A comparative sensor based multi-classes neural network classifications for human activity recognition
College
Gokongwei College of Engineering
Department/Unit
Manufacturing Engineering and Management
Document Type
Article
Source Title
Journal of Advanced Computational Intelligence and Intelligent Informatics
Volume
22
Issue
5
First Page
711
Last Page
717
Publication Date
9-1-2018
Abstract
Human activity recognition with the smartphone could be important for many applications, especially since most of the people use this device in their daily life. A smartphone is a portable gadget with internal sensors and enough hardware power to accommodate this problem. In this paper, three neural network algorithms were compared to detect six major activities. The data are collected by a smartphone in real life and simulated on the remote server. The results show that MLP and GMDH neural network have better accuracy and performance compared with the LVQ neural network algorithm. © 2018 Fuji Technology Press.All Rights Reserved.
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Digitial Object Identifier (DOI)
10.20965/jaciii.2018.p0711
Recommended Citation
Aminpour, R., & Dadios, E. P. (2018). A comparative sensor based multi-classes neural network classifications for human activity recognition. Journal of Advanced Computational Intelligence and Intelligent Informatics, 22 (5), 711-717. https://doi.org/10.20965/jaciii.2018.p0711
Disciplines
Computer Sciences | Mechanical Engineering
Keywords
Human activity recognition; Neural networks (Computer science)
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