Human action tracking design of neural network algorithm based on GA-PSO in physical training

Leng, Xinke; Jiang, Huijun; Zuo, Xin

doi:10.1007/s10586-017-1658-x

Cited by 8 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To improve the effect of physical training, in [12], the authors combined machine learning to identify physical training characteristics and action prediction, combined with the Internet of things technology to process physical training data, and constructed a machine learning and the Internet of things based on physical education and training system. To solve the problem of human motion recognition, in [13], the authors proposed a recognition system for human motion tracking in physical training. In [14], the authors presented a balanced shunt point field programmable gate array-based method of physical training planning to improve the effectiveness of physical training planning.…”

Section: Related Researchmentioning

confidence: 99%

Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

Wang

2022

Mobile Information Systems

View full text Add to dashboard Cite

The rapid development of Olympic Games and the intensification of professional sports events make the competition between excellent athletes and teams become more and more fierce, so the demands on athletes’ psychology, techniques, and physical strength are becoming higher and higher, especially that the demands on physical strength are more prominent. Therefore, physical training is an important part of sports training and the core link of competitive sports, which is widely valued by coaches and athletes all over the world. At the same time, with the influence and penetration of the rapid development of modern information technology and network big data on competitive sports, the scientific and digital process of athletes’ physical training has also been accelerated, and many new ideas, scientific training methods, and advanced sports technology have emerged. During the pandemic, many aggregation activities have been disrupted, as well as physical training. Therefore, this paper improves the quality of physical training through video, so that athletes can do physical training at any time and any place. A cut vertices spanning tree algorithm based on machine learning is proposed to distribute layered multicast streaming and dynamically adjust the number of layers of sports video streaming. The cut vertices spanning tree algorithm is mainly applied to the situation when the network bandwidth resources are relatively scarce. The evaluation results indicate that the proposed method can improve the estimated quality of experience (QoE), packet loss rate, link utilization, and video delay on Mininet simulation platform. Furthermore, it can be seen from the experiments that the proposed method has a good performance on distribution of sports video stream assisting physical training.

show abstract

Section: Related Researchmentioning

confidence: 99%

Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

Wang

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…Another line of research addresses the optimal behavior inference problem using a genetic algorithm (GA), which improves their objectives by sufficiently exploring the solution space [6], [7], [15], [16]. Although GA did not require building a matrix, chromosome and crossover/mutation schemes should be predefined to perform the algorithm [8], [16]. [14].…”

Section: Introductionmentioning

confidence: 99%

A Behavior Optimization Method for Unmanned Combat Aerial Vehicles Using Matrix Factorization

et al. 2020

View full text Add to dashboard Cite

One of the fundamental technologies for unmanned combat aerial vehicles and combat simulators is behavior optimization, which finds a behavior that maximizes the probability of winning a battle. With the advent of military science, combat logs became available, allowing machine learning algorithms to be used for the behavior optimization. Due to implicit attributes such as the experience of an operator that are not explicitly presented in log data, existing methods for behavior optimization have limitations in performance improvement. Furthermore, specific behaviors occur with low frequency, resulting in a dataset with imbalanced and empty values. Therefore, we apply a matrix factorization (MF) method, which is one of latent factor models and known for sophisticated imputation of empty values, to the behavior optimization problem of unmanned combat aerial vehicles. A situation-behavior matrix, whose elements are ratings indicating the optimality of behaviors in situations, is defined to implement the MF based method. Experiments for performance comparison were conducted on combat logs, in which the proposed method yielded satisfactory results. INDEX TERMS behavior optimization, unmanned vehicle, matrix factorization, reinforcement learning, situation-behavior matrix ABBREVIATIONS AM Advantage matrix. FOV Field of view. GA Genetic algorithm. LOS Line of sight. MF Matrix factorization. nDCG Normalized discounted cumulative gain. RL Reinforcement learning. SB Situation-behavior. UV Unmanned vehicle.

show abstract

Interpolation neural network constructed by the step path and its approximation performance

2020

View full text Add to dashboard Cite

Human action tracking design of neural network algorithm based on GA-PSO in physical training

Cited by 8 publications

References 12 publications

Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

A Behavior Optimization Method for Unmanned Combat Aerial Vehicles Using Matrix Factorization

Interpolation neural network constructed by the step path and its approximation performance

Contact Info

Product

Resources

About