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/69665/25/69665_Feature%20Fusion_%20H-ELM%20based%20Learned_article.pdf http://irep.iium.edu.my/69665/19/69665_Feature%20Fusion%20H-ELM%20based%20learned%20features%20and%20hand-crafted%20features_scopus.pdf http://irep.iium.edu.my/69665/26/69665_Feature%20Fusion_%20H-ELM%20based%20Learned_wos.pdf |
| _version_ | 1848787153765531648 |
|---|---|
| author | AlDahoul, Nouar Htike, Zaw Zaw |
| author_facet | AlDahoul, Nouar Htike, Zaw Zaw |
| author_sort | AlDahoul, Nouar |
| building | IIUM Repository |
| collection | Online Access |
| description | 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). |
| first_indexed | 2025-11-14T17:20:24Z |
| format | Article |
| id | iium-69665 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-14T17:20:24Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-696652020-04-10T00:51:38Z http://irep.iium.edu.my/69665/ Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition AlDahoul, Nouar Htike, Zaw Zaw Q350 Information theory 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). 2018-12 Article PeerReviewed application/pdf en http://irep.iium.edu.my/69665/25/69665_Feature%20Fusion_%20H-ELM%20based%20Learned_article.pdf application/pdf en http://irep.iium.edu.my/69665/19/69665_Feature%20Fusion%20H-ELM%20based%20learned%20features%20and%20hand-crafted%20features_scopus.pdf application/pdf en http://irep.iium.edu.my/69665/26/69665_Feature%20Fusion_%20H-ELM%20based%20Learned_wos.pdf AlDahoul, Nouar and Htike, Zaw Zaw (2018) Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition. International Journal of Advanced Computer Science and Applications. ISSN 2158-107X E-ISSN 2156-5570 (In Press) http://thesai.org/Publications/IJACSA |
| spellingShingle | Q350 Information theory AlDahoul, Nouar Htike, Zaw Zaw Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition |
| title | Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition |
| title_full | Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition |
| title_fullStr | Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition |
| title_full_unstemmed | Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition |
| title_short | Feature fusion H-ELM based learned features and hand-crafted features for human activity recognition |
| title_sort | feature fusion h-elm based learned features and hand-crafted features for human activity recognition |
| topic | Q350 Information theory |
| url | http://irep.iium.edu.my/69665/ http://irep.iium.edu.my/69665/ http://irep.iium.edu.my/69665/25/69665_Feature%20Fusion_%20H-ELM%20based%20Learned_article.pdf http://irep.iium.edu.my/69665/19/69665_Feature%20Fusion%20H-ELM%20based%20learned%20features%20and%20hand-crafted%20features_scopus.pdf http://irep.iium.edu.my/69665/26/69665_Feature%20Fusion_%20H-ELM%20based%20Learned_wos.pdf |