Article detail · 2019
A Novel Approach for Activity Recognition with Down-Sampling 1D Local Binary Pattern Features
- Year
- 2019
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue Advances in Electrical and Computer Engineering
- Catalog match (ISSN) Advances in Electrical and Computer Engineering
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
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.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
34 citations
OpenAlex cited_by_count (cache / database)
33 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Brain tumor classification using modified local binary patterns (LBP) feature extraction methods 2020
- Brain tumor classification using modified local binary patterns (LBP) feature extraction methods 2020
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- A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification 2021
- A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification 2021
- A novel feature extraction method for bearing fault classification with one dimensional ternary patterns 2020