Evaluation Method of Athletes’ Special Physical Fitness Based on Internet of Things

Li, Jinling

doi:10.1590/1517-8692202127022021_0047

Cited by 7 publications

(5 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Again in advance for our estimation procedures for estimating mean velocities and procedures for estimating nonhorizontal models. There are four ways to estimate the average velocity, and the accuracy of the estimated error of the estimated material is less than that of the nonhorizontal model [ 23 ]. The mean error and mean correlation error indicate that the error is incorrect and means that the relative error of BP neural network casino is 6.91 kg and 9%, fraction, which is less than 10.05 kg and 13% of the estimated velocity.…”

Section: Results Analysismentioning

confidence: 99%

Load Prediction Model of Athletes’ Physical Training Competition Based on Nonlinear Algorithm Combined with Ultrasound

Zheng

2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

In order to provide theoretical support and ideas for the “dose” of high-stakes physical activity in athletics, the author has developed models for athletic competition based on nonlinear techniques together with ultrasound. Based on test data, average mean estimation method, and nonlinear regression model estimates, 52 points (46 test points, 6 point estimates) is enrolled in the highest voltage and maximum voltage measurement based on the BP neural network model. The estimation method was developed and the accuracy of the estimation of our estimation method was compared and evaluated using the estimation data. Experimental results show that the average relative error of the average estimate compared to the accuracy of the bench press was 25%, the standard estimate which is not linear regression is 31%, and BP neural network model estimation is 9%. Compared with the accuracy of the assumption of half squatting, the average relative error of the estimated velocity is 13%, the standard nonlinear regression estimate is 20%, and BP neural network model estimated method is 9%. The BP neural network predicts the method with the best performance and intelligence, but its actual functioning and application are complex. The average speed estimate is the most appropriate for use, but the equipment must be high. The process of estimating a linear regression model requires minimal equipment, but its prediction error is high.

show abstract

Section: Results Analysismentioning

confidence: 99%

Load Prediction Model of Athletes’ Physical Training Competition Based on Nonlinear Algorithm Combined with Ultrasound

Zheng

2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

show abstract

“…is method has high accuracy, but due to the high sampling frequency required for the extraction of transient characteristics, it has high requirements for hardware. Moreover, the switching operation of electric load occurs only at the beginning and end of operation, and the transient characteristics cannot be extracted during the time period of stable operation, so this method is not suitable for electric load identification in stable operation [12]. Liu et al divided the electric load into linear load and nonlinear load.…”

Section: Literature Reviewmentioning

confidence: 99%

Performance Evaluation System Based on Online Monitoring of Internet of Things

Zhang

Zhao

2022

Security and Communication Networks

View full text Add to dashboard Cite

Research on performance evaluation system based on online monitoring of Internet of things technology, taking medical imaging equipment as an example. Build the performance management platform of the Internet of things through four aspects: data acquisition, network architecture, CPU, and display output. Select 100 medical imaging devices in clinical use in the hospital and divide them into the control group (50 sets) and the observation group (50 sets) according to different management modes. The control group adopts conventional performance management mode, and the observation group implements Internet of things performance management mode. Compare the differences of equipment operation performance, cost-effectiveness, and service effect between the two groups. The experimental results showed that the equipment utilization rate, operation rate, inspection schedule balance, and technician schedule balance of the observation group were better than those of the control group, and the difference was statistically significant ( P < 0.05). The quarterly cost-benefit increase of x-ray examination equipment, CT equipment, Mr equipment, interventional radiotherapy equipment, color ultrasound instrument, and nuclear medicine equipment in the observation group was higher than that in the control group ( P < 0.05). The satisfaction of imaging doctors, technicians, engineers, and patients with the equipment service quality of the observation group was higher than that of the control group; the difference was statistically significant ( P < 0.05). It is proved that the Internet of things performance management platform of medical imaging equipment can solve the problems of untimely information collection and unscientific scheduling during equipment operation, realize the real-time display and statistics of equipment operation performance, and improve the operation efficiency and service level of medical imaging equipment.

show abstract

“…It is known that physical training plays an important role in sportsmanship [16,17,18]. This is especially true for children of primary school age.…”

Section: Discussionmentioning

confidence: 99%

Physical fitness of young Greco-Roman athletes under the restrictions of the COVID-19 pandemic

Rusanov

Vostroknutov

et al. 2022

Pedagogy of Health

View full text Add to dashboard Cite

Background and Study Aim. The physical training of young athletes is the basis for future success in competitions. Its effectiveness depends on many factors, among which safe conditions for life support stand out. In the context of the COVID-19 pandemic, there are significant restrictions on the training of young athletes. The aim of the study is the physical training of young Greco-Roman wrestlers in the context of the COVID-19 pandemic. Material and Methods. The study involved young athletes (boys, n=20, age 5-7 years (Kharkiv, Ukraine). The experimental group of initial training (n=10) consisted of young athletes of the first year of study (children's sports school). The control group (n=10) consisted of children in the first year of primary school education. The study was conducted for 12 weeks. At the beginning and at the end of the study, a final testing of the level of physical fitness of boys was carried out. Tests were used to determine strength abilities, speed-strength abilities, coordination readiness and flexibility development. The study included all students whose parents agreed to participate in the study. This study was conducted by the Declaration of Helsinki and approved by the Ethics Committee of University. Results. Positive changes in the experimental group were observed in tests characterizing the level of development of coordination abilities. During repeated testing, the experimental group was much better oriented in space when performing three rolls. This was manifested in a reduction in the time of their implementation (t=4.091; р˂0.001) and a longer balance in static conditions (t=4.11; р˂0.01). In tests for the manifestation of strength and coordination abilities, there is a tendency to positive changes. However, there was no confirmation of a likely difference between pre-test and post-test (p>0.05). In tests for flexibility, the result remained almost unchanged and even deteriorated slightly. There were very slight positive changes in other studied indicators. Conclusions. For the formation of basic physical fitness, it is effective to provide a greater variety of training means and to maximize the focus on the game method of training. The process of training wrestlers of this age should be directed mainly to the development of coordination abilities.

show abstract

Evaluation Method of Athletes’ Special Physical Fitness Based on Internet of Things

Cited by 7 publications

References 6 publications

Load Prediction Model of Athletes’ Physical Training Competition Based on Nonlinear Algorithm Combined with Ultrasound

Load Prediction Model of Athletes’ Physical Training Competition Based on Nonlinear Algorithm Combined with Ultrasound

Performance Evaluation System Based on Online Monitoring of Internet of Things

Physical fitness of young Greco-Roman athletes under the restrictions of the COVID-19 pandemic

Contact Info

Product

Resources

About