused by the kernel function and the different options of its parameters is discussed, and the effects of reducing dimension analysis and kernel principal component analysis are compared.
Journal ArticleDOI
Real-time fault diagnosis for gas turbine generator systems using extreme learning machine
TL;DR: Experimental results show that the proposed diagnostic framework can detect component faults much faster than SVM, while ELM is competitive with SVM in accuracy.
Journal ArticleDOI
Identification of Green, Oolong and Black Teas in China via Wavelet Packet Entropy and Fuzzy Support Vector Machine
TL;DR: A computer-vision and machine-learning based system, which did not require expensive signal acquiring devices and time-consuming procedures, and is effective for tea identification.