Research on fall detection system based on support vector machine

Li-ran PEI; Ping-ping JIANG; Guo-zheng YAN

doi:10.3788/OPE.20172501.0182

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Research on fall detection system based on support vector machine

Information Sciences | 更新时间：2020-07-07

- Research on fall detection system based on support vector machine
- Optics and Precision Engineering Vol. 25, Issue 1, Pages: 182-187(2017)
- 作者机构：
  
  上海交通大学电子信息与电气工程学院, 上海 200240
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20172501.0182
  CLC： TP212.9
- Received：24 August 2016，
  
  Accepted：20 October 2016，
  
  Published：25 January 2017
- 稿件说明：
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Li-ran PEI, Ping-ping JIANG, Guo-zheng YAN. Research on fall detection system based on support vector machine[J]. Optics and precision engineering, 2017, 25(1): 182-187.
DOI：

Li-ran PEI, Ping-ping JIANG, Guo-zheng YAN. Research on fall detection system based on support vector machine[J]. Optics and precision engineering, 2017, 25(1): 182-187. DOI： 10.3788/OPE.20172501.0182.

摘要

实时跌倒检测能有效降低老人因跌倒导致的身心伤害，提高老人的独居能力和健康水平。为提高基于惯性传感器的跌倒检测系统的准确率，降低系统误报率和漏报率，提出了应用基于径向基函数的支持向量机算法实现跌倒判定。首先，应用佩戴在人体腰间的便携式跌倒检测系统完成数据的采集；然后，利用基于径向基函数（RBF）的SVM分类器标记疑似跌倒行为，并利用粒子群算法完成分类算法中惩罚因子

和RBF参数

的优化。结果表明，在区分跌倒与类似跌倒的日常活动时，基于SVM算法的跌倒检测系统准确率、误报率和漏报率分别为97.67%，4.0%和0.67%。与传统的阈值方法相比，跌倒检测性能有很大提高，从而加强了该系统在老人跌倒检测中的应用。

Abstract

Real-time fall detection has great advantages of reducing physical and psychological damage in senior citizens group after falls and improving solitude ability and health level of senior citizens. A support vector machine (SVM) algorithm

which is based on RBF (Radial Basis Function) and applied to achieve fall detection

has been proposed in order to improve accuracy rate and lower false positive and false negative rate of fall detection system on the basis of inertial sensor. First

the system completes data collection by portable inertial sensing system at waist; then

it utilizes RBF-based SVM classifier to identify suspected fall behaviors and Particle Swarm Optimization to complete optimization of penalty factor 'C' and RBF argument 'g' in sorting algorithm. The falls and similar falls daily activities distinguishing experimetal results indicate that accuracy rate

false positive and false negative rate based on SVM algorithm are 97.67%

4.0% and 0.67% respectively. Compared with traditional threshold methods

the performance of proposed method on fall detection is promoted remarkably

so it can conclude that the appliance of the system in senior citizens' fall detection is enhanced as well.

关键词

Keywords

references

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