The Design And Application Of Monitoring System For Fall Detection And Prevention Based On Multi-sensor Data Acquisition

Increasingly increase along with the population aging in our country, how to improve the quality of life of the elderly and how to effectively protect the personal safety of old people has become a strategic demand of the country. Fall is the most important factor in personal safety threat to the elderly,development of a safe and effective fall detection and prevention and fall protection device has great social value and market economy. To design a safe,reliable and effective fall detection and prevention algorithm cannot leave a lot of human movement experiment,so developing a real time human movement state of the human body inertia data and monitor the experiment system is particularly important. A large body of and effective inertial data is workable fall detection and prevention analysis and algorithm of the premise,in this paper, design of data acquisition monitoring system based on multi-sensor human inertia has support for a variety of transmission methods, sampling rate is adjustable,convenient operation, friendly interface software platform, low cost, etc.System by collecting and sending data node, data receiving station and data processing software platform of three parts. Node by STM32F405 RG as a master,nine shaft MPU9150 inertial sensors,Rf module NRF24L01 + and build the smallest system of power management module, etc. This article mainly from the following several parts:First, send the human body inertia data under a machine node design; Second, applied to the nodes of the realization of the multisensor data fusion algorithm; Third, PC monitoring software platform design; Fourth,the fall in the human body movement experiment of processing and analysis.Through a lot of human movement experiment found that the system of real-time data transmission speed, high reliability, sampling rate can reach 150 hz. Monitoring software can not only real-time rendering of high precision inertial data at the same time with the human body motion capture function, to better reflect the reality of the data and reliability. To prevent fall detection and prevention algorithm improve the real and effective data source.