期刊
PATTERN RECOGNITION LETTERS
卷 29, 期 16, 页码 2213-2220出版社
ELSEVIER
DOI: 10.1016/j.patrec.2008.08.002
关键词
Acceleration data; Activity recognition; Feature subset selection; Feature extraction; Neural network; Triaxial accelerometer
This paper presents a systematic design approach for constructing neural classifiers that are capable of classifying human activities using a triaxial accelerometer. The philosophy Of Our design approach is to apply a divide-and-conquer strategy that separates dynamic activities from static activities preliminarily and recognizes these two different types of activities separately. Since multilayer neural networks can generate complex discriminating surfaces for recognition problems, we adopt neural networks as the classifiers for activity recognition. An effective feature subset selection approach has been developed to determine significant feature subsets and compact classifier structures with satisfactory accuracy. Experimental results have successfully validated the effectiveness of the proposed recognition scheme. (C) 2008 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据