Feature Analysis to Human Activity Recognition

  • Jozsef Suto Department of Information Systems and Networks University of Debrecen, Debrecen, Hungary
  • Stefan Oniga 1. Department of Information Systems and Networks University of Debrecen, Debrecen, Hungary oniga.istvan@inf.unideb.hu 2. Department of Electronic and Computer Engineering Technical University of Cluj-Napoca, North University Center at Baia Mare, Baia Mare, Romania
  • Petrica Pop Sitar Department of Mathematics and Informatics Technical University of Cluj-Napoca, North University Center at Baia Mare, Baia Mare, Romania

Abstract

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.

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Published
2016-12-02
How to Cite
SUTO, Jozsef; ONIGA, Stefan; POP SITAR, Petrica. Feature Analysis to Human Activity Recognition. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 12, n. 1, p. 116-130, dec. 2016. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2787>. Date accessed: 25 nov. 2020. doi: https://doi.org/10.15837/ijccc.2017.1.2787.

Keywords

human activity recognition, feature extraction, feature selection, machine learning