@Article{cmc.2020.011740,
AUTHOR = {Abdu Gumaei, Mabrook Al-Rakhami, Hussain AlSalman, Sk. Md. Mizanur Rahman, Atif Alamri},
TITLE = {DL-HAR: Deep Learning-Based Human Activity Recognition  Framework for Edge Computing},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1033--1057},
URL = {http://www.techscience.com/cmc/v65n2/39861},
ISSN = {1546-2226},
ABSTRACT = {Human activity recognition is commonly used in several Internet of Things 
applications to recognize different contexts and respond to them. Deep learning has 
gained momentum for identifying activities through sensors, smartphones or even 
surveillance cameras. However, it is often difficult to train deep learning models on 
constrained IoT devices. The focus of this paper is to propose an alternative model by 
constructing a Deep Learning-based Human Activity Recognition framework for edge 
computing, which we call DL-HAR. The goal of this framework is to exploit the 
capabilities of cloud computing to train a deep learning model and deploy it on lesspowerful edge devices for recognition. The idea is to conduct the training of the model in 
the Cloud and distribute it to the edge nodes. We demonstrate how the DL-HAR can 
perform human activity recognition at the edge while improving efficiency and accuracy. 
In order to evaluate the proposed framework, we conducted a comprehensive set of 
experiments to validate the applicability of DL-HAR. Experimental results on the 
benchmark dataset show a significant increase in performance compared with the state-of-the-art models.},
DOI = {10.32604/cmc.2020.011740}
}