scholarly journals Application of human motion recognition technology in extreme learning machine

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098321
Author(s):  
Anzhu Miao ◽  
Feiping Liu
Keyword(s):  
Extreme Learning Machine ◽  
Research Work ◽  
Human Motion ◽  
Feature Descriptor ◽  
Background Variable ◽  
Motion Recognition ◽  
Network Training ◽  
Other Information ◽  
Human Motion Recognition ◽  
Learning Machine

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.

scholarly journals Human motion recognition based on limit learning machine

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142093307
Author(s):  
Hong Chen ◽  
Hongdong Zhao ◽  
Baoqiang Qi ◽  
Shi Wang ◽  
Nan Shen ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Motion Capture ◽  
Recognition Rate ◽  
Human Motion ◽  
Motion Capture Data ◽  
Joint Position ◽  
Data Set ◽  
Motion Recognition ◽  
Human Motion Recognition ◽  
Learning Machine

With the development of technology, human motion capture data have been widely used in the fields of human–computer interaction, interactive entertainment, education, and medical treatment. As a problem in the field of computer vision, human motion recognition has become a key technology in somatosensory games, security protection, and multimedia information retrieval. Therefore, it is important to improve the recognition rate of human motion. Based on the above background, the purpose of this article is human motion recognition based on extreme learning machine. Based on the existing action feature descriptors, this article makes improvements to features and classifiers and performs experiments on the Microsoft model specific register (MSR)-Action3D data set and the Bonn University high density metal (HDM05) motion capture data set. Based on displacement covariance descriptor and direction histogram descriptor, this article described both combine to produce a new combination; the description can statically reflect the joint position relevant information and at the same time, the change information dynamically reflects the joint position, uses the extreme learning machine for classification, and gets better recognition result. The experimental results show that the combined descriptor and extreme learning machine recognition rate on these two data sets is significantly improved by about 3% compared with the existing methods.

A Projection-based Human Motion Recognition Algorithm based on Depth Sensors

2021 ◽  
pp. 1-1
Author(s):  
Mu-Chun Su ◽  
Pang-Ti Tai ◽  
Jieh-Haur Chen ◽  
Yi-Zeng Hsieh ◽  
Shu-Fang Lee ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Human Motion ◽  
Motion Recognition ◽  
Depth Sensors ◽  
Human Motion Recognition

scholarly journals Application of Extreme Learning Machine in GPS Positioning Process

2018 ◽  
Vol 176 ◽  
pp. 01034
Author(s):  
Chengxin Li ◽  
Jing Peng ◽  
Lv Zhicheng ◽  
Mengli Wang ◽  
Gang Ou
Keyword(s):  
Extreme Learning Machine ◽  
Experimental Simulation ◽  
Data Generation ◽  
Simulation Experiments ◽  
Simulation Data ◽  
Gps Positioning ◽  
Network Training ◽  
Generation Network ◽  
Network Prediction ◽  
Learning Machine

In the positioning process of GPS, the linear least squares algorithm and Kalman filtering algorithm are widely used but still have shortcomings. Application of extreme learning machine in this area is proposed in this paper, which breaks through the limitations of the traditional method of positioning based on mathematical models. Two simulation experiments of ELM in GPS positioning process are presented in this paper while the latter is a supplement to the former. Each one contains three phases, including simulation data generation, network training and network prediction, each of which is considered carefully. The feasibility of extreme learning machine is verified through experimental simulation. A more accurate positioning result can be obtained.

scholarly journals A microarray gene expressions with classification using extreme learning machine

Genetika ◽  
2015 ◽  
Vol 47 (2) ◽  
pp. 523-534
Author(s):  
M. Yasodha ◽  
P. Ponmuthuramalingam
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Dna Microarrays ◽  
Research Work ◽  
The Body ◽  
Cancer Classification ◽  
Medical Sciences ◽  
Gene Expressions ◽  
Learning Machine ◽  
Elm Classifier

In the present scenario, one of the dangerous disease is cancer. It spreads through blood or lymph to other location of the body, it is a set of cells display uncontrolled growth, attack and destroy nearby tissues, and occasionally metastasis. In cancer diagnosis and molecular biology, a utilized effective tool is DNA microarrays. The dominance of this technique is recognized, so several open doubt arise regarding proper examination of microarray data. In the field of medical sciences, multicategory cancer classification plays very important role. The need for cancer classification has become essential because the number of cancer sufferers is increasing. In this research work, to overcome problems of multicategory cancer classification an improved Extreme Learning Machine (ELM) classifier is used. It rectify problems faced by iterative learning methods such as local minima, improper learning rate and over fitting and the training completes with high speed.

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