摘要
随着人们对健康监测关注度的日益增长和智能手机的普及,文中提出了一种基于SVM的人体运动分析方法,并基于该方法和Android平台设计了一种防跌倒系统。通过采集智能手机内置传感器的数据,并对原始数据进行滤波去噪、加窗分割、时频特征提取及特征降维等处理,使用SVM分析与识别不同的行为。该防跌倒系统,在检测到用户处于跌倒状态时会发出求救信息。实验与测试结果表明,所提出的方法能获得97. 5%的敏感性和98. 3%的特异性,对跌倒行为具有较高的识别精度。
Focusing on growing concern on health monitoring and the popularity of smartphones,a human movement analysis method based on SVM is proposed and a fall prevention Android platform system based on this method is designed. By collecting the data through built-in sensors of smartphones,and processing the original data,such as filtering and denoising,window segmentation,time-frequency feature extraction and feature dimensionality reduction,and using SVM to analyze and identify different behaviors. The fall prevention system sends a distress message when it detects that the user is in a falling state. Experiments and test results show that the proposed method can obtain sensitivity of 97. 5% and specificity of 98. 3%,has high recognition accuracy for falls.
引文
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