scholarly journals Analysis and Recognition of Human Lower Limb Motions Based on Electromyography (EMG) Signals

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2473
Author(s):  
Junyao Wang ◽  
Yuehong Dai ◽  
Xiaxi Si
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Lower Limb ◽  
Recognition Rate ◽  
Measurement Unit ◽  
Lower Limb Muscles ◽  
Average Recognition Rate ◽  
Standing Up ◽  
Length Data ◽  
Hidden Layer

Background: This paper focuses on the characteristics of lower limb EMG signals for common movements. Methods: We obtained length data for lower limb muscles during gait motion using software named OpenSim; statistical product and service solutions (SPSS) were utilized to study the correlation between each muscle, based on gait data. Low-correlation muscles in different regions were selected; inertial measurement unit (IMU) and EMG sensors were used to measure the lower limb angles and EMG signals when on seven kinds of slope, in five kinds of gait (walking on flat ground, uphill, downhill, up-step and down-step) and four kinds of movement (squat, lunge, raised leg and standing up). Results: After data denoising and feature extraction, we designed a double hidden-layer BP neural network to recognize the above motions according to EMG signals. Results show that EMG signals of selected muscles have a certain periodicity in the process of movement that can be used to identify lower limb movements. Conclusions: It can be seen, after the recognition of different proportions of training and testing sets that the average recognition rate of the BP neural network is 86.49% for seven gradients, 93.76% for five kinds of gait and 86.07% for four kinds of movements.

Selective Facial Expression Recognition Using fastICA

2012 ◽  
Vol 433-440 ◽  
pp. 2755-2761 ◽  
Author(s):  
Xiao Hua Zhang ◽  
Zhi Fei Liu ◽  
Ya Jun Guo ◽  
Li Qiang Zhao
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Bp Neural Network ◽  
Facial Expression Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Facial Features ◽  
Expression Recognition ◽  
Average Recognition Rate ◽  
Neural Network Classifiers

This paper proposes a facial expression recognition approach based on the combination of fastICA method and neural network classifiers. First we get some special facial expression regions, including eyebrows, eyes and mouth, in which wavelet transform is done to reduce the dimension. Then the fastICA method is used to extract these three facial features. Finally, BP neural network classifier is adopted to recognize facial expression. Experimental on the JAFFE database results show that the method is effective for both dimension reduction and recognition performance in comparison with traditional PCA and ICA method. We have obtained recognition rates as high as 93.33% in categorizing the facial expressions neutral, anger, or sadness. The best average recognition rate achieves 90.48%.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

Machine-Printed Invoice Number Based on Fuzzy Recognition

2012 ◽  
Vol 214 ◽  
pp. 705-710 ◽  
Author(s):  
Xiao Ping Xian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Real System ◽  
Recognition Method ◽  
Recognition Time ◽  
Recognition Ability ◽  
System Requirements ◽  
Fuzzy Recognition ◽  
Detection And Separation

A new fuzzy recognition method of machine-printed invoice number based on neural network is presented. This method includes ten links: invoice number detection and separation of right on top of invoice, binarization, denoising, incline correction, extraction of invoice code numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. Through testing, the recognition rate of this method can be over 99%.The recognition time of characters for character is less than 1 second, which means that the method is of more effective recognition ability and can better satisfy the real system requirements.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

Precision Model of Predicting FDM Rapid Prototype Based on BP Neural Network

2008 ◽  
Vol 392-394 ◽  
pp. 891-897
Author(s):  
G.Q. Shang ◽  
C.H. Sun ◽  
X.F. Chen ◽  
J.H. Du
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Fused Deposition Modeling ◽  
Rapid Prototype ◽  
Fused Deposition ◽  
Novel Approach ◽  
Deposition Modeling ◽  
Hidden Layer ◽  
Parts Manufacturing ◽  
Bp Neural Network Model

Fused deposition modeling (FDM) has been widely applied in complex parts manufacturing and rapid tooling and so on. The precision of prototype was affected by many factors during FDM, so it is difficult to depict the process using a precise mathematical model. A novel approach for establishing a BP neural network model to predict FDM prototype precision was proposed in this paper. Firstly, based on analyzing effect of each factor on prototyping precision, some key parameters were confirmed to be feature parameters of BP neural networks. Then, the dimensional numbers of input layer and middle hidden layer were confirmed according to practical conditions, and therefore the model structure was fixed. Finally, the structure was trained by a great lot of experimental data, a model of BP neural network to predict precision of FDM prototype was constituted. The results show that the error can be controlled within 10%, which possesses excellent capability of predicting precision.

Prediction Model of Flow-Induced Noise in Large-Scale Centrifugal Pumps Based on BP Neural Network

2017 ◽  
Author(s):  
Chang Guo ◽  
Ming Gao ◽  
Peixin Dong ◽  
Yuetao Shi ◽  
Fengzhong Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Centrifugal Pumps ◽  
Output Layer ◽  
Sample Data ◽  
Input Layer ◽  
Flow Induced Noise ◽  
Hidden Layer

As one kind of serious environmental problems, flow-induced noise in centrifugal pumps pollutes the working circumstance and deteriorates the performance of pumps, meanwhile, it always changes drastically under various working conditions. Consequently, it is extremely significant to predict flow-induced noise of centrifugal pumps under various working conditions with a practical mathematical model. In this paper, a three-layer back propagation (BP) neural network model is established and the number of input, hidden and output layer node is set as 3, 6 and 1, respectively. To be specific, the flow rate, rotational speed and medium temperature are chosen as input layer, and the corresponding flow-induced noise evaluated by average of total sound pressure level (A_TSPL) as output layer. Furthermore, the tansig function is used to act as transfer function between the input layer and hidden layer, and the purelin function is used between hidden layer and output layer. The trainlm function based on Levenberg-Marquardt algorithm is selected as the training function. By using a large number of sample data, the training of the network model and prediction research are accomplished. The results indicate that good correlation is established among the sample data, and the predictive values show great consistence with simulation ones, of which the average relative error of A_TSPL in process of verification is 0.52%. The precision of the model can satisfy the requirement of relevant research and engineering application.

The Recognition of Handwritten Digital Based on BP Neural Network

2013 ◽  
Vol 416-417 ◽  
pp. 1239-1243
Author(s):  
Shan Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Practical Significance ◽  
Test Results ◽  
Recognition Method ◽  
Number Recognition ◽  
Recognition Ability ◽  
Fuzzy Recognition ◽  
Zip Code

The article put forward to new recognition method of handwritten digital based on BP neural network. Its recognition process mainly includes ten aspect: incline correction of handwritten number, edge detection and separation of a set number, binarization, denoising, extraction of numerals, window scaling, location standardization, thinning, extraction of numeral feature and fuzzy recognition based on BP neural network. The test results show that the recognition rate of this method can be over 92 percent. The recognition time of characters for character is less than 1.1 second, which means that the method is more effective recognition ability and can better satisfy the real system requirements.It should be widely applied practical significance for Book Number Recognition, zip code recognition sorting.

scholarly journals Development of Customer Relationship Management System in E-Commerce Based on BP Neural Network

2012 ◽  
Vol 6-7 ◽  
pp. 995-999
Author(s):  
Mei Ling Zhou ◽  
Jing Jing Hao
Keyword(s):  
Neural Network ◽  
Customer Relationship Management ◽  
Bp Neural Network ◽  
Management System ◽  
Customer Value ◽  
Relationship Management ◽  
Customer Relationship ◽  
Feed Forward Network ◽  
Neural Network Algorithm ◽  
Hidden Layer

BP neural network can learn and store a lot of input - output mode mapping, without prior reveal the mathematical equations describe the mapping. The model based on BP neural network algorithm is constituted by an input layer, output layer and one hidden layer, three-layer feed forward network. CRM is to acquire, maintain and increase the methods and processes of profitable customers. The core of CRM is the customer value management, customer value; it is divided into the de facto value, potential value and model value. The paper presents development of customer relationship management system in e-commerce based on BP neural network. The experiment shows BP is superior to RFCA in CRM.

The Use of BP Neural Network in Evaluation about the Appearance of the Garments

2012 ◽  
Vol 433-440 ◽  
pp. 4320-4323 ◽  
Author(s):  
Jing Wang ◽  
Jin Ying Song ◽  
Ai Qing Tang
Keyword(s):  
Neural Network ◽  
Principal Components ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Original Data ◽  
Back Propagation Algorithm ◽  
Bp Network ◽  
Self Adaptation ◽  
Hidden Layer ◽  
Good Agreement

This article reports the use of BP neural network for evaluation of the appearance of garment after dry wash. The selected data about parameters of fabrics and interlinings are analyzed by principal analysis and eight principal components are obtained through this method. A BP neural network with a single hidden layer is constructed including eight input nodes, six hidden nodes and one output nodes. During training the network with a back-propagation algorithm, the eight principal components are used as input parameters, while the rate of the appearance of the garment are used as output parameters. The weight values are modified with momentum and learning rate self-adaptation to solve the two defects of the BP network. All original data are preprocessed and the learning process is successful in achieving a global error minimum. The rate of the appearance can be evaluated with this training network and there is a good agreement between the evaluated and tested values.

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