FEATURE SELECTIONS FOR HUMAN ACTIVITY RECOGNITION IN SMART HOME ENVIRONMENTS

被引：0

作者：

Fang, Hongqing ^{[1
]}

Srinivasan, Raghavendiran ^{[2
]}

Cook, Diane J. ^{[2
]}

机构：

[1] Hohai Univ, Dept Automat Engn, Coll Energy & Elect Engn, Nanjing 211100, Jiangsu, Peoples R China

[2] Washington State Univ, Sch Elect Engn & Comp Sci, Pullman, WA 99163 USA

来源：

INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2012年 / 8卷 / 5B期

基金：

中国国家自然科学基金; 美国国家科学基金会;

关键词：

Activity recognition; Naive Bayes classifier; Hidden Markov model; Viterbi algorithm; Smart home;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, three probabilistic models are applied to represent and recognize human activities from observed sensor sequences: Naive Bayes classifier, forward procedure of a Hidden Markov Model and Viterbi algorithm based on a Hidden Markov Model. A variety of different feature selection methods is tested in order to reduce the dimensionality of the learning problem. The results show that the activity recognition performance measures of the three algorithms have a strong relationship with the dataset features that are utilized. Larger time feature values and smaller length size feature values will generate better results, relatively.

引用

页码：3525 / 3535

页数：11

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