人员意外跌倒检测研究方法分析与综述

Analysis and Review of Research Methods for Personnel Accidental Fall Detection

随着世界人口老龄化问题日益严重,意外跌倒成为造成老年人意外死亡的第二大原因。如果尽早应对老年人跌倒问题,为他们跌倒进行第一时间的施救,则可以降低老年人跌倒所造成的严重后果,因此智能检测与防护系统的开发已经成为人们关注的焦点。本文研究了人员意外跌倒检测的相关方法,并对其进行分析与综述。首先介绍了跌倒检测系统中常用的基于传感器的数据获取方法;随后介绍了跌倒检测中常用于数据处理的CNN(convolutional neural networks)架构和LSTM(long-short term memory)架构,并将CNN架构分为目标检测和光流估计两部分进行介绍;最后总结了将传感器和相应的数据处理架构应用于机器人平台的研究工作,可为从事跌倒检测算法研究的人员提供一定的方法和技术指导。

With the aging of the world’s population becoming increasingly serious,accidental falls have become the second leading cause of accidental deaths among the elderly.If the problem of elderly falls is dealt with as early as possible and the first aid is provided for them,the serious consequences caused by the fall of the elderly can be reduced.Therefore,the development of intelligent detection and protection systems has received a great deal of attention.This article studies the related methods of accidental fall detection,analyzes and summarizes them.First,it introduces the sensor-based data acquisition methods commonly used in fall detection systems;then it introduces the convolutional neural Networks architecture and long-short term memory architecture commonly used for data processing in fall detection,dividing the CNN architecture into two parts—target detection and optical flow estimation to introduce;finally,the research work of applying sensors and corresponding data processing architecture to the robot platform is summarized,which can provide certain methods and technical guidance for those who are engaged in the research of fall detection algorithms.