A comparative sensor based multi-classes neural network classifications for human activity recognition

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...

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Main Authors: Aminpour, Ramtin, Dadios, Elmer P.
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Published: Animo Repository 2018
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Online Access:https://animorepository.dlsu.edu.ph/faculty_research/2703
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Institution: De La Salle University
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spelling oai:animorepository.dlsu.edu.ph:faculty_research-37022021-10-27T08:39:38Z A comparative sensor based multi-classes neural network classifications for human activity recognition Aminpour, Ramtin Dadios, Elmer P. 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. 2018-09-01T07:00:00Z text https://animorepository.dlsu.edu.ph/faculty_research/2703 Faculty Research Work Animo Repository Human activity recognition Neural networks (Computer science) Computer Sciences Mechanical Engineering
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Human activity recognition
Neural networks (Computer science)
Computer Sciences
Mechanical Engineering
spellingShingle Human activity recognition
Neural networks (Computer science)
Computer Sciences
Mechanical Engineering
Aminpour, Ramtin
Dadios, Elmer P.
A comparative sensor based multi-classes neural network classifications for human activity recognition
description 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.
format text
author Aminpour, Ramtin
Dadios, Elmer P.
author_facet Aminpour, Ramtin
Dadios, Elmer P.
author_sort Aminpour, Ramtin
title A comparative sensor based multi-classes neural network classifications for human activity recognition
title_short A comparative sensor based multi-classes neural network classifications for human activity recognition
title_full A comparative sensor based multi-classes neural network classifications for human activity recognition
title_fullStr A comparative sensor based multi-classes neural network classifications for human activity recognition
title_full_unstemmed A comparative sensor based multi-classes neural network classifications for human activity recognition
title_sort comparative sensor based multi-classes neural network classifications for human activity recognition
publisher Animo Repository
publishDate 2018
url https://animorepository.dlsu.edu.ph/faculty_research/2703
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