Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks

In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.

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Neural network-based models of binomial time series in data analysis problems

Doklady of the National Academy of Sciences of Belarus ◽

10.29235/1561-8323-2021-65-6-654-660 ◽

2021 ◽

Vol 65 (6) ◽

pp. 654-660

Author(s):

Yu. S. Kharin

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Pattern Recognition ◽

Data Analysis ◽

Equivalence Relation ◽

Model Parameters ◽

Computer Data ◽

Arbitrary Type ◽

New Family

This article is devoted to constructing neural network-based models for discrete-valued time series and their use in computer data analysis. A new family of binomial time series based on neural networks is presented, which makes it possible to approximate the arbitrary-type stochastic dependence in time series. Ergodicity conditions and an equivalence relation for these models are determined. Consistent statistical estimators for model parameters and algorithms for computer data analysis (including forecasting and pattern recognition) are developed.

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Pattern Recognition by Hierarchical Feature Extraction

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2003.p0278 ◽

2003 ◽

Vol 15 (3) ◽

pp. 278-285

Author(s):

Daigo Misaki ◽

◽

Shigeru Aomura ◽

Noriyuki Aoyama

Keyword(s):

Neural Network ◽

Neural Networks ◽

Pattern Recognition ◽

Feature Extraction ◽

Input Data ◽

Local Features ◽

Detailed Classification

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called ""landmark""s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

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Artificial Neural Network in Drug Delivery and Pharmaceutical Research

The Open Bioinformatics Journal ◽

10.2174/1875036201307010049 ◽

2013 ◽

Vol 7 (1) ◽

pp. 49-62 ◽

Cited By ~ 23

Author(s):

Vijaykumar Sutariya ◽

Anastasia Groshev ◽

Prabodh Sadana ◽

Deepak Bhatia ◽

Yashwant Pathak

Keyword(s):

Neural Network ◽

Neural Networks ◽

Drug Delivery ◽

Pattern Recognition ◽

Analytical Data ◽

Pharmaceutical Research ◽

Artificial Neural ◽

The Brain

Artificial neural networks (ANNs) technology models the pattern recognition capabilities of the neural networks of the brain. Similarly to a single neuron in the brain, artificial neuron unit receives inputs from many external sources, processes them, and makes decisions. Interestingly, ANN simulates the biological nervous system and draws on analogues of adaptive biological neurons. ANNs do not require rigidly structured experimental designs and can map functions using historical or incomplete data, which makes them a powerful tool for simulation of various non-linear systems.ANNs have many applications in various fields, including engineering, psychology, medicinal chemistry and pharmaceutical research. Because of their capacity for making predictions, pattern recognition, and modeling, ANNs have been very useful in many aspects of pharmaceutical research including modeling of the brain neural network, analytical data analysis, drug modeling, protein structure and function, dosage optimization and manufacturing, pharmacokinetics and pharmacodynamics modeling, and in vitro in vivo correlations. This review discusses the applications of ANNs in drug delivery and pharmacological research.

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Convolutional Neural Network for Iris Recognition

10.21203/rs.3.rs-244624/v1 ◽

2021 ◽

Author(s):

Wael Alnahari

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Iris Recognition ◽

Recognition System ◽

High Accuracy ◽

Accuracy Rate

Abstract In this paper, I proposed an iris recognition system by using deep learning via neural networks (CNN). Although CNN is used for machine learning, the recognition is achieved by building a non-trained CNN network with multiple layers. The main objective of the code the test pictures’ category (aka person name) with a high accuracy rate after having extracted enough features from training pictures of the same category which are obtained from a that I added to the code. I used IITD iris which included 10 iris pictures for 223 people.

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Ultra High Frequency Sigmoid and Trigonometric Higher Order Neural Networks for Data Pattern Recognition

Advances in Computational Intelligence and Robotics - Applied Artificial Higher Order Neural Networks for Control and Recognition ◽

10.4018/978-1-5225-0063-6.ch004 ◽

2016 ◽

pp. 80-112 ◽

Cited By ~ 1

Author(s):

Ming Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Pattern Recognition ◽

Nonlinear Model ◽

High Frequency ◽

Higher Order ◽

Sine Function ◽

Ultra High Frequency ◽

Higher Order Neural Networks ◽

Cosine Functions

This chapter develops a new nonlinear model, Ultra high frequency siGmoid and Trigonometric Higher Order Neural Networks (UGT-HONN), for data pattern recognition. UGT-HONN includes Ultra high frequency siGmoid and Sine function Higher Order Neural Networks (UGS-HONN) and Ultra high frequency siGmoid and Cosine functions Higher Order Neural Networks (UGC-HONN). UGS-HONN and UGC-HONN models are used to recognition data patterns. Results show that UGS-HONN and UGC-HONN models are better than other Polynomial Higher Order Neural Network (PHONN) and Trigonometric Higher Order Neural Network (THONN) models, since UGS-HONN and UGC-HONN models to recognize data pattern with error approaching 0.0000%.

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Ultra High Frequency Sigmoid and Trigonometric Higher Order Neural Networks for Data Pattern Recognition

Nature-Inspired Computing ◽

10.4018/978-1-5225-0788-8.ch027 ◽

2016 ◽

pp. 682-715

Author(s):

Ming Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Pattern Recognition ◽

Nonlinear Model ◽

High Frequency ◽

Higher Order ◽

Sine Function ◽

Ultra High Frequency ◽

Higher Order Neural Networks ◽

Cosine Functions

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Generalized Born radii computation using linear models and neural networks

Bioinformatics ◽

10.1093/bioinformatics/btz818 ◽

2019 ◽

Vol 36 (6) ◽

pp. 1757-1764

Author(s):

Saida Saad Mohamed Mahmoud ◽

Gennaro Esposito ◽

Giuseppe Serra ◽

Federico Fogolari

Keyword(s):

Neural Network ◽

Neural Networks ◽

Linear Model ◽

Correlation Coefficient ◽

Linear Models ◽

Reference Method ◽

Supplementary Information ◽

Model Parameters ◽

Generalized Born ◽

Supplementary Material

Abstract Motivation Implicit solvent models play an important role in describing the thermodynamics and the dynamics of biomolecular systems. Key to an efficient use of these models is the computation of generalized Born (GB) radii, which is accomplished by algorithms based on the electrostatics of inhomogeneous dielectric media. The speed and accuracy of such computations are still an issue especially for their intensive use in classical molecular dynamics. Here, we propose an alternative approach that encodes the physics of the phenomena and the chemical structure of the molecules in model parameters which are learned from examples. Results GB radii have been computed using (i) a linear model and (ii) a neural network. The input is the element, the histogram of counts of neighbouring atoms, divided by atom element, within 16 Å. Linear models are ca. 8 times faster than the most widely used reference method and the accuracy is higher with correlation coefficient with the inverse of ‘perfect’ GB radii of 0.94 versus 0.80 of the reference method. Neural networks further improve the accuracy of the predictions with correlation coefficient with ‘perfect’ GB radii of 0.97 and ca. 20% smaller root mean square error. Availability and implementation We provide a C program implementing the computation using the linear model, including the coefficients appropriate for the set of Bondi radii, as Supplementary Material. We also provide a Python implementation of the neural network model with parameter and example files in the Supplementary Material as well. Supplementary information Supplementary data are available at Bioinformatics online.

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Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion

Sensors ◽

10.3390/s20020537 ◽

2020 ◽

Vol 20 (2) ◽

pp. 537 ◽

Cited By ~ 1

Author(s):

Jiyuan Song ◽

Aibin Zhu ◽

Yao Tu ◽

Yingxu Wang ◽

Muhammad Affan Arif ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Lower Extremity ◽

Human Body ◽

Recognition Accuracy ◽

Screening Method ◽

Motion Pattern ◽

Recognition Method ◽

Motion State ◽

Motion Mode

Aiming at the requirement of rapid recognition of the wearer’s gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.

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A Probabilistic Neural Network-Based Module for Recognition of Objects from their 3-D Images

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2013040105 ◽

2013 ◽

Vol 2 (2) ◽

pp. 66-79 ◽

Cited By ~ 2

Author(s):

Onsy A. Abdel Alim ◽

Amin Shoukry ◽

Neamat A. Elboughdadly ◽

Gehan Abouelseoud

Keyword(s):

Neural Network ◽

Neural Networks ◽

Decision Making ◽

Pattern Recognition ◽

Object Recognition ◽

Probabilistic Neural Network ◽

Training Procedure ◽

Generalization Capability ◽

Mine Detection ◽

Recognition Of Objects

In this paper, a pattern recognition module that makes use of 3-D images of objects is presented. The proposed module takes advantage of both the generalization capability of neural networks and the possibility of manipulating 3-D images to generate views at different poses of the object that is to be recognized. This allows the construction of a robust 3-D object recognition module that can find use in various applications including military, biomedical and mine detection applications. The paper proposes an efficient training procedure and decision making strategy for the suggested neural network. Sample results of testing the module on 3-D images of several objects are also included along with an insightful discussion of the implications of the results.

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