2-3 September 2021
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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Smartphone-based Recognition of Human Activities using Shallow Machine Learning
Digital Object Identifier (DOI) : 10.14569/IJACSA.2021.0120410
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 12 Issue 4, 2021.
Abstract: The human action recognition (HAR) attempts to classify the activities of individuals and the environment through a collection of observations. HAR research is focused on many applications, such as video surveillance, healthcare and human computer interactions. Many problems can deteriorate the performance of human recognition systems. Firstly, the development of a light-weight and reliable smartphone system to classify human activities and reduce labelling and labelling time; secondly, the features derived must generalise multiple variations to address the challenges of action detection, including individual appearances, viewpoints and histories. In addition, the relevant classification should be guaranteed by those features. In this paper, a model was proposed to reliably detect the type of physical activity conducted by the user using the phone's sensors. This includes review of the existing research solutions, how they can be strengthened, and a new approach to solve the problem. The Stochastic Gradient Descent (SGD) decreases the computational strain to accelerate trade iterations at a lower rate. SGD leads to J48 performance enhancement. Furthermore, a human activity recognition dataset based on smartphone sensors are used to validate the proposed solution. The findings showed that the proposed model was superior.
Keywords: Data preprocessing; data mining; classification; genetic programming; Naïve Bayes; decision tree
Maha Mohammed Alhumayyani, Mahmoud Mounir and Rasha Ismael, “Smartphone-based Recognition of Human Activities using Shallow Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 12(4), 2021. http://dx.doi.org/10.14569/IJACSA.2021.0120410
@article{Alhumayyani2021,
title = {Smartphone-based Recognition of Human Activities using Shallow Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120410},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120410},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {4},
author = {Maha Mohammed Alhumayyani and Mahmoud Mounir and Rasha Ismael}
}
