Study on safety mode of dragon boat sports physical fitness training based on machine learning
مطالعه نحوه ایمنی تمرینات آمادگی جسمانی ورزشی قایق اژدها بر اساس یادگیری ماشین-2019
In order to improve the safety control ability of dragon boat sports physical fitness training, this paper uses the advantages of machine learning in data analysis and feature mining in the training of dragon boat sports. A machine learning-based safety mode control model for dragon boat sports physical fitness training was proposed. Big data statistical analysis method was used to analyze the constraint parameters of the safety mode of dragon boat sports physical fitness training, and combined with the joint association rule mining method, the dragon boat sports physical fitness training safety mode training was carried out. The correlation feature quantity which constrains the safety of dragon boat physical ability training is extracted. The fuzzy clustering technique is used to classify and study the safety management data of dragon boat sport physical ability training. The method of spectral density analysis and fuzzy fusion clustering analysis is used to realize automatic mining of safety association feature data of dragon boat physical fitness training, and machine learning algorithm is combined to realize the optimization of safety pattern of dragon boat sports physical fitness training. The simulation results show that the feature extraction of the safety model of dragon boat sports physical fitness training is better and the ability of feature resolution is stronger, which improves the safety management ability of dragon boat sports physical fitness training.
Keywords: Machine learning | Dragon boat sports | Physical training | Safety mode
Research of WSN and Big Data Analysis based Continuous Pulse Monitoring System for Efficient Physical Training
تحقيق بر روی سيستم مديريت پالس مداوم برای آموزش فيزيکی کارآمد با استفاده از تحليل داده های شبکه های حسگر بی سیم و داده های بزرگ-2016
For the problems that we can’t monitor abnormal conditions of heart rate as well as carrying out scientific and efficient training plans based on knowledge from variation of them. A ZigBee and big data analysis based pulse monitoring system has been proposed. The system is composed of multiple ZigBee based pulse monitoring sensors, customized gateways and back-end system. Individuals’ pulse information are collected by the sensors and passed to back-end system to support big data analysis of the training conditions. To guarantee collecting efficient pulse signal, we have researched photo electricity based dynamic and continuous heart rate monitoring methods as well as comprehensive anti-jamming methods. Finally, by using according big data analysis methods we have built up the training model by the standards such as different age, different mood and so on. Results shows the system can be used to improve the physical training level; accumulate the training data of the individuals and support more efficient and scientific training plans.
Keywords: IOT | ZigBee | Big Data Analysis | Pulse Monitoring | Physical Training