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Mobile Information Systems
Volume 2016 (2016), Article ID 1784101, 10 pages
http://dx.doi.org/10.1155/2016/1784101
Research Article

An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks

1Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147 Centro, 98000 Zacatecas, ZAC, Mexico
2Instituto Tecnológico Superior Zacatecas Sur, Av. Tecnológico 100, Las Moritas, 99700 Tlaltenango, ZAC, Mexico
3Unidad Académica de Medicina Humana y Ciencias de la Salud, Universidad Autónoma de Zacatecas, Jardín Juarez 147 Centro, 98000 Zacatecas, ZAC, Mexico

Received 1 June 2016; Revised 31 August 2016; Accepted 29 September 2016

Academic Editor: Daniele Riboni

Copyright © 2016 Carlos E. Galván-Tejada et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to Cite this Article

Carlos E. Galván-Tejada, Jorge I. Galván-Tejada, José M. Celaya-Padilla, et al., “An Analysis of Audio Features to Develop a Human Activity Recognition Model Using Genetic Algorithms, Random Forests, and Neural Networks,” Mobile Information Systems, vol. 2016, Article ID 1784101, 10 pages, 2016. doi:10.1155/2016/1784101