Research Of Smart Wearable Device For Preventing Elderly People From Falling

The aging of China’s population has become increasingly serious.At the same time,it has been intertwined with the Chinese family’s family pension tradition that has lasted for thousands of years,and the 40-year-only one-child policy that followed the reform and opening up of the Chinese mainland,which has caused tremendous pressure on our society.Researching and developing special equipment to protect the health of the elderly is one of the important efforts to alleviate these pressures.Fall is the enemy of the health of the elderly.This article aims to develop equipment for preventing fall of the elderly,which has important practical value.This article summarizes the research status of research-related technologies at home and abroad,and carries out program design,risk early warning algorithm research,hardware design,software design,and key experiments.The main research contents and achievements of this paper are as follows:(1)Design of the overall plan for the fall prevention device for the elderly.The device is mainly composed of five parts:a master chip,a power chip,a motion sensor,an airbag module,and a Bluetooth serial port module.Lab VIEW and MATLAB are used to build a software development platform.(2)01d people fall prevention device hardware design.According to the design of the plan,the specific design of the hardware was carried out,including the specific selection and system design of sensors,embedded computer chips,power chips,and Bluetooth serial port modules.(3)Data acquisition and preprocessing software implementation.The data collection software program was compiled for the designed hardware,and the automatic acquisition and filtering of human posture related data were preprocessed.(4)Danger warning algorithm research and software implementation.From the collected and pre-processed data,features that characterize human stance are extracted and a human gestural data set is established.Support vector machine(SVM)and Random Forest algorithm are used to establish the fall posture detection model respectively,and the two models are compared and evaluated.Finally,the fall detection model based on support vector machine was selected.The model has better real-time performance.(5)Key experiment.The experiment was designed using a scientific experimental design method to verify the fall-proof wearable device of the fall detection model based on the support vector machine.The experimental results show that the designed detection model and fall prevention device can perform real-time discrimination and early warning of the human fall posture.