The Research of 3D Human Motion Simulation and Video Analysis System Implemented in Sports Training

2014 ◽  
Vol 926-930 ◽  
pp. 2743-2746 ◽  
Author(s):  
Rui Min Hu ◽  
Zhen Dong He ◽  
Feng Bai
Keyword(s):  
Computer Vision ◽  
Video Analysis ◽  
Rapid Development ◽  
Human Movement ◽  
Research Direction ◽  
Human Motion ◽  
Motion Simulation ◽  
Sports Training ◽  
Camera Tracking ◽  
Human Motion Simulation

With the rapid development of computer technology, human motion tracking based on video is a kind of using ordinary camera tracking unmarked human movement technology. It has important application value in automatic monitoring, human-computer interaction, sports analysis and many other fields. This research is a hot research direction in the field of computer vision in recent years. Because of the complexity of the problem and the lack of understanding of the nature of the human visual tracking based on video is always a difficult problem in computer vision. The research content of this article is set in sports training, for motion analysis of non-contact, no interfere with measurement and simulation requirements, the use of computer graphics and computer vision technology, discussing 3D human motion simulation technology based on video analysis.

scholarly journals Critical Features in Human Motion Simulation for Ergonomic Analysis

2005 ◽  
Vol 49 (12) ◽  
pp. 1196-1199 ◽  
Author(s):  
Matthew P. Reed ◽  
Don B. Chaffin ◽  
Julian Faraway
Keyword(s):  
Human Movement ◽  
Human Motion ◽  
Motion Simulation ◽  
University Of Michigan ◽  
Physical Ergonomics ◽  
Human Motion Simulation ◽  
Human Figure ◽  
Ergonomic Analysis ◽  
The University ◽  
Digital Human

Digital human figure models (DHM) are increasingly the tools of choice for assessments of the physical ergonomics of products and workplaces. Software representations of users and workers are used to visualize people performing tasks of interest. Analyses have usually focused on clearance and reach in static postures, not because the actual tasks are static, but rather because DHM have lacked robust, accurate motion simulation capability. Research is underway at many institutions to develop improved motion simulation methods, drawing on a wide variety of methodologies from fields such as computer graphics, kinesiology, motor control, and robotics. Experience in the Human Motion Simulation Laboratory at the University of Michigan suggests that conventional metrics of accuracy for posture and movement prediction do not adequately capture the aspects of human movement that are most important for ergonomic analysis. This paper identifies and justifies a set of these critical features.

scholarly journals Research on Sports Training Action Recognition Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Wang
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Human Motion ◽  
Medical Monitoring ◽  
Human Beings ◽  
Sports Training ◽  
Motion Recognition ◽  
Human Motion Recognition

With the rapid development of science and technology in today’s society, various industries are pursuing information digitization and intelligence, and pattern recognition and computer vision are also constantly carrying out technological innovation. Computer vision is to let computers, cameras, and other machines receive information like human beings, analyze and process their semantic information, and make coping strategies. As an important research direction in the field of computer vision, human motion recognition has new solutions with the gradual rise of deep learning. Human motion recognition technology has a high market value, and it has broad application prospects in the fields of intelligent monitoring, motion analysis, human-computer interaction, and medical monitoring. This paper mainly studies the recognition of sports training action based on deep learning algorithm. Experimental work has been carried out in order to show the validity of the proposed research.

Posture prediction and physics-based human motion simulation

2019 ◽  
pp. 425-440
Author(s):  
Rajan Bhatt ◽  
Kimberly Farrell ◽  
Karim Abdel-Malek ◽  
Jasbir Arora ◽  
Chris Murphy
Keyword(s):  
Human Motion ◽  
Motion Simulation ◽  
Human Motion Simulation ◽  
Posture Prediction

Real time evaluation algorithm of human motion in tennis training robot

2020 ◽  
pp. 1-9
Author(s):  
Yingying Wang ◽  
Yongzhi Zhang
Keyword(s):  
Real Time ◽  
Rapid Development ◽  
Human Movement ◽  
Human Motion ◽  
Sports Activity ◽  
Training Requirements ◽  
Core Theory ◽  
Evaluation Algorithm ◽  
Stability And Accuracy ◽  
Time Evaluation

Tennis is a set of sports and entertainment and a sports activity, since 2014, tennis in China has been another rapid development. With the development of economy and technology, tennis training mode has been further optimized and reformed. At present, tennis training robot is the mainstream way to train athletes. However, there are some defects in the current tennis training robots, such as the low accuracy of human motion real-time evaluation, and the lack of stability. Therefore, this paper puts forward the related research on the real-time evaluation algorithm of human motion in tennis training robots, hoping to make up for the deficiency in this field. The research of this paper is mainly divided into four parts. The first part is to analyze the current situation of technology research in this field and put forward the idea of this paper by analyzing the shortcomings of the existing technology. The second part is the related basic theory research; this part deeply studies the core theory of tennis training and intelligent training robot, which provides a theoretical basis for the realization of the optimization scheme. The third part is the design and implementation of a real-time human motion evaluation optimization algorithm for tennis training robots. At the end of the paper, that is, the fourth part, through the way of field test and investigation, further proves the superiority of the improved real-time evaluation algorithm of human movement. The algorithm has good stability and accuracy and can meet the existing tennis training requirements.

scholarly journals Human Motion Gesture Recognition Based on Computer Vision

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Ma ◽  
Zhendong Zhang ◽  
Enqing Chen
Keyword(s):  
Computer Vision ◽  
Gesture Recognition ◽  
Human Body ◽  
Spatial Relationship ◽  
Research Direction ◽  
Human Motion ◽  
The Body ◽  
Body Parts ◽  
Human Body Model ◽  
Relationship Of

Human motion gesture recognition is the most challenging research direction in the field of computer vision, and it is widely used in human-computer interaction, intelligent monitoring, virtual reality, human behaviour analysis, and other fields. This paper proposes a new type of deep convolutional generation confrontation network to recognize human motion pose. This method uses a deep convolutional stacked hourglass network to accurately extract the location of key joint points on the image. The generation and identification part of the network is designed to encode the first hierarchy (parent) and the second hierarchy (child) and show the spatial relationship of human body parts. The generator and the discriminator are designed as two parts in the network, and they are connected together in order to encode the possible relationship of appearance and, at the same time, the possibility of the existence of human body parts and the relationship between each part of the body and its parental part coding. In the image, the key nodes of the human body model and the general body posture can be identified more accurately. The method has been tested on different data sets. In most cases, the results obtained by the proposed method are better than those of other comparison methods.

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