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Print ISSN: 1582-7445
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 HIGH-IMPACT PAPER 

A Novel Approach for Activity Recognition with Down-Sampling 1D Local Binary Pattern Features

KUNCAN, F. See more information about KUNCAN, F. on SCOPUS See more information about KUNCAN, F. on IEEExplore See more information about KUNCAN, F. on Web of Science, KAYA, Y. See more information about  KAYA, Y. on SCOPUS See more information about  KAYA, Y. on SCOPUS See more information about KAYA, Y. on Web of Science, KUNCAN, M. See more information about KUNCAN, M. on SCOPUS See more information about KUNCAN, M. on SCOPUS See more information about KUNCAN, M. on Web of Science
 
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Download PDF pdficon (1,829 KB) | Citation | Downloads: 1,196 | Views: 1,914

Author keywords
digital signal processing, feature extraction, machine learning, pattern recognition, wearable sensors

References keywords
activity(24), recognition(20), human(20), learning(12), applications(11), sensors(10), classification(10), wearable(9), systems(9), machine(9)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-02-28
Volume 19, Issue 1, Year 2019, On page(s): 35 - 44
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.01005
Web of Science Accession Number: 000459986900005
SCOPUS ID: 85064195416

Abstract
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The sensors on the mobile devices directly reflect the physical and demographic characteristics of the user. Sensor signals may contain information about the gender and movement of the person. Automatic recognition of physical activities often referred to as human activity recognition (HAR). In this study, a novel feature extraction approach for the HAR system using the mobile sensor signals, the Down Sampling One Dimensional Local Binary Pattern (DS-1D-LBP) method is proposed. Feature extraction from signals is one of the most critical stages of HAR because the success of the HAR system depends on the features extraction. The proposed HAR system consists of two stages. In the first stage, DS-1D-LBP conversion was applied to the sensor signals in order to extract statistical features from the newly formed signals. In the last stage, classification with Extreme Learning Machine (ELM) was performed using these features. The highest success rate was 96.87 percent in the experimental results according to the different parameters of DS-1D-LBP and ELM. As a result of this study, the novel approach demonstrated that the proposed model performed with a high success rate using mobile sensor signals for the HAR system.


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References Weight

Web of Science® Citations for all references: 12,342 TCR
SCOPUS® Citations for all references: 19,577 TCR

Web of Science® Average Citations per reference: 263 ACR
SCOPUS® Average Citations per reference: 417 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-05-18 06:53 in 240 seconds.




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