基于WiFi背景噪音的被动式人体行为识别研究

doi:10.3969/j.issn.0253-2778.2015.04.008

中国科学技术大学学报 ›› 2015, Vol. 45 ›› Issue (4): 308-313.DOI: 10.3969/j.issn.0253-2778.2015.04.008

• 论著 • 上一篇

基于WiFi背景噪音的被动式人体行为识别研究

谷雨，权良虎，陈孟妮，任福继

合肥工业大学情感计算与先进智能机器安徽省重点实验室，安徽合肥 230009

收稿日期:2014-03-12 修回日期:2014-10-10 接受日期:2014-10-10 出版日期:2014-10-10 发布日期:2014-10-10
通讯作者: 谷雨
作者简介:谷雨(通讯作者)，博士/教授.研究方向：智能计算理论与无线通讯. E-mail: yugu.bruce@gmail.com
基金资助:
安徽省科技攻关项目资助(1206c0805039), 国家高技术研究发展(863)计划(2012AA011103), 国家自然科学基金青年项目（61300034）资助.

Research on passive human activity recognition using WiFi ambient signals

GU Yu, QUAN Lianghu, CHEN Mengni, REN Fuji

Affective Computing andAnHui Province Key Laboratory of Advanced Intelligence Machine, Hefei University of Technology, Hefei 230009,China

Received:2014-03-12 Revised:2014-10-10 Accepted:2014-10-10 Online:2014-10-10 Published:2014-10-10

摘要/Abstract

摘要： 利用WiFi背景噪音，传统K-NN和Bagging算法可有效识别较少人体行为，但对较多状态：无人、走、坐、站、睡、跌倒、跑，实验发现，单纯使用K-NN和Bagging算法分类效果并不理想，故设计了一种新的融合算法.实验结果证实，融合算法相较于K-NN和Bagging算法可以大幅提高识别准确率，将新算法应用于多人混合状态识别也取得较好的识别准确率.

关键词: WiFi背景噪音, 人体行为, 融合算法, 混合状态

Abstract: Although traditional k-nearest neighbor(K-NN) and Bagging can recognize effectively less human activities using WiFi ambient signal, recognition by either alone of the seven states, namely, empty, walking, sitting, standing, sleeping, falling and running, is not ideal. To improve recognition rates, a new algorithm, fusion algorithm, was designed. It significantly outperforms K-NN and Bagging in terms of recognition ratios in both single-subject and multi-subject experiments.

Key words: WiFi ambient signals, human activities, fusion algorithm, multi-subject

中图分类号:

TP181

谷雨，权良虎，陈孟妮，任福继. 基于WiFi背景噪音的被动式人体行为识别研究[J]. 中国科学技术大学学报, 2015, 45(4): 308-313.

GU Yu, QUAN Lianghu, CHEN Mengni, REN Fuji. Research on passive human activity recognition using WiFi ambient signals[J]. Journal of University of Science and Technology of China, 2015, 45(4): 308-313.

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基于WiFi背景噪音的被动式人体行为识别研究

Research on passive human activity recognition using WiFi ambient signals

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