传感技术学报2017，Vol.30Issue(10)：1504-1511,8.DOI:10.3969/j.issn.1004-1699.2017.10.009
基于人体姿态的P SO-SVM特征向量跌倒检测方法
PSO-SVM Feature Vector Fall Detection Algorithm Based on Human Postures
摘要
Abstract
Adopting the method of accelerating threshold can not demonstrate the variation of falling message. It will lead to the misjudgement of tumble,when using a wearable device to detect falling situation. In this paper,a PSO-SVM eigenvector fall detection algorithm based on human posture is proposed. Firstly,it collected the data of human body through the MEMS acceleration sensor node,and optimized the collected data by the conjugate gradient meth-ods to reduce the nonlinear error. Secondly,the support vector machine( SVM) is used to detect and classify the fall behavior,and the SVM parameters are optimized by Particle Swarm Optimization( PSO) algorithm to obtain the opti-mal classification model. According to analyze the collected data by SVM classification model,it can judge whether to fall;Finally,it can constructed the PSO-SVM eigenvector which fusing human posture angle to detect the specific information of fall process. The experimental results show that the proposed method attains a recognition rate of 95.5%,which can distinguish the other non-falls. The detection accuracy is higher than other methods, the root-mean-square error is smaller and the robustness is better.关键词
跌倒检测/人体姿态/传感器节点/特征向量/支持向量机/粒子群Key words
fall detection/human posture/sensor node/feature vector/support vector machine/particle swarm opti-mization分类
信息技术与安全科学引用本文复制引用
麻文刚,王小鹏,吴作鹏..基于人体姿态的P SO-SVM特征向量跌倒检测方法[J].传感技术学报,2017,30(10):1504-1511,8.基金项目
国家自然科学基金项目(61261029,61761027) （61261029,61761027）
