FEATURE SELECTION BASED ON MUTUAL INFORMATION FOR HUMAN ACTIVITY RECOGNITION

被引：0

作者：

Fish, Benjamin ^{[1
]}

Khan, Ammar ^{[1
]}

Chehade, Nabil Hajj ^{[1
]}

Chien, Chieh ^{[1
]}

Pottie, Greg ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Ctr Embedded Networked Sensing, Los Angeles, CA 90024 USA

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2012年

基金：

美国国家科学基金会;

关键词：

Activity Classification; Feature Selection; Accelerometers;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

In this work, we consider a classification problem of 14 physical activities using a body sensor network (BSN) consisting of 14 tri-axial accelerometers. We use a tree-based classifier, and develop a feature selection algorithm based on mutual information to find the relevant features at every internal node of the tree. We evaluate our algorithm on 31 features per accelerometer (total of 434), and we present the results on 8 subjects with a 96% average accuracy.

引用

页码：1729 / 1732

页数：4

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