基于无线信道状态信息的跌倒无源监测方法

doi:10.11772/j.issn.1001-9081.2018091938

计算机应用 ›› 2019, Vol. 39 ›› Issue (5): 1528-1533.DOI: 10.11772/j.issn.1001-9081.2018091938

• 应用前沿、交叉与综合 • 上一篇下一篇

基于无线信道状态信息的跌倒无源监测方法

黄濛濛¹, 刘军¹, 张逸凡¹, 谷雨¹, 任福继^1,2

1. 合肥工业大学计算机与信息学院, 合肥 230601;
2. 德岛大学信息科学与智能系统系, 日本德岛 77085020

收稿日期:2018-09-19 修回日期:2018-11-26 出版日期:2019-05-10 发布日期:2019-05-14
通讯作者: 黄濛濛
作者简介:黄濛濛(1995-),女,安徽六安人,硕士研究生,主要研究方向:情感计算、跌倒监测;刘军(1978-),男,江苏新沂人,副教授,博士,CCF会员,主要研究方向:机器学习加速、计算机体系结构;张逸凡(1993-),男,安徽合肥人,硕士研究生,主要研究方向:情感计算、机器学习;谷雨(1986-),男,安徽庐江人,教授,博士,CCF高级会员,主要研究方向:情感计算;任褔继(1959-),男,四川南充人,教授,博士,日本工程院院士,主要研究方向:情感计算、智能机器人。
基金资助:
国家自然科学资金资助项目（61772169，61432004，61502140）；国家重点研发计划项目（2018YFB0803403）；中央高校基本科研专项资金资助项目（JZ2018HGPA0272）；江苏省物联网重点实验室开放项目（JSWLW-2017-002）。

Passive falling detection method based on wireless channel state information

HUANG Mengmeng¹, LIU Jun¹, ZHANG Yifan¹, GU Yu¹, REN Fuji^1,2

1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei Anhui 230601, China;
2. Department of Information Science and Intelligent Systems, University of Tokushima, Tokushima 77085020, Japan

Received:2018-09-19 Revised:2018-11-26 Online:2019-05-10 Published:2019-05-14
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61772169, 61432004, 61502140), the National Key R&D Program of China (2018YFB0803403), the Fundamental Research Fund for the Central Universities (JZ2018HGPA0272), the Open Project by Jiangsu Province Key Laboratory of Internet of Things (JSWLW-2017-002).

摘要/Abstract

摘要： 针对传统基于视频或传感器的跌倒检测方法中环境依赖、空间受限等问题，提出了一种基于无线信道状态信息的跌倒无源检测方法Fallsense。该方法利用普适、低成本的商用WiFi设备，首先采集无线信道状态数据并对数据进行预处理，然后设计动作—信号分析模型，建立轻量级动态模板匹配算法以从时序信道数据中实时检测出承载真实跌倒事件的相关片段。大量实际环境下的实验表明，Fallsense可以实现较高的准确率以及较低的误报率，准确率达到95%，误报率为2.44%。与经典WiFall系统相比，Fallsense将时间复杂度从WiFall的O（mN log N）降低到O（N）（N是样本数，m是特征数），且准确率提高了2.69%，误报率下降了4.66%。实验结果表明，所提方法是一种快速高效的无源跌倒检测方法。

关键词: 跌倒检测, 信道状态信息, 模板匹配, 无源监测

Abstract: Traditional vision-based or sensor-based falling detection systems possess certain inherent shortcomings such as hardware dependence and coverage limitation, hence Fallsense, a passive falling detection method based on wireless Channel State Information (CSI) was proposed. The method was based on low-cost, pervasive and commercial WiFi devices. Firstly, the wireless CSI data was collected and preprocessed. Then a model of motion-signal analysis was built, where a lightweight dynamic template matching algorithm was designed to detect relevant fragments of real falling events from the time-series channel data in real time. Experiments in a large number of actual environments show that Fallsense can achieve high accuracy and low false positive rate, with an accuracy of 95% and a false positive rate of 2.44%. Compared with the classic WiFall system, Fallsense reduces the time complexity from O(mN log N) to O(N) (N is the sample number, m is the feature number), and increases the accuracy by 2.69%, decreases the false positive rate by 4.66%. The experimental results confirm that this passive falling detection method is fast and efficient.

Key words: falling detection, channel state information, template matching, passive detection

中图分类号:

TP391.4

黄濛濛, 刘军, 张逸凡, 谷雨, 任福继. 基于无线信道状态信息的跌倒无源监测方法[J]. 计算机应用, 2019, 39(5): 1528-1533.

HUANG Mengmeng, LIU Jun, ZHANG Yifan, GU Yu, REN Fuji. Passive falling detection method based on wireless channel state information[J]. Journal of Computer Applications, 2019, 39(5): 1528-1533.

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基于无线信道状态信息的跌倒无源监测方法

Passive falling detection method based on wireless channel state information

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