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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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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
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,829 KB) | Citation | Downloads: 801 | Views: 1,524

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

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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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Cited-By CrossRef

[1] Brain tumor classification using modified local binary patterns (LBP) feature extraction methods, Kaplan, Kaplan, Kaya, Yılmaz, Kuncan, Melih, Ertunç, H. Metin, Medical Hypotheses, ISSN 0306-9877, Issue , 2020.
Digital Object Identifier: 10.1016/j.mehy.2020.109696

[2] A novel feature extraction method for bearing fault classification with one dimensional ternary patterns, Kuncan, Melih, Kaplan, Kaplan, Mi̇naz, Mehmet Recep, Kaya, Yılmaz, Ertunç, H. Metin, ISA Transactions, ISSN 0019-0578, Issue , 2020.
Digital Object Identifier: 10.1016/j.isatra.2019.11.006

[3] A New Approach for Human Recognition Through Wearable Sensor Signals, Kılıç, Şafak, Kaya, Yılmaz, Askerbeyli, İman, Arabian Journal for Science and Engineering, ISSN 2193-567X, Issue 4, Volume 46, 2021.
Digital Object Identifier: 10.1007/s13369-021-05391-3

[4] An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis, Kaplan, Kaplan, Kaya, Yılmaz, Kuncan, Melih, Mi̇naz, Mehmet Recep, Ertunç, H. Metin, Applied Soft Computing, ISSN 1568-4946, Issue , 2020.
Digital Object Identifier: 10.1016/j.asoc.2019.106019

[5] A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification, Kaya, Yılmaz, Kuncan, Melih, Kaplan, Kaplan, Minaz, Mehmet Recep, Ertunç, H.Metin, Journal of Experimental & Theoretical Artificial Intelligence, ISSN 0952-813X, Issue 1, Volume 33, 2021.
Digital Object Identifier: 10.1080/0952813X.2020.1735530

[6] An effective method for detection of stator fault in PMSM with 1D-LBP, Mi̇naz, Mehmet Recep, ISA Transactions, ISSN 0019-0578, Issue , 2020.
Digital Object Identifier: 10.1016/j.isatra.2020.07.013

[7] Yeraltı Metro Hatlarında Video Analiz Yöntemiyle Olay Algılama Kontrolünün Gerçekleştirilmesi, ÇEKEREK, Emre, KANDİLLİ, İsmet, KUNCAN, Melih, El-Cezeri Fen ve Mühendislik Dergisi, ISSN 2148-3736, 2020.
Digital Object Identifier: 10.31202/ecjse.727104

[8] Fault diagnosis of rolling bearing based on multiscale one-dimensional hybrid binary pattern, Cao, Susheng, Xu, Feiyu, Ma, Tianchi, Measurement, ISSN 0263-2241, 2021.
Digital Object Identifier: 10.1016/j.measurement.2021.109552

[9] An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA, Kuncan, Melih, IEEE Access, ISSN 2169-3536, Issue , 2020.
Digital Object Identifier: 10.1109/ACCESS.2020.3011980

[10] A new approach for physical human activity recognition based on co-occurrence matrices, Kuncan, Fatma, Kaya, Yılmaz, Tekin, Ramazan, Kuncan, Melih, The Journal of Supercomputing, ISSN 0920-8542, 2021.
Digital Object Identifier: 10.1007/s11227-021-03921-2

[11] Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters, Kaya, Yılmaz, Kuncan, Melih, Kaplan, Kaplan, Minaz, Mehmet Recep, Ertunç, H. Metin, Soft Computing, ISSN 1432-7643, Issue 16, Volume 24, 2020.
Digital Object Identifier: 10.1007/s00500-019-04656-2

[12] Turkish handwriting recognition system using multi-layer perceptron, Kuncan, Melih, Vardar, Enes, Kaplan, Kaplan, Ertunç, H. Metin, Journal of Mechatronics and Artificial Intelligence in Engineering, ISSN 2669-1116, Issue 2, Volume 1, 2020.
Digital Object Identifier: 10.21595/jmai.2020.21502

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