Feature Fusion: H-ELM based Learned Features and Hand-Crafted Features for Human Activity Recognition
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2019, Vol 10, Issue 7
Abstract
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 an 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 an accuracy of 97.62% and takes 3.4 seconds as a training time by using a normal Central Processing Unit (CPU).
Authors and Affiliations
Nouar AlDahoul, Rini Akmeliawati, Zaw Zaw Htike
Keywords
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- EP ID EP611479
- DOI 10.14569/IJACSA.2019.0100770
- Views 115
- Downloads 0
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