A Review of Machine Learning Network in Human Motion Biomechanics

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Wan Shi Low ◽  
Chow Khuen Chan ◽  
Joon Huang Chuah ◽  
Yee Kai Tee ◽  
Yan Chai Hum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Motion ◽  
Learning Network

scholarly journals A Systems Biology Approach to Identify Essential Epigenetic Regulators for Specific Biological Processes in Plants

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 364
Author(s):  
Rachel M. McCoy ◽  
Russell Julian ◽  
Shoban R. V. Kumar ◽  
Rajeev Ranjan ◽  
Kranthi Varala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Inference ◽  
Root Apical Meristem ◽  
Environmental Cues ◽  
Biological Processes ◽  
Learning Network ◽  
Meristem Development ◽  
Epigenetic Regulator ◽  
Gene Regulatory ◽  
Epigenetic Regulators

Upon sensing developmental or environmental cues, epigenetic regulators transform the chromatin landscape of a network of genes to modulate their expression and dictate adequate cellular and organismal responses. Knowledge of the specific biological processes and genomic loci controlled by each epigenetic regulator will greatly advance our understanding of epigenetic regulation in plants. To facilitate hypothesis generation and testing in this domain, we present EpiNet, an extensive gene regulatory network (GRN) featuring epigenetic regulators. EpiNet was enabled by (i) curated knowledge of epigenetic regulators involved in DNA methylation, histone modification, chromatin remodeling, and siRNA pathways; and (ii) a machine-learning network inference approach powered by a wealth of public transcriptome datasets. We applied GENIE3, a machine-learning network inference approach, to mine public Arabidopsis transcriptomes and construct tissue-specific GRNs with both epigenetic regulators and transcription factors as predictors. The resultant GRNs, named EpiNet, can now be intersected with individual transcriptomic studies on biological processes of interest to identify the most influential epigenetic regulators, as well as predicted gene targets of the epigenetic regulators. We demonstrate the validity of this approach using case studies of shoot and root apical meristem development.

scholarly journals Case Study of Deep Learning Model of Temperature-Induced Deflection of a Cable-Stayed Bridge Driven by Data Knowledge

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2293
Author(s):  
Zixiang Yue ◽  
Youliang Ding ◽  
Hanwei Zhao ◽  
Zhiwen Wang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Prior Knowledge ◽  
Learning Model ◽  
Bridge Management ◽  
Learning Network ◽  
Cable Stayed Bridge ◽  
Main Girder ◽  
Deep Learning Model

A cable-stayed bridge is a typical symmetrical structure, and symmetry affects the deformation characteristics of such bridges. The main girder of a cable-stayed bridge will produce obvious deflection under the inducement of temperature. The regression model of temperature-induced deflection is hoped to provide a comparison value for bridge evaluation. Based on the temperature and deflection data obtained by the health monitoring system of a bridge, establishing the correlation model between temperature and temperature-induced deflection is meaningful. It is difficult to complete a high-quality model only by the girder temperature. The temperature features based on prior knowledge from the mechanical mechanism are used as the input information in this paper. At the same time, to strengthen the nonlinear ability of the model, this paper selects an independent recurrent neural network (IndRNN) for modeling. The deep learning neural network is compared with machine learning neural networks to prove the advancement of deep learning. When only the average temperature of the main girder is input, the calculation accuracy is not high regardless of whether the deep learning network or the machine learning network is used. When the temperature information extracted by the prior knowledge is input, the average error of IndRNN model is only 2.53%, less than those of BPNN model and traditional RNN. Combining knowledge with deep learning is undoubtedly the best modeling scheme. The deep learning model can provide a comparison value of bridge deformation for bridge management.

scholarly journals Recognition Technology of Athlete’s Limb Movement Combined Based on the Integrated Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fei Tan ◽  
Xiaoqing Xie
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithm ◽  
Human Motion ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Recording Device ◽  
Table Tennis ◽  
Movement Recognition ◽  
Random Forest Tree

Human motion recognition based on inertial sensor is a new research direction in the field of pattern recognition. It carries out preprocessing, feature selection, and feature selection by placing inertial sensors on the surface of the human body. Finally, it mainly classifies and recognizes the extracted features of human action. There are many kinds of swing movements in table tennis. Accurately identifying these movement modes is of great significance for swing movement analysis. With the development of artificial intelligence technology, human movement recognition has made many breakthroughs in recent years, from machine learning to deep learning, from wearable sensors to visual sensors. However, there is not much work on movement recognition for table tennis, and the methods are still mainly integrated into the traditional field of machine learning. Therefore, this paper uses an acceleration sensor as a motion recording device for a table tennis disc and explores the three-axis acceleration data of four common swing motions. Traditional machine learning algorithms (decision tree, random forest tree, and support vector) are used to classify the swing motion, and a classification algorithm based on the idea of integration is designed. Experimental results show that the ensemble learning algorithm developed in this paper is better than the traditional machine learning algorithm, and the average recognition accuracy is 91%.

scholarly journals Human Motion Recognition by Textile Sensor Based on Machine Learning Algorithms

2018 ◽  
Author(s):  
Chi Cuong Vu ◽  
Jooyong Kim
Keyword(s):  
Machine Learning ◽  
Wearable Sensors ◽  
Learning Algorithms ◽  
Human Motion ◽  
Machine Learning Algorithms ◽  
Identification Rate ◽  
Human Motion Recognition ◽  
Textile Sensor ◽  
Wearable Products ◽  
Walled Carbon Nanotubes

Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research was only done in simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic applications. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable products without unnecessary heavy and uncomfortable electronic devices.

scholarly journals Automatic Modulation Classification Exploiting Hybrid Machine Learning Network

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Feng Wang ◽  
Shanshan Huang ◽  
Hao Wang ◽  
Chenlu Yang
Keyword(s):  
Machine Learning ◽  
Hot Spot ◽  
Support Vector ◽  
Modulation Classification ◽  
Electronic Warfare ◽  
Time Frequency ◽  
Automatic Modulation Classification ◽  
Learning Network ◽  
Hybrid Machine ◽  
Electromagnetic Signals

It is a research hot spot in cognitive electronic warfare systems to classify the electromagnetic signals of a radar or communication system according to their modulation characteristics. We construct a multilayer hybrid machine learning network for the classification of seven types of signals in different modulation. We extract the signal modulation features exploiting a set of algorithms such as time-frequency analysis, discrete Fourier transform, and instantaneous autocorrelation and accomplish automatic modulation classification using naive Bayesian and support vector machine in a hybrid manner. The parameters in the network for classification are determined automatically in the training process. The numerical simulation results indicate that the proposed network accomplishes the classification accurately.

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