Research on influence mechanism of running clothing fatigue based on BP neural network

2020 ◽  
pp. 1-11
Author(s):  
Weiran Chen ◽  
Xiuhong Li ◽  
Xiaoran Chen ◽  
Yan Xiong
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Experimental Analysis ◽  
Measurement Method ◽  
Fatigue Threshold ◽  
Threshold Analysis ◽  
Influence Mechanism ◽  
Threshold Distribution ◽  
Fatigue Measurement ◽  
Clothing Pressure

Dress fatigue can affect the efficiency of sports, especially for running, the dress fatigue has a greater impact on it. Moreover, at present, there are few studies on dress fatigue. Based on this, this study is based on BP neural network, and uses surface electromyography theory and muscle fatigue measurement method to perform fatigue measurement. The fatigue threshold analysis is mainly carried out by the experimental method, and the prediction model of the wearing fatigue threshold based on BP neural network is constructed based on the actual demand. Moreover, this paper verifies the reliability of threshold distribution by experimental analysis combined with model analysis. In addition, the study sets the organizational structure and clothing pressure as verification indicators to analyze the performance of the model. The research results show that the model constructed in this study can effectively analyze the mechanism of fatigue impact of running dress, and this paper can provide reference for the study of dress fatigue.

scholarly journals Influence mechanism of running sportswear fatigue based on BP neural network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiaoli Liu ◽  
Zhibin Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Sports Medicine ◽  
Evaluation Method ◽  
Subjective Assessment ◽  
Skin Surface ◽  
Emg Signal ◽  
Starting Point ◽  
Clothing Pressure ◽  
Fatigue Evaluation

AbstractThe fatigue of running sportswear is reflected in the fatigue caused by the tightness of the tights on the skin surface of the limbs, trunk, and other parts during long-term running sports. However, the current research on the fatigue of running sportswear is not deep enough. Therefore, the purpose of this study is to study the mechanism of the fatigue of running sportswear based on BP neural network. This article first takes sportswear as the starting point and uses the surface myoelectricity index as a physiological quantity as a means to combine clothing with sports medicine and sports physiology, breaking the traditional shackles of subjective assessment of fatigue, and giving play to the advantages of interdisciplinary to expand the new direction of the apparel industry and, secondly, use muscle fatigue evaluation method to analyze the muscles of the lower leg under the pressure of sportswear to analyze the strength of the EMG signal and the number of participating sports units and the frequency of discharge synchronization. Experimental data shows that the AUC is 0.756 when wearing sports tights, the sensitivity and specificity are 72% and 19%, and the accuracy is 65%. The experimental results show that clothing pressure affects the fatigue of running sportswear based on BP neural network.

Establishing a Genetic Algorithm-Back Propagation model to predict the pressure of girdles and to determine the model function

2020 ◽  
Vol 90 (21-22) ◽  
pp. 2564-2578
Author(s):  
Zhou Jie ◽  
Ma Qiurui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Back Propagation ◽  
Optimal Functions ◽  
Clothing Pressure ◽  
Bp Neural Network Model

A Genetic Algorithm-Back Propagation (GA-BP) neural network method has been proposed to predict the clothing pressure of girdles in different postures. Firstly, a Back Propagation (BP) neural network model was used to predict the clothing pressure based on seven parameters, and three optimal functions of the model were derived. However, the prediction error 0.85411 of the network was more than the forecast requirement of 0.5 and the optimal initial weights and thresholds for the network could not be calculated. Therefore, a GA model and the BP neural network model were combined into a new GA-BP neural network model, which was used to predict the clothing pressure based on the three optimal functions. The results showed that the prediction error for this GA-BP neural network model was 0.41652, which was less than the forecast requirement of 0.5. Hence, the model was shown to predict the girdle pressure with acceptable accuracy. Finally, the internal calculation function equation for the GA-BP neural network was derived.

Continuous Blood Pressure Measurement Method Based on the Pulse Image Sensor and BP Neural Network

2014 ◽  
Vol 596 ◽  
pp. 476-479
Author(s):  
Ai Hua Zhang ◽  
Xing Zhong Zhou ◽  
Li Ming Yang ◽  
Rong Shen ◽  
Zhe Wei
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Internal Pressure ◽  
Bp Neural Network ◽  
Pressure Measurement ◽  
Measurement Method ◽  
Blood Pressure Measurement ◽  
Image Sensor ◽  
Image Sequences ◽  
Continuous Blood Pressure

A continuous pressure measurement method is proposed based the pulse image sensor and BP neural network for continuous measurement of arterial blood pressure. The multi-information synchronous acquisition system is built to collect pulse image sequences, sphygmopalpation pressure, probe internal pressure, and blood pressure of subjects. The feature vector is formed from pulse image sequences, sphygmopalpation pressure, and probe internal pressure to predict continuous blood pressure by BP neural network. The results show that the mean difference (MD) and standard deviation (SD) of systolic blood pressure (SBP) and diastolic blood pressure (DBP) meet the standard of Association for the Advancement of Medical Instrumentation (AAMI). The method could be used to predict continuous blood pressure and provides a new method for arterial continuous blood pressure measurement.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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