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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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 |
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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 |
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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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text |
author |
Aminpour, Ramtin Dadios, Elmer P. |
author_facet |
Aminpour, Ramtin Dadios, Elmer P. |
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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 |
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Animo Repository |
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2018 |
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https://animorepository.dlsu.edu.ph/faculty_research/2703 |
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