Analysis Method of Motion Information Driven by Medical Big Data

Pingsheng Zhu,Jingyi Zhang,Tong Zhao

doi:10.1109/access.2019.2956803

Pingsheng Zhu, Jingyi Zhang + Show 1 more

Open Access

https://doi.org/10.1109/access.2019.2956803

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Journal: IEEE Access	Publication Date: Jan 1, 2019
Citations: 8	License type: CC BY 4.0

Affiliation: First Affiliated Hospital of Henan University

Abstract

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.

Highlights

Health is the basic right of human beings, and health is the first wealth of life [1]
The whole effective Information analysis of Big Data Based on EEMD-HHT and BP neural network is designed as Figure 4
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

Summary

INTRODUCTION

Health is the basic right of human beings, and health is the first wealth of life [1]. The wearable intelligent medical big data acquisition terminal in this solution can monitor the physical activity of the user in real time by using the built-in low-power tri-axial acceleration sensor [25], [26] On this basis, the raw data of the acceleration sensor is pre-processed, and combined with the corresponding transformation algorithm of physical activity [27]; the key index accelerometer value of the physical activity of the human body is calculated. In the experimental study, we will analyze the relationship between coherence and the average instantaneous frequency of IMF information, and combine the two effectively to carry out a comprehensive evaluation of exercise risk, which can reflect the change trend of signal vibration frequency and amplitude in the process of exercise, and reflect the coupling characteristics between muscles, so as to enhance the reliability of muscle fatigue estimation

MOTION RISK ASSESSMENT METHOD BASED ON BP NEURAL NETWORK

ANALYSIS OF BIG DATA MOVEMENT RISK ASSESSMENT

SIMULATION AND DISCUSSION

Findings

CONCLUSION