计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 242-246.doi: 10.11896/JsJkx.191000077

• 计算机图形学 & 多媒体 • 上一篇    下一篇

基于BP神经网络的摔倒检测算法

周立鹏1, 孟利民1, 周磊1, 蒋维2, 董建平3   

  1. 1 浙江工业大学信息工程学院 杭州 310023;
    2 浙江树人大学信息科技学院 杭州 310015;
    3 台州市行政学院信息处 浙江 台州 318000
  • 发布日期:2020-07-07
  • 通讯作者: 孟利民(mlm@zJut.edu.cn)
  • 作者简介:2111703015@zJut.edu.cn
  • 基金资助:
    国家自然科学基金项目(61871349);浙江省基础公益项目(LY18F010024,LQ19F010013)

Fall Detection Algorithm Based on BP Neural Network

ZHOU Li-peng1, MENG Li-min1, ZHOU Lei1, JIANG Wei2 and DONG Jian-ping3   

  1. 1 College of Information Engineering,ZheJiang University of Technology,Hangzhou 310023,China
    2 College of Information Science and Technology,ZheJiang Shuren Unuversity,Hangzhou 310015,China
    3 Information Department,Taizhou Administrative College,Taizhou,ZheJiang 318000,China
  • Published:2020-07-07
  • About author:ZHOU Li-peng, born in 1994, postgra-duate.His main research interests include wireless communication signal processing and system design, machine learning, etc.
    MENG Li-min, born in 1963, Ph.D, professor, Ph.D supervisor.Her main research interests include wireless communication and network, intelligent information system, network management, multimedia digital communication and network, etc.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61871349) and Basic public welfare proJects in ZheJiang (LY18F010024,LQ19F010013).

摘要: 摔倒对于老年人来说是一个十分严重的问题,实时检测老年人是否摔倒对于减轻摔倒造成的伤害具有重要意义。为此,文中提出了一种基于BP神经网络的摔倒检测算法。该算法采用佩戴于腰部的六轴传感器(MPU6050)来采集人体运动数据,使用简单的统计学方法对数据进行特征提取,并以提取到的特征为BP神经网络的输入神经元,用Levenberg-Marquardt算法训练神经网络模型,使其能够实现摔倒检测的功能。实验结果表明,该算法可以较好地识别摔倒,其准确率可以达到99.55%。

关键词: BP神经网络, 可穿戴式设备, 模式识别, 摔倒检测, 特征提取

Abstract: Fall is a very serious problem for the elderly.Real-time detection of whether the elderly fall or not is of great significance to reduce the inJury caused by falling.Therefore,a fall detection algorithm based on BP neural network is proposed in this paper.The algorithm collects human motion data with a six-axis sensor (MPU6050) worn at the waist,and uses a simple statistical method to extract features from the data.The extracted features are used as input neurons of BP neural network,and Levenberg-Marquardt algorithm is used to train the neural network model,so that it can realize the function of fall detection.Experimental results show that the algorithm can recognize falls well and the accuracy can reach 99.55%.

Key words: BP neural network, Fall detection, Feature extraction, Pattern recognition, Wearable equipment

中图分类号: 

  • TP183
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