Multi‐view frontal face image generation: A survey

In recent years, athlete action recognition has become an important research field for showing and recognition of athlete actions. Generally speaking, movement recognition of athletes can be performed through a variety of modes, such as motion sensors, machine vision, and big data analysis. Among them, machine vision and big data analysis usually contain significant information which can be used for various purposes. Machine vision can be expressed as the recognition of the time sequence of a series of athlete actions captured through camera, so that it can intervene in the training of athletes by visual methods and approaches. Big data contains a large number of athletes’ historical training and competition data which need exploration. In-depth analysis and feature mining of big data will help coach teams to develop training plans and devise new suggestions. On the basis of the above observations, this paper proposes a novel spatiotemporal attention map convolutional network to identify athletes’ actions, and through the auxiliary analysis of big data, gives reasonable action intervention suggestions, and provides coaches and decision-making teams to formulate scientific training programs. Results of the study show the effectiveness of the proposed research.

Section: Methodsmentioning

confidence: 99%

Machine Vision and Big Data-Driven Sports Athletes Action Training Intervention Model

Jiang

Wang

Lei

2021

“…Motion capture technology is to obtain 3D motion information of the human body in real-time by computer vision [5][6][7] For example, in the aspect of fall monitoring, through wearing inertial sensors on the wrist or other parts, real-time acquisition of the acceleration, angular velocity, and other information of the wearing part, when the human body suddenly falls, the inertial data of the wearing part will change suddenly, to judge that the human body falls and timely ask the family members and medical staff for help.…”

Section: Design and Implementation Of A Wearable Human Motion Capture Systemmentioning

confidence: 99%

Health Analysis of Footballer Using Big Data and Deep Learning

Yang

Yuan

Yan

2021

With the development of information technology, health management and big data have risen and developed in recent years. Big data need proper analysis and shape in order to extract meaningful information from it. This paper analyzes the application status and prospects of big data in the field of health management. The results show that the most widely used big data in health management are intelligent wearable devices. Big data applications in football players’ mental health monitoring systems and chronic disease health management systems also have a good prospect. The intelligent wearable device is applied to several aspects of sports work: teaching and sports training, real-time monitoring of football players’ physical exercise process, collecting football players’ heart rate, calorie consumption, exercise steps, and track, blood pressure, blood oxygen, and other physical exercise data; through monitoring the heart rate, we can get the intensity and duration of football players’ physical exercise in school; through the calculation, we can also get the football players’ time energy consumption and understand the overall situation of football players’ physical exercise in school; through step counting and track monitoring, we can master the number of steps and track of football players; by monitoring the changes of blood oxygen and blood pressure of football players, we need to build a third-party residents’ health information storage and analysis system and further realize the marketization of residents’ health big data. The experimental results of the proposed study show the effectiveness of the proposed work.

“…Technology has changed the complexity of computer image processing and can also achieve the same versatility, which is very convenient for our daily application [20,21]. erefore, a comprehensive analysis of the image processing technology in artificial intelligence [22][23][24][25][26] will be better recognition of technology after the traditional image processing technology [27][28][29][30][31][32].…”

Section: Research On the Process Of Image Recognition Technology In Artificialmentioning

confidence: 99%

Trampoline Motion Decomposition Method Based on Deep Learning Image Recognition

Liu

Dong

Wang

2021

The automatic segmentation and classification of an unknown motion data stream based on given motion classes constitute an important research problem with applications in computer vision, animation, healthcare, and sports sciences. In this paper, the scenario of trampoline motions is considered, where an athlete performs a routine consisting of sequence of jumps that belong to predefined motion classes such as somersaults. The purpose of this study was to make theoretical discussions on the turning starting time and starting technique of trampoline somersault based on image recognition and point out that the appropriate turning starting time of trampoline somersault is the event when the spring net of the trampoline recovers and applies force to the human body, and the overturning start exists in the latter half of the take-off action. It is considered that how to obtain the ideal full reaction force of the net facing the human body is the flip starting technique. This work analyzes the key steps and events for trampoline somersaults and the application of artificial intelligence for the recognition of actions in the healthcare and sports fields. The effectiveness of the proposed study is shown through experimental results. The study can facilitate the process of recognition of trampoline somersault.