scholarly journals Construction and Simulation of Athlete’s Wrong Action Recognition Model in Sports Training Based on Embedded Wireless Communication and Computer Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Naichun Gao
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Action Recognition ◽  
Center Of Gravity ◽  
Knee Valgus ◽  
Sports Training ◽  
Recognition Method ◽  
Total Frequency ◽  
Emg Signal ◽  
Computer Vision Technology

Embedded networking has a broad prospect. Because of the Internet and the rapid development of PC skills, computer vision technology has a wide range of applications in many fields, especially the importance of identifying wrong movements in sports training. To study the computer vision technology to identify the wrong movement of athletes in sports training, in this paper, a hidden Markov model based on computer vision technology is constructed to collect video and identify the landing and take-off movements and badminton serving movements of a team of athletes under the condition of sports training, Bayesian classification algorithm to analyze the acquired sports training action data, obtain the error frequency, and the number of errors of the landing jump action, and the three characteristic data of the displacement, velocity, and acceleration of the body’s center of gravity of the athlete in the two cases of successful and incorrect badminton serve actions and compared and analyzed the accuracy of the action recognition method used in this article, the action recognition method based on deep learning and the action recognition method based on EMG signal under 30 experiments. The training process of deep learning is specifically split into two stages: 1st, a monolayer neuron is built layer by layer so that the network is trained one layer at a time; when all layers are fully trained, a tuning is performed using a wake-sleep operation. The final result shows that the frequency of the wrong actions of the athletes on the landing jump is concentrated in the knee valgus, the total frequency of error has reached 58%, and the frequency of personal error has reached 45%; the problem of the landing distance of the two feet of the team athletes also appeared more frequently, the total frequency reached 50%, and the personal frequency reached 30%. Therefore, athletes should pay more attention to the problems of knee valgus and the distance between feet when performing landing jumps; the difference in the displacement, speed, and acceleration of the body’s center of gravity during the badminton serve will affect the error of the action. And the action recognition method used in this study has certain advantages compared with the other two action recognition methods, and the accuracy of action recognition is higher.

scholarly journals Application of Convolution Neural Network (CNN) Model Combined with Pyramid Algorithm in Aerobics Action Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qi Liang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Action Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Convolution Neural Network ◽  
Recognition Method ◽  
Recognition Model ◽  
Recognition Function ◽  
The Common

In order to realize high-accuracy recognition of aerobics actions, a highly applicable deep learning model and faster data processing methods are required. Therefore, it is a major difficulty in the field of research on aerobics action recognition. Based on this, this paper studies the application of the convolution neural network (CNN) model combined with the pyramid algorithm in aerobics action recognition. Firstly, the basic architecture of the convolution neural network model based on the pyramid algorithm is proposed. Combined with the application strategy of the common recognition model in aerobics action recognition, the traditional aerobics action capture information is processed. Through the characteristics of different aerobics actions, different accurate recognition is realized, and then, the error of the recognition model is evaluated. Secondly, the composite recognition function of the convolution neural network model in this application is constructed, and the common data layer effect recognition method is used in the optimization recognition. Aiming at the shortcomings of the composite recognition function, the pyramid algorithm is used to improve the convolution neural network recognition model by deep learning optimization. Finally, through the effectiveness comparison experiment, the results show that the convolution neural network model based on the pyramid algorithm is more efficient than the conventional recognition method in aerobics action recognition.

Fight Action Recognition Method using AI Deep Learning

2021 ◽  
Vol 27 (7) ◽  
pp. 482-489
Author(s):  
Ho-yeon Kim ◽  
Young-Ki Song ◽  
Jae-Soo Cho
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Recognition Method

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.

scholarly journals Computer Vision-Driven Evaluation System for Assisted Decision-Making in Sports Training

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lijin Zhu
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Evaluation System ◽  
Rapid Development ◽  
Sports Training ◽  
Traditional System ◽  
System User ◽  
Overall Evaluation ◽  
Multiple Sensor ◽  
Application Fields

Computer vision has become a fast-developing technology in the field of artificial intelligence, and its application fields are also expanding, thanks to the rapid development of deep learning. It will be of great practical value if it is combined with sports. When a traditional exercise assistance system is introduced into sports training, the athlete’s training information can be obtained by monitoring the exercise process through sensors and other equipment, which can assist the athlete in retrospectively analyzing the technical actions. However, the traditional system must be equipped with multiple sensor devices, and the exercise information provided must be accurate. This paper proposes a motion assistance evaluation system based on deep learning algorithms for human posture recognition. The system is divided into three sections: a standard motion database, auxiliary instruction, and overall evaluation. The standard motion database can be customized by the system user, and the auxiliary teaching system can be integrated. The user’s actions are compared to the standard actions and intuitively displayed to the trainers as data. The system’s overall evaluation component can recognize and display video files, giving trainers an intelligent training platform. Simulator tests are also available. It also demonstrates the efficacy of the algorithm used in this paper.

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action
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