Analysis Method of Motion Information Driven by Medical Big Data

Nowadays, with the maturity and development of Internet, cloud computing and Internet of things technology, and the wide popularization of medical / health information technology, medical / health related data is growing at an amazing speed. At the same time, the popularization and application of genomic technology and the rapid development of wearable mobile medical and mobile health technology, promote the field of health care to enter the era of big data. Traditional sports risk assessment methods can assess the risks in sports, but there are problems such as time-consuming and poor accuracy in the assessment process, which can not be used in large-scale sports assessment. A method of motion risk assessment based on big data analysis is proposed. Based on the analysis of risk factors, this paper constructs a risk assessment model of large-scale sports, and introduces multi-level superposition operation and multi-factor mediation variance method, processes the sports risk data, and realizes the sports risk assessment using BP neural network based on the big data analysis method. The experimental results show that the proposed method based on big data analysis, compared with the traditional risk assessment method, can carry out high-efficiency and accurate motion risk assessment, and can be applied to large-scale risk assessment. This system has theoretical and practical significance for guiding the theory and practice of big data driven health / disease management, promoting the transformation and upgrading of sports health management and the development of health care big data industrialization.