Abdu Gumaei1, 2, *, Mabrook Al-Rakhami1, 2, Hussain AlSalman2, Sk. Md. Mizanur Rahman3, Atif Alamri1, 2
CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1033-1057, 2020, DOI:10.32604/cmc.2020.011740
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… More >
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