Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition
Recognizing human activities is one of the main goals of human-centered intelligent systems. Smartphone sensors produce a continuous sequence of observations. These observations are noisy, unstructured and high dimensional. Therefore, efficient features have to be extracted in order to perform accur...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English English English |
| Published: |
2018
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/69665/ http://irep.iium.edu.my/69665/ http://irep.iium.edu.my/69665/1/Fulpaper.pdf http://irep.iium.edu.my/69665/13/Acceptance%20Letter%2069665.pdf http://irep.iium.edu.my/69665/19/69665_Feature%20Fusion%20H-ELM%20based%20learned%20features%20and%20hand-crafted%20features_scopus.pdf |
| Summary: | Recognizing human activities is one of the main goals of human-centered intelligent systems. Smartphone sensors produce a continuous sequence of observations. These observations are noisy, unstructured and high dimensional. Therefore, efficient features have to be extracted in order to perform accurate classification. This paper proposes a combination of Hierarchical and kernel Extreme Learning Machine (HK-ELM) methods to learn features and map them to specific classes in a short time. Moreover, a feature fusion approach is proposed to combine H-ELM based learned features with hand-crafted ones. Our proposed method was found to outperform state-of-the-art in terms of accuracy and training time. It gives accuracy of 97.62 % and takes 3.4 seconds as a training time by using a normal Central Processing Unit (CPU). |
|---|