ORCID

0000-0001-7702-2564 (El Moudden)

Document Type

Article

Publication Date

2016

DOI

10.12988/ces.2016.67119

Publication Title

Contemporary Engineering Sciences

Volume

9

Issue

21

Pages

1031-1041

Abstract

Human activity recognition (HAR) is an emerging research topic in pattern recognition, especially in computer vision. The main objective of human activity recognition is to automatically detect and analyze human activities from the information acquired from different sensors. Human activity prediction using big data remains a challengingly open problem. Several approaches have recently been developed in order to find practical ways to solve high dimensionality of data problems. The aim of this study is to attempt, using data mining techniques, to deal with HAR modeling involving a significant number of variables in order to identify relevant parameters from data and thus to maximize the classification accuracy while minimizing the number of features. The proposed framework has been tested on a publicly HAR available dataset and the results have been interpreted and discussed.

Rights

Copyright © 2016 Ismail El Moudden, Mounir Ouzir, Badreddine Benyacoub and Souad El Bernoussi.

This article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Original Publication Citation

El Moudden, I., Ouzir, M., Benyacoub, B., & El Bernoussi, S. E. (2016). Mining human activity using dimensionality reduction and pattern recognition. Contemporary Engineering Sciences, 9(21), 1031-1041. https://doi.org/10.12988/ces.2016.67119

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