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).

Nouar AlDahoul, Rini Akmeliawati and Zaw Zaw Htike, “Feature Fusion: H-ELM based Learned Features and Hand-Crafted Features for Human Activity Recognition” International Journal of Advanced Computer Science and Applications(IJACSA), 10(7), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100770

@article{AlDahoul2019,
title = {Feature Fusion: H-ELM based Learned Features and Hand-Crafted Features for Human Activity Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100770},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100770},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {7},
author = {Nouar AlDahoul and Rini Akmeliawati and Zaw Zaw Htike}
}