Real-Time 3D Face Alignment Using an Encoder-Decoder Network With an Efficient Deconvolution Layer

As a traditional Chinese sport, competitive martial arts routines have a long history. The competition rules are the unified norms and standards formulated for sports competitions. They are a yardstick for referees to judge the technical level and competitive ability of athletes and an essential basis for coaches during training. In particular, the new rules increase the difficulty of martial arts routines training and score, improve the balance movement of various groups, highlight the action specifications, increase the proportion of the score, and strengthen the scoring measures for the performance level. Subsequently, this puts higher requirements for the exceptional technical level of routine athletes. Therefore, it is vital to formulate scientific martial arts systematic training methods. This paper considers the above problem and current popular artificial intelligence technology and constructs a neural network algorithm to solve it. In addition, since lactic acid is a good monitoring indicator of the training load intensity and effect of martial arts routine exercises, this article also considers extensive lactate measurement data to construct martial arts systematic training methods. Through simulations, our experimental verification and the obtained results demonstrate the effectiveness of the proposed algorithm.

Section: Introductionmentioning

confidence: 99%

Martial Arts Routine Training Method Based on Artificial Intelligence and Big Data of Lactate Measurement

Han

Huo

Li³

2021

“…All over the world, significant changes have taken place in the lifestyles of youths. This amount has been significantly reduced compared with before, and the busy schoolwork of adolescents has also led to a significant reduction in the time of their physical exercise [ 1 ]. In response to the current decline in young people and children's physical fitness, we are very concerned about adolescents' healthy development.…”

Section: Introductionmentioning

confidence: 99%

Data Mining Algorithm for Physical Health Monitoring of Young Students Based on Big Data

Sun

Jiang

Yuan

et al. 2021

With the continuous improvement of living standards, the level of physical development of adolescents has improved significantly. The physical functions and healthy development of adolescents are relatively slow and even appear to decline. This paper proposes a novel data mining algorithm based on big data for monitoring of adolescent student’s physical health to overcome this problem and enhance young people’s physical fitness and mental health. Since big data technology has positive practical significance in promoting young people’s healthy development and promoting individual health rights, this article will implement commonly used data mining algorithms and Hadoop/Spark big data processing. The algorithm on different platforms verified that the big data platform has good computing performance for the data mining algorithm by comparing the running time. The current work will prove to be a complete physical health data management system and effectively save, process, and analyze adolescents’ physical test data.

“…e human eye often cannot accurately judge the movement posture of the ball [1], and often there are problems that the ball's falling point cannot be accurately recognized and the movement track cannot be retained. e use of machine learning techniques to identify and locate the ball and computer vision [4][5][6][7][8][9] and deep learning techniques [10][11][12][13][14] to reconstruct and display the ball's trajectory is a futuristic application. It results in instant playback systems in table tennis matches due to merging technology and sports.…”

Section: Introductionmentioning

confidence: 99%

Falling-Point Recognition and Scoring Algorithm in Table Tennis Using Dual-Channel Target Motion Detection

Yang

Chang

Chen

2021

In table tennis, the ball has numerous characteristics of high speed, small size, and changeable trajectory. Due to these characteristics, the human eye often cannot accurately judge the ball’s movement and position, leading to the problem of precise detection of the ball’s falling point and movement tracking. In sports, the use of machine learning for locating and detecting the ball and the use of deep learning for reconstructing and displaying the ball’s trajectories are considered futuristic technologies. Therefore, this paper proposes a novel algorithm for identifying and scoring points in table tennis based on dual-channel target motion detection. The proposed algorithm consists of multiple input channels to jointly learn different features of table tennis images. The original image is used as the input of the first channel, and then the Sobel operator is used to extract the first-order derivative feature of the original image, which is used as the input of the second channel. The table tennis feature information from the two channels is then fused and sent to the 3D neural network module. The fully connected layer is used to identify the table tennis ball’s drop point, compare it with a standard drop point, calculate the error distance, and give a score. We also constructed a data set and conducted experiments. The experimental results show that the method in this paper is effective in sports.