Human Activity Recognition on Smartphone: A Classification Analysis

The study hinged on the human activity recognition on smartphones by using the random forests model and Ada Boost model to make the classification. The study compared the classification results of two models and found the AdaBoost model had the better classification results. The study also found the...

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Main Authors: Wu, Shianghau (Author), Song, Yanan (Author)
Format: EJournal Article
Published: Institute of Advanced Engineering and Science, 2014-09-01.
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042 |a dc 
100 1 0 |a Wu, Shianghau  |e author 
700 1 0 |a Song, Yanan  |e author 
245 0 0 |a Human Activity Recognition on Smartphone: A Classification Analysis 
260 |b Institute of Advanced Engineering and Science,   |c 2014-09-01. 
500 |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3846 
520 |a The study hinged on the human activity recognition on smartphones by using the random forests model and Ada Boost model to make the classification. The study compared the classification results of two models and found the AdaBoost model had the better classification results. The study also found the Ada Boost model had the advantage of less calculation time. 
540 |a Copyright (c) 2014 Institute of Advanced Engineering and Science 
540 |a http://creativecommons.org/licenses/by-nc-nd/4.0 
546 |a eng 
690 |a Data Mining; Text Mining 
690 |a Activity Recognition; Random Forests Model; AdaBoost Model; Classification; Machine Learning 
655 7 |a info:eu-repo/semantics/article  |2 local 
655 7 |a info:eu-repo/semantics/publishedVersion  |2 local 
655 7 |2 local 
786 0 |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 12, No 9: September 2014; 7041-7045 
786 0 |n 2502-4760 
786 0 |n 2502-4752 
786 0 |n 10.11591/ijeecs.v12.i9 
787 0 |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3846/2320 
856 4 1 |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3846/2320  |z Get fulltext