Computer-aided physical training sports injury risk simulation based on embedded image system

Abstract In teamwork, injuries occur from time to time. The athlete's body is very important, so the rich experience of teamwork can reduce the injury to the athlete, which is a very important aspect of preventing the risk and mitigating the risk. There are several ways to reduce the risk factors of injury. One is to use positioning method, scientists have developed a 2.4 GHz frequency wireless receiving sensor system, referred to as WSN. This system can effectively monitor indoor and outdoor environment and identify and analyze buried active RF in advance. After several frequency tests, more effective performance is obtained, and the transmission power and signal intensity system are developed. It can generate high-frequency amplitudes, and the transmitted data can create independent labels to receive. However, the difficulty in understanding the subtle differences between different statistical methods and making conclusions that lead to erroneous assumptions has been produced from the data. Therefore, the purpose of this study is to obtain the outline to determine the method used to determine the risk of damage, to predict the movement of injuries while developing the model and the existing evidence to highlight the difference between the association and the prediction associated with damaging it. Sports injuries risk factors research aimed to study the example of the method is evaluated using chopped strains. Injuries resulting from the complex interaction of several risk factors. Develop and adapt and, mental, emotional, social events. Therefore covers education system and the necessary physical activities organized by physical education. In order to create a rough picture of the whole creation and technique. The soap places to students who have used a solvent, is embedded in the grease.

[1]  J. Alonso,et al.  Translation Into Spanish and Proposal to Modify the Orchard Sports Injury Classification System (OSICS) Version 12 , 2020, Orthopaedic journal of sports medicine.

[2]  Kazuyoshi Ono,et al.  New Wearable Heart Rate Monitor for Contact Sports and Its Potential to Change Training Load Management , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[3]  Jonathan de Halleux,et al.  MakeCode and CODAL: Intuitive and efficient embedded systems programming for education , 2019, J. Syst. Archit..

[4]  Lizbeth Goodman,et al.  Life and living: Co-designing real and virtual spaces for survivors of severe Acquired Brain Injury (sABI) , 2017, 2017 23rd International Conference on Virtual System & Multimedia (VSMM).

[5]  Maziah Mat Rosly,et al.  Arm Exercises for Individuals with Spinal Cord Injury: Exergaming versus Arm Cranking , 2019, 2019 IEEE 7th International Conference on Serious Games and Applications for Health (SeGAH).

[6]  Dapeng Tang,et al.  Hybridized Hierarchical Deep Convolutional Neural Network for Sports Rehabilitation Exercises , 2020, IEEE Access.

[7]  C. Whatman,et al.  A prospective study of sport injuries in youth females. , 2020, Physical therapy in sport : official journal of the Association of Chartered Physiotherapists in Sports Medicine.

[8]  J. Oh,et al.  Prediction model for utilization of complementary and alternative medicine for sports injuries among Korean elite collegiate athletes , 2020, Integrative medicine research.

[9]  Jack D. Ade,et al.  Maturity-associated considerations for training load, injury risk, and physical performance in youth soccer: One size does not fit all , 2020, Journal of sport and health science.

[10]  Leilei Wang,et al.  Physical education image analysis based on virtual crowd simulation and FPGA , 2020, Microprocess. Microsystems.

[11]  Anfeng Xu,et al.  Ecological evolution path of smart education platform based on deep learning and image detection , 2020, Microprocess. Microsystems.

[12]  Yucheng Yang,et al.  Improved Ada Boost Classifier for Sports Scene Detection in Videos: from Data Extraction to Image Understanding , 2020, 2020 International Conference on Inventive Computation Technologies (ICICT).

[13]  Wendy Flores-Fuentes,et al.  Optical cyber-physical system embedded on an FPGA for 3D measurement in structural health monitoring tasks , 2018, Microprocess. Microsystems.

[14]  Zhu Haiyun,et al.  Sports performance prediction model based on integrated learning algorithm and cloud computing Hadoop platform , 2020 .

[15]  A. Neto,et al.  Etiology, prevalence, and severity of reported acute sports injuries in Brazilian Jiu-Jitsu Paradesports: An observational study , 2020 .

[16]  S. Kliethermes,et al.  Sport specialization and sport participation opportunities and their association with injury history in female high school volleyball athletes. , 2020, Physical therapy in sport : official journal of the Association of Chartered Physiotherapists in Sports Medicine.

[17]  Ayman El-Baz,et al.  Athlete-Customized Injury Prediction using Training Load Statistical Records and Machine Learning , 2018, 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[18]  Lin Zhang,et al.  Sports wearable device design and health data monitoring based on wireless internet of things , 2020 .

[19]  Karina Jaskolka,et al.  A Python-based laboratory course for image and video signal processing on embedded systems , 2019, Heliyon.