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 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).

@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},
  volume    = {10},
  number    = {7},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Nouar AlDahoul and Rini Akmeliawati and Zaw Zaw Htike},
  doi       = {10.14569/IJACSA.2019.0100770},
  url       = {https://doi.org/10.14569/IJACSA.2019.0100770}
}