A Novel Approach for Activity Recognition with Down-Sampling 1D Local Binary Pattern Features
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Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Univ Suceava, Fac Electrical Eng
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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-ID-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-ID-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.
Açıklama
Anahtar Kelimeler
digital signal processing, feature extraction, machine learning, pattern recognition, wearable sensors
Kaynak
Advances in Electrical and Computer Engineering
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
Cilt
19
Sayı
1
