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CN 41-1437/TS  ISSN 2096-1553

基于AdaBoost多核支持向量机的跌倒检测研究

赵婉婉 任静 刘燕南 武东辉 余凯

赵婉婉, 任静, 刘燕南, 等. 基于AdaBoost多核支持向量机的跌倒检测研究[J]. 轻工学报, 2019, 34(3): 84-91. doi: 10.3969/j.issn.2096-1553.2019.03.010
引用本文: 赵婉婉, 任静, 刘燕南, 等. 基于AdaBoost多核支持向量机的跌倒检测研究[J]. 轻工学报, 2019, 34(3): 84-91. doi: 10.3969/j.issn.2096-1553.2019.03.010
ZHAO Wanwan, REN Jing, LIU Yannan, et al. Research on fall detection based on Adaboost multiple kernel support vector machine[J]. Journal of Light Industry, 2019, 34(3): 84-91. doi: 10.3969/j.issn.2096-1553.2019.03.010
Citation: ZHAO Wanwan, REN Jing, LIU Yannan, et al. Research on fall detection based on Adaboost multiple kernel support vector machine[J]. Journal of Light Industry, 2019, 34(3): 84-91. doi: 10.3969/j.issn.2096-1553.2019.03.010

基于AdaBoost多核支持向量机的跌倒检测研究

    作者简介: 赵婉婉(1989-),女,河南省济源市人,郑州轻工业大学助理工程师,主要研究方向为电气工程、模式识别、智能家居.;
  • 基金项目: 河南省科技攻关计划项目(182102210622);河南省高等学校重点科研项目(19A413013)

  • 中图分类号: TP391.41

Research on fall detection based on Adaboost multiple kernel support vector machine

  • Received Date: 2018-10-10

    CLC number: TP391.41

  • 摘要: 针对传统的跌倒检测模型实时性低、误报率高的问题,提出AdaBoost多核支持向量机模型(ADB-MKSVM),用于对跌倒动作进行检测识别.该模型基于改进的AdaBoost模型框架,将多核支持向量机作为基分类器,并集合这些基分类器构成一个更强的最终分类器;依据人体动作数据分布和每次训练集中各样本的分类是否正确,以及上次的总体分类准确率,来确定每个样本的权值,采用权重动态分配的方法提高跌倒动作的识别率.测试结果表明,该模型具有良好的分类性能,且传感器绑于腰部位置可有效提高跌倒动作的检测效果,其准确率为99.33%,跌倒检出率为63.6%,跌倒检测错误率为1.62%.
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  • 收稿日期:  2018-10-10
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赵婉婉, 任静, 刘燕南, 等. 基于AdaBoost多核支持向量机的跌倒检测研究[J]. 轻工学报, 2019, 34(3): 84-91. doi: 10.3969/j.issn.2096-1553.2019.03.010
引用本文: 赵婉婉, 任静, 刘燕南, 等. 基于AdaBoost多核支持向量机的跌倒检测研究[J]. 轻工学报, 2019, 34(3): 84-91. doi: 10.3969/j.issn.2096-1553.2019.03.010
ZHAO Wanwan, REN Jing, LIU Yannan, et al. Research on fall detection based on Adaboost multiple kernel support vector machine[J]. Journal of Light Industry, 2019, 34(3): 84-91. doi: 10.3969/j.issn.2096-1553.2019.03.010
Citation: ZHAO Wanwan, REN Jing, LIU Yannan, et al. Research on fall detection based on Adaboost multiple kernel support vector machine[J]. Journal of Light Industry, 2019, 34(3): 84-91. doi: 10.3969/j.issn.2096-1553.2019.03.010

基于AdaBoost多核支持向量机的跌倒检测研究

    作者简介:赵婉婉(1989-),女,河南省济源市人,郑州轻工业大学助理工程师,主要研究方向为电气工程、模式识别、智能家居.
  • 郑州轻工业大学 建筑环境工程学院, 河南 郑州 450002
基金项目:  河南省科技攻关计划项目(182102210622);河南省高等学校重点科研项目(19A413013)

摘要: 针对传统的跌倒检测模型实时性低、误报率高的问题,提出AdaBoost多核支持向量机模型(ADB-MKSVM),用于对跌倒动作进行检测识别.该模型基于改进的AdaBoost模型框架,将多核支持向量机作为基分类器,并集合这些基分类器构成一个更强的最终分类器;依据人体动作数据分布和每次训练集中各样本的分类是否正确,以及上次的总体分类准确率,来确定每个样本的权值,采用权重动态分配的方法提高跌倒动作的识别率.测试结果表明,该模型具有良好的分类性能,且传感器绑于腰部位置可有效提高跌倒动作的检测效果,其准确率为99.33%,跌倒检出率为63.6%,跌倒检测错误率为1.62%.

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