Feature Analysis to Human Activity Recognition

International journal of computers, communications and control

Stefan Oniga¹  Jozsef Suto²  Petrica Pop Sitar³ 

[1] 1. Department of Information Systems and NetworksUniversity of Debrecen, Debrecen, [e-mail]2. Department of Electronic and Computer EngineeringTechnical University of Cluj-Napoca,North University Center at Baia Mare, Baia Mare, Romania;Department of Information Systems and NetworksUniversity of Debrecen, Debrecen, Hungary;Department of Mathematics and InformaticsTechnical University of Cluj-Napoca,North University Center at Baia Mare, Baia Mare, Romania

关键词: human activity recognition;    feature extraction;    feature selection;    machine learning;   

DOI  :  10.15837/ijccc.2017.1.2787

学科分类：计算机科学（综合）

来源: Universitatea Agora

【 摘 要 】

Human activity recognition (HAR) is one of those research areas whose importance and popularity have notably increased in recent years. HAR can be seen as a general machine learning problem which requires feature extraction and feature selection. In previous articles different features were extracted from time, frequency and wavelet domains for HAR but it is not clear that, how to determine the best feature combination which maximizes the performance of a machine learning algorithm. The aim of this paper is to present the most relevant feature extraction methods in HAR and to compare them with widely-used filter and wrapper feature selection algorithms. This work is an extended version of [1]a where we tested the efficiency of filter and wrapper feature selection algorithms in combination with artificial neural networks. In this paper the efficiency of selected features has been investigated on more machine learning algorithms (feed-forward artificial neural network, k-nearest neighbor and decision tree) where an independent database was the data source. The result demonstrates that machine learning in combination with feature selection can overcome other classification approaches.

【 授权许可】

Free